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Wang L, Xu HX, Wang R, Zhang F, Deng D, Zhu X, Tan Q, Yang H. Advances in multi-omics studies of microvascular invasion in hepatocellular carcinoma. Eur J Med Res 2025; 30:165. [PMID: 40075448 PMCID: PMC11905518 DOI: 10.1186/s40001-025-02421-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Accepted: 03/01/2025] [Indexed: 03/14/2025] Open
Abstract
Microvascular invasion (MVI) represents a pivotal independent prognostic factor for the recurrence of hepatocellular carcinoma (HCC) after surgery. It contributes to early intervention for potentially recurrent HCC to enhance patient outcomes and increase survival rates. Traditionally, the diagnosis of MVI has relied on postoperative pathological analysis, and accurate preoperative detection methodologies are lacking. Recent research suggests that multi-omics strategies play a role in definitively diagnosing MVI before surgery and offering personalized selection for clinical decision-making in HCC management. This review meticulously examines a multi-omics approach for the preoperative prediction of MVI in HCC patients, aiming to innovate diagnostic paradigms to anticipate postsurgical recurrence, thereby facilitating earlier and more personalized therapeutic strategies.
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Affiliation(s)
- Lili Wang
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.
| | - Han Xin Xu
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Rui Wang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Fachang Zhang
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Diandian Deng
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Xiaoyang Zhu
- Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
| | - Qi Tan
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Heng Yang
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
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Ahmadzadeh AM, Lomer NB, Torigian DA. Radiomics and machine learning models for diagnosing microvascular invasion in cholangiocarcinoma: a systematic review and meta-analysis of diagnostic test accuracy studies. Clin Imaging 2025; 121:110456. [PMID: 40088548 DOI: 10.1016/j.clinimag.2025.110456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2024] [Revised: 01/30/2025] [Accepted: 03/12/2025] [Indexed: 03/17/2025]
Abstract
PURPOSE We aimed to systematically assess the value of radiomics/machine learning (ML) models for diagnosing microvascular invasion (MVI) in patients with cholangiocarcinoma (CCA) using various radiologic modalities. METHODS A systematic search of was conducted on Web of Sciences, PubMed, Scopus, and Embase. All the studies that assessed the value of radiomics models or ML models along with the use of imaging features were included. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria and METhodological RadiomICs Score (METRICS) were used for quality assessment. Pooled estimates for the diagnostic performance of radiomics/ML models were calculated. I-squared was used to assess heterogeneity, and sensitivity and subgroup analyses were performed to find the sources of heterogeneity. Deeks' funnel plots were used to assess publication bias. RESULTS 11 studies were included in the systematic review with only one study being about extrahepatic CCA. According to the METRICS, the mean score was 62.99 %. Meta-analyses were performed on intrahepatic CCA studies. The meta-analysis of the best ML models revealed an AUC of 0.93 in the training cohort and an AUC of 0.85 in the validation cohort. Regarding the best radiomics model, the AUC was 0.85 in the training cohort and 0.81 in the validation cohort. CONCLUSION Radiomics/ML models showed very good diagnostic performance regarding MVI diagnosis in patients with intrahepatic CCA and may provide a non-invasive method for this purpose. However, given the high heterogeneity and low number of the included studies, further multi-center studies with prospective design and robust external validation are essential.
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Affiliation(s)
- Amir Mahmoud Ahmadzadeh
- Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nima Broomand Lomer
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, PA 19104, United States
| | - Drew A Torigian
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States of America.
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Zhou M, Chen M, Luo M, Chen M, Huang H. Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging. Eur Radiol 2025; 35:979-988. [PMID: 39143248 DOI: 10.1007/s00330-024-11025-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 06/13/2024] [Accepted: 08/02/2024] [Indexed: 08/16/2024]
Abstract
OBJECTIVES To explore diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for assessing pathological prognostic factors in patients with rectal cancer. MATERIALS AND METHODS A total of 162 patients (105 males; mean age of 61.8 ± 13.1 years old) scheduled to undergo radical surgery were enrolled in this prospective study. The pathological prognostic factors included histological differentiation, lymph node metastasis (LNM), and extramural vascular invasion (EMVI). The DWI, IVIM, and DKI parameters were obtained and correlated with prognostic factors using univariable and multivariable logistic regression. Their assessment value was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS Multivariable logistic regression analyses showed that higher mean kurtosis (MK) (odds ratio (OR) = 194.931, p < 0.001) and lower apparent diffusion coefficient (ADC) (OR = 0.077, p = 0.025) were independently associated with poorer differentiation tumors. Higher perfusion fraction (f) (OR = 575.707, p = 0.023) and higher MK (OR = 173.559, p < 0.001) were independently associated with LNMs. Higher f (OR = 1036.116, p = 0.024), higher MK (OR = 253.629, p < 0.001), lower mean diffusivity (MD) (OR = 0.125, p = 0.038), and lower ADC (OR = 0.094, p = 0.022) were independently associated with EMVI. The area under the ROC curve (AUC) of MK for histological differentiation was significantly higher than ADC (0.771 vs. 0.638, p = 0.035). The AUC of MK for LNM positivity was higher than f (0.770 vs. 0.656, p = 0.048). The AUC of MK combined with MD (0.790) was the highest among f (0.663), MK (0.779), MD (0.617), and ADC (0.610) in assessing EMVI. CONCLUSION The DKI parameters may be used as imaging biomarkers to assess pathological prognostic factors of rectal cancer before surgery. CLINICAL RELEVANCE STATEMENT Diffusion kurtosis imaging (DKI) parameters, particularly mean kurtosis (MK), are promising biomarkers for assessing histological differentiation, lymph node metastasis, and extramural vascular invasion of rectal cancer. These findings suggest DKI's potential in the preoperative assessment of rectal cancer. KEY POINTS Mean kurtosis outperformed the apparent diffusion coefficient in assessing histological differentiation in resectable rectal cancer. Perfusion fraction and mean kurtosis are independent indicators for assessing lymph node metastasis in rectal cancer. Mean kurtosis and mean diffusivity demonstrated superior accuracy in assessing extramural vascular invasion.
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Affiliation(s)
- Mi Zhou
- Department of Radiology, Sichuan Provincial Orthopaedics Hospital, 610041, Chengdu, China
| | - Mengyuan Chen
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Mingfang Luo
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Meining Chen
- Department of MR Scientific Marketing, Siemens Healthineers, 200135, Shanghai, China
| | - Hongyun Huang
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 610072, Chengdu, China.
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Nong HY, Cen YY, Lu SJ, Huang RS, Chen Q, Huang LF, Huang JN, Wei X, Liu MR, Li L, Ding K. Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma: A retrospective study. World J Gastrointest Oncol 2025; 17:96439. [PMID: 39817122 PMCID: PMC11664629 DOI: 10.4251/wjgo.v17.i1.96439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 09/06/2024] [Accepted: 11/07/2024] [Indexed: 12/12/2024] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma (HCC) surgery. Currently, there is a paucity of preoperative evaluation approaches for MVI. AIM To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC. METHODS Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital. Patients were classified into two groups: MVI-positive (n = 57) and MVI-negative (n = 40), based on postoperative pathological results. The correlation between relevant radiological signs and MVI status was analyzed. MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features, which were combined with radiological signs to construct artificial neural network (ANN) models for MVI prediction. The predictive performance of the ANN models was evaluated using area under the curve, sensitivity, and specificity. ANN models with relatively high predictive performance were screened using the DeLong test, and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models' stability. RESULTS The absence of a pseudocapsule, an incomplete pseudocapsule, and the presence of tumor blood vessels were identified as independent predictors of HCC MVI. The ANN model constructed using the dominant features of the combined group (pseudocapsule status + tumor blood vessels + arterial phase + venous phase) demonstrated the best predictive performance for MVI status and was found to be automated, highly operable, and very stable. CONCLUSION The ANN model constructed using the dominant features of the combined group can be recommended as a non-invasive method for preoperative prediction of HCC MVI status.
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Affiliation(s)
- Hai-Yang Nong
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
- Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
- Guangxi Clinical Medical Research Center for Hepatobiliary Diseases, Affiliated Hospital of Youiiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Yong-Yi Cen
- Guangxi Clinical Medical Research Center for Hepatobiliary Diseases, Affiliated Hospital of Youiiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
- Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
| | - Shan-Jin Lu
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Rui-Sui Huang
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Qiong Chen
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Li-Feng Huang
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Jian-Ning Huang
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Xue Wei
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Man-Rong Liu
- Department of Ultrasound, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Lin Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
| | - Ke Ding
- Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
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Wei YC, Yun L, Liang YL, Grimm R, Yang C, Tao YF, Jiang SC, Liao JY. Nomogram based on the neutrophil-to-lymphocyte ratio and MR diffusion quantitative parameters for predicting Ki67 expression in hepatocellular carcinoma from a prospective study. Sci Rep 2024; 14:31738. [PMID: 39738357 PMCID: PMC11685758 DOI: 10.1038/s41598-024-82333-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2024] [Accepted: 12/04/2024] [Indexed: 01/02/2025] Open
Abstract
This study aimed to establish and validate a multiparameter prediction model for Ki67 expression in hepatocellular carcinoma (HCC) patients while also exploring its potential to predict the one-year recurrence risk. The clinical, pathological, and imaging data of 83 patients with HCC confirmed by postoperative pathology were analyzed, and the patients were randomly divided into a training set (n = 58) and a validation set (n = 25) at a ratio of 7:3. All patients underwent a magnetic resonance imaging (MRI) scan that included multi-b value diffusion-weighted scanning before surgery, and quantitative parameters were obtained via intravoxel incoherent motion (IVIM) and diffusion kurtosis (DKI) models. Univariate and multivariate logistic regression analyses were conducted using the training set data to construct a model, which was internally validated. The area under the curve (AUC) of the receiver operating characteristics (ROC), a decision curve analysis (DCA), and a calibration analysis were used to evaluate the model's performance. Additionally, for patients with available follow-up data, the combined model was evaluated for its potential utility in predicting the one-year recurrence risk by analyzing the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.The combined model outperformed the clinicaland parametric models in predicting high Ki67 expression. The nomograms based on the combined model included the neutrophil-to-lymphocyte ratio (NLR), ADCslow_Aver. The model showed strong discrimination in the training set, with an AUC of 0.836 (95% CI: 0.729-0.942) and acceptable calibration (Hosmer-Lemeshow p = 0.109). In the validation set, the model maintained moderate discrimination (AUC 0.806, 95% CI: 0.621-0.990) with good calibration (p = 0.663). DCA revealed that the combined model provided good clinical value and correction effects. Additionally, when used to predict the one-year recurrence risk, the combined model achieved moderate accuracy (AUC = 0.747), highlighting its potential utility in identifying patients at a higher risk of recurrence. A nomogram incorporating the NLR and quantitative MR diffusion parameters effectively predicts Ki67 expression in HCC patients before surgery. The model also shows promise in predicting recurrence risk, which may aid in postoperative risk stratification and patient management. Clinical Relevance Statement We established a model that incorporated the NLR and quantitative magnetic resonance diffusion parameters, which demonstrated robust performance in predicting both high Ki67 expression and the one-year recurrence risk in HCC patients. This model shows potential clinical value in guiding postoperative risk stratification and personalized treatment planning.
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Affiliation(s)
- Yu-Chen Wei
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Liang Yun
- Department of Radiology, Guilin Municipal Hospital of Traditional Chinese Medicine, Guilin, China
| | - Yan-Ling Liang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | | | - Chongze Yang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuan-Fang Tao
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Sheng-Chen Jiang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jin-Yuan Liao
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Ministry of Education, Guangxi Zhuang Autonomous Region, Nanning, 530021, People's Republic of China.
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Chen Z, Zhu Y, Wang L, Cong R, Feng B, Cai W, Liang M, Li D, Wang S, Hu M, Mi Y, Wang S, Ma X, Zhao X. Virtual MR Elastography and Multi-b-value DWI Models for Predicting Microvascular Invasion in Solitary BCLC Stage A Hepatocellular Carcinoma. Acad Radiol 2024:S1076-6332(24)00871-7. [PMID: 39643466 DOI: 10.1016/j.acra.2024.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 11/07/2024] [Accepted: 11/11/2024] [Indexed: 12/09/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the performance of virtual MR elastography (vMRE) for predicting microvascular invasion (MVI) in Barcelona Clinic Liver Cancer (BCLC) stage A (≤ 5.0 cm) hepatocellular carcinoma (HCC) and to construct a combined nomogram based on vMRE, multi-b-value DWI models, and clinical-radiological (CR) features. METHODS Consecutive patients with suspected HCC who underwent multi-b-value DWI examinations were prospectively collected. Quantitative parameters from vMRE, mono-exponential, intravoxel incoherent motion, and diffusion kurtosis imaging models were obtained. Multivariate logistic regression was used to identify independent MVI predictors and build prediction models. A combined MRI_Score was constructed using independent quantitative parameters. A visualized nomogram was built based on significant CR features and MRI_Score. The predictive performance of quantitative parameters and models was evaluated. RESULTS The study included 103 patients (median age: 56 years; range: 35-70 years; 87 males and 16 females). Diffusion-based shear modulus (μDiff) exhibited a predictive performance for MVI with area under the curve (AUC) of 0.735. The MRI_Score was developed employing true diffusion coefficient (D), mean kurtosis (MK), and μDiff. CR model and MRI_Score achieved AUCs of 0.787 and 0.840, respectively. The combined nomogram based on AFP, corona enhancement, tumor capsule, TTPVI, and MRI_Score significantly improved the predictive performance to an AUC of 0.931 (Delong test p < 0.05). CONCLUSION vMRE exhibited great potential for predicting MVI in BCLC stage A HCC. The combined nomogram integrating CR features, vMRE, and quantitative diffusion parameters significantly improved the predictive accuracy and could potentially assist clinicians in identifying appropriate treatment options.
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Affiliation(s)
- Zhaowei Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Yongjian Zhu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Rong Cong
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Wei Cai
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Shuang Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Mancang Hu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Yongtao Mi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing 100176, China (S.W.).
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
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Liu G, Shen Z, Chong H, Zhou J, Zhang T, Wang Y, Ma D, Yang Y, Chen Y, Wang H, Sack I, Guo J, Li R, Yan F. Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma. J Magn Reson Imaging 2024; 60:2626-2640. [PMID: 38344910 DOI: 10.1002/jmri.29276] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 01/21/2024] [Accepted: 01/22/2024] [Indexed: 11/15/2024] Open
Abstract
BACKGROUND Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies. PURPOSE To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE). STUDY TYPE Prospective. POPULATION 188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up. FIELD STRENGTH/SEQUENCE 1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences. ASSESSMENT Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors. STATISTICAL TESTS Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant. RESULTS Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance. DATA CONCLUSION Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Guixue Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhehan Shen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huanhuan Chong
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiahao Zhou
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianyi Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yikun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Ma
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuchen Yang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yongjun Chen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huafeng Wang
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ingolf Sack
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jing Guo
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Ma Y, Yue P, Zhang J, Yuan J, Liu Z, Chen Z, Zhang H, Zhang C, Zhang Y, Dong C, Lin Y, Liu Y, Li S, Meng W. Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform. Ann Med 2024; 56:2357354. [PMID: 38813815 PMCID: PMC11141304 DOI: 10.1080/07853890.2024.2357354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 04/26/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Early diagnosis of acute gallstone pancreatitis severity (GSP) is challenging in clinical practice. We aimed to investigate the efficacy of CT features and radiomics for the early prediction of acute GSP severity. METHODS We retrospectively recruited GSP patients who underwent CT imaging within 48 h of admission from tertiary referral centre. Radiomics and CT features were extracted from CT scans. The clinical and CT features were selected by the random forest algorithm to develop the ML GSP model for the identification of severity of GSP (mild or severe), and its predictive efficacy was compared with radiomics model. The predictive performance was assessed by the area under operating characteristic curve. Calibration curve and decision curve analysis were performed to demonstrate the classification performance and clinical efficacy. Furthermore, we built a web-based open access GSP severity calculator. The study was registered with ClinicalTrials.gov (NCT05498961). RESULTS A total of 301 patients were enrolled. They were randomly assigned into the training (n = 210) and validation (n = 91) cohorts at a ratio of 7:3. The random forest algorithm identified the level of calcium ions, WBC count, urea level, combined cholecystitis, gallbladder wall thickening, gallstones, and hydrothorax as the seven predictive factors for severity of GSP. In the validation cohort, the areas under the curve for the radiomics model and ML GSP model were 0.841 (0.757-0.926) and 0.914 (0.851-0.978), respectively. The calibration plot shows that the ML GSP model has good consistency between the prediction probability and the observation probability. Decision curve analysis showed that the ML GSP model had high clinical utility. CONCLUSIONS We built the ML GSP model based on clinical and CT image features and distributed it as a free web-based calculator. Our results indicated that the ML GSP model is useful for predicting the severity of GSP.
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Affiliation(s)
- Yuhu Ma
- Department of Anesthesiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Ping Yue
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Jinduo Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Jinqiu Yuan
- Clinical Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Zhaoqing Liu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zixian Chen
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Hengwei Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Chao Zhang
- Department of Orthopedics, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Yong Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Chunlu Dong
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Yanyan Lin
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Yatao Liu
- Department of Anesthesiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Shuyan Li
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Wenbo Meng
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
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Wang Q, Zhou Y, Yang H, Zhang J, Zeng X, Tan Y. MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma. Med Phys 2024; 51:4673-4686. [PMID: 38642400 DOI: 10.1002/mp.17087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/12/2024] [Accepted: 04/02/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. PURPOSE Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHOD The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC). RESULT Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance. CONCLUSION This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.
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Affiliation(s)
- Qinghua Wang
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| | - Yongjie Zhou
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Hongan Yang
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| | - Jingrun Zhang
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| | - Xianjun Zeng
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
| | - Yongming Tan
- Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
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Zhang Y, Sheng R, Yang C, Dai Y, Zeng M. Detecting microvascular invasion in hepatocellular carcinoma using the impeded diffusion fraction technique to sense macromolecular coordinated water. Abdom Radiol (NY) 2024; 49:1892-1904. [PMID: 38526597 DOI: 10.1007/s00261-024-04230-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 03/26/2024]
Abstract
OBJECTIVES Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size. RESULTS The results suggested a significantly higher fraction of impeded diffusion (FID) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, FID, tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by FID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164). CONCLUSION IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech Univerisity, Shanghai, 200032, China.
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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Yang K, Wu K, Lei Z, Liu T, Zhang X, Li J, Yang K, Peng Q, Lin T, Liu C, Qu K. Tumor budding is a meaningful prognostic marker in patients with hepatocellular carcinoma after curative hepatectomy. PORTAL HYPERTENSION & CIRRHOSIS 2024; 3:86-95. [DOI: 10.1002/poh2.85] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/19/2024] [Indexed: 01/05/2025]
Abstract
AbstractAimTumor budding (TB) has excellent prognostic value in many solid tumors, but there is little research on it in hepatocellular carcinoma (HCC). This study assessed the prognostic value of TB in patients with HCC who received hepatectomy.MethodsThis retrospective study included 210 patients with HCC who received curative hepatectomy at the First Affiliated Hospital of Xi'an Jiaotong University, between 2016 and 2018. TB was evaluated on hematoxylin‐ and eosin‐stained slides according to the criteria established by the 2016 International Tumor Budding Consensus Conference. t‐tests, Chi‐squared tests, and rank‐sum tests were used to correlate the extent of TB with clinicopathological parameters. Prognostic analysis was performed using Cox regression models and the Kaplan–Meier method.ResultsThe positive detection rate of TB was 45.2% (95/210) in 210 patients with HCC. Patients positive for TB always exhibit lower tumor differentiation, higher hepatitis B virus DNA levels, and more severe liver fibrosis. Multivariate Cox analysis identified TB (hazard ratio [HR]: 2.232, 95% confidence interval [CI]: 1.479–3.368, p < 0.001) as an independent prognostic factor for patients' recurrence‐free survival (RFS), similar to tumor size (HR: 1.070, 95% CI: 1.070–1.142, p = 0.042) and satellite nodule (HR: 2.266, 95% CI: 1.298–3.956, p = 0.004). Kaplan–Meier analysis also demonstrated that TB‐positive patients had a significantly worse RFS. Interestingly, subgroup analysis revealed that among HCC patients with negative microvascular invasion (MVI), TB was also strongly associated with RFS (HR: 3.206, 95% CI: 1.667–6.168, p < 0.001). These findings suggest that TB may serve as a supplemental prognostic biomarker for HCC‐negative MVI.ConclusionsTB is an adverse prognostic biomarker for HCC, particularly in patients negative for MVI. TB evaluation should be considered in the postoperative pathological examination of HCC in clinical practice.
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Affiliation(s)
- Kaibo Yang
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
| | - Kunjin Wu
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
| | - Zitong Lei
- Department of Critical Care Nephrology and Blood Purification The First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
| | - Tong Liu
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
| | - Xing Zhang
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
- Hangzhou Institute of National Extremely‐weak Magnetic Field Infrastructure Hangzhou Zhejiang China
| | - Jing Li
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
| | - Kun Yang
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
| | - Qiuting Peng
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
| | - Ting Lin
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
- Department of Surgical Intensive Care Units The First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
| | - Chang Liu
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
| | - Kai Qu
- Department of Hepatobiliary Surgery and Liver Transplantation The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education Xi'an Shaanxi China
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Zhang Y, Chen J, Yang C, Dai Y, Zeng M. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging-based habitat imaging. Eur Radiol 2024; 34:3215-3225. [PMID: 37853175 DOI: 10.1007/s00330-023-10339-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 07/27/2023] [Accepted: 08/20/2023] [Indexed: 10/20/2023]
Abstract
OBJECTIVES Habitat imaging allows for the quantification and visualization of various subregions within the tumor. We aim to develop an approach using diffusion-weighted imaging (DWI)-based habitat imaging for preoperatively predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHODS Sixty-five patients were prospectively included and underwent multi-b DWI examinations. Based on the true diffusion coefficient (Dt), perfusion fraction (f), and mean kurtosis coefficient (MK), which respectively characterize cellular density, perfusion, and heterogeneity, the HCCs were divided into four habitats. The volume fraction of each habitat was quantified. The logistic regression was used to explore the risk factors from habitat fraction and clinical variables. Clinical, habitat, and nomogram models were constructed using the identified risk factors from clinical characteristics, habitat fraction, and their combination, respectively. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curves (AUCs). RESULTS MVI-positive HCC exhibited a significantly higher fraction of habitat 4 (f4) and a significantly lower fraction of habitat 2 (f2) (p < 0.001), which were selected as risk factors. Additionally, tumor size and elevated alpha-fetoprotein (AFP) were also included as risk factors for MVI. The nomogram model demonstrated the highest diagnostic performance (AUC = 0.807), followed by the habitat model (AUC = 0.777) and the clinical model (AUC = 0.708). Decision curve analysis indicated that the nomogram model offered more net benefit in identifying MVI compared to the clinical model. CONCLUSIONS DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively determining the MVI of HCC with high accuracy. CLINICAL RELEVANCE STATEMENT The proposed strategy, diffusion-weighted imaging-based habitat imaging, can be applied for preoperatively and noninvasively identifying microvascular invasion in hepatocellular carcinoma, which offers potential benefits in terms of prognostic prediction and clinical management. KEY POINTS • This study proposed a strategy of DWI-based habitat imaging for hepatocellular carcinoma. • The habitat imaging-derived metrics can serve as diagnostic markers for identifying the microvascular invasion. • Integrating the habitat-based metric and clinical variable, a predictive nomogram was constructed and displayed high accuracy for predicting microvascular invasion.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Jiejun Chen
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, 200032, China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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Zhang Y, Sheng R, Dai Y, Yang C, Zeng M. The value of varying diffusion curvature MRI for assessing the microvascular invasion of hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:1154-1164. [PMID: 38311671 DOI: 10.1007/s00261-023-04168-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 02/06/2024]
Abstract
PURPOSE Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power. RESULTS MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10-3 vs 0.572 ± 0.148 × 10-3 mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10-3 vs 1.471 ± 0.322 × 10-3 mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve. CONCLUSION VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making.
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Affiliation(s)
- Yunfei Zhang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech Univerisity, Shanghai, 200032, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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Li YX, Lv WL, Qu MM, Wang LL, Liu XY, Zhao Y, Lei JQ. Research progresses of imaging studies on preoperative prediction of microvascular invasion of hepatocellular carcinoma. Clin Hemorheol Microcirc 2024; 88:171-180. [PMID: 39031344 DOI: 10.3233/ch-242286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer, accounting for approximately 90% of liver cancer cases. It currently ranks as the fifth most prevalent cancer worldwide and represents the third leading cause of cancer-related mortality. As a malignant disease with surgical resection and ablative therapy being the sole curative options available, it is disheartening that most HCC patients who undergo liver resection experience relapse within five years. Microvascular invasion (MVI), defined as the presence of micrometastatic HCC emboli within liver vessels, serves as an important histopathological feature and indicative factor for both disease-free survival and overall survival in HCC patients. Therefore, achieving accurate preoperative noninvasive prediction of MVI holds vital significance in selecting appropriate clinical treatments and improving patient prognosis. Currently, there are no universally recognized criteria for preoperative diagnosis of MVI in clinical practice. Consequently, extensive research efforts have been directed towards preoperative imaging prediction of MVI to address this problem and the relative research progresses were reviewed in this article to summarize its current limitations and future research prospects.
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Affiliation(s)
- Yi-Xiang Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | - Wei-Long Lv
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
- Gansu Intelligent Imaging Medical Engineering Research Center, Lanzhou, China
- Precision Image Collaborative Innovation Gansu International Science and Technology Cooperation Base, Lanzhou, China
| | - Meng-Meng Qu
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | - Li-Li Wang
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
- Gansu Intelligent Imaging Medical Engineering Research Center, Lanzhou, China
- Precision Image Collaborative Innovation Gansu International Science and Technology Cooperation Base, Lanzhou, China
| | - Xiao-Yu Liu
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | - Ying Zhao
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | - Jun-Qiang Lei
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
- Gansu Intelligent Imaging Medical Engineering Research Center, Lanzhou, China
- Precision Image Collaborative Innovation Gansu International Science and Technology Cooperation Base, Lanzhou, China
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15
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Ma X, Qian X, Wang Q, Zhang Y, Zong R, Zhang J, Qian B, Yang C, Lu X, Shi Y. Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:1296-1309. [PMID: 37679641 PMCID: PMC10620280 DOI: 10.1007/s11547-023-01704-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023]
Abstract
OBJECTIVE Microvascular invasion (MVI) is a significant adverse prognostic indicator of intrahepatic cholangiocarcinoma (ICC) and affects the selection of individualized treatment regimens. This study sought to establish a radiomics nomogram based on the optimal VOI of multi-sequence MRI for predicting MVI in ICC tumors. METHODS 160 single ICC lesions with MRI scanning confirmed by postoperative pathology were randomly separated into training and validation cohorts (TC and VC). Multivariate analysis identified independent clinical and imaging MVI predictors. Radiomics features were obtained from images of 6 MRI sequences at 4 different VOIs. The least absolute shrinkage and selection operator algorithm was performed to enable the derivation of robust and effective radiomics features. Then, the best three sequences and the optimal VOI were obtained through comparison. The MVI prediction nomogram combined the independent predictors and optimal radiomics features, and its performance was evaluated via the receiver operating characteristics, calibration, and decision curves. RESULTS Tumor size and intrahepatic ductal dilatation are independent MVI predictors. Radiomics features extracted from the best three sequences (T1WI-D, T1WI, DWI) with VOI10mm (including tumor and 10 mm peritumoral region) showed the best predictive performance, with AUCTC = 0.987 and AUCVC = 0.859. The MVI prediction nomogram obtained excellent prediction efficacy in both TC (AUC = 0.995, 95%CI 0.987-1.000) and VC (AUC = 0.867, 95%CI 0.798-0.921) and its clinical significance was further confirmed by the decision curves. CONCLUSION A nomogram combining tumor size, intrahepatic ductal dilatation, and the radiomics model of MRI multi-sequence fusion at VOI10mm may be a predictor of preoperative MVI status in ICC patients.
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Affiliation(s)
- Xijuan Ma
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No. 199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China
| | - Xianling Qian
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
| | - Qing Wang
- Graduate Department, Bengbu Medical College, Bengbu, 233000, Anhui, People's Republic of China
| | - Yunfei Zhang
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Central Research Institute, United Imaging Healthcare, No. 2258 Chengbei Rd, Shanghai, 201807, People's Republic of China
| | - Ruilong Zong
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No. 199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China
| | - Jia Zhang
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No. 199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China
| | - Baoxin Qian
- Huiying Medical Technology, Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing City, 100192, People's Republic of China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China
| | - Xin Lu
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Shanghai, 200032, People's Republic of China.
- Department of Radiology, Shanghai Geriatric Medical Center, No. 2560 Chunshen Rd, Shanghai, 201104, People's Republic of China.
| | - Yibing Shi
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, No. 199 Jiefang South Road, Quanshan District, Xuzhou, 221009, Jiangsu, People's Republic of China.
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Huang XW, Ding J, Zheng RR, Ma JY, Cai MT, Powell M, Lin F, Yang YJ, Jin C. An ultrasound-based radiomics model for survival prediction in patients with endometrial cancer. J Med Ultrason (2001) 2023; 50:501-510. [PMID: 37310510 PMCID: PMC10955020 DOI: 10.1007/s10396-023-01331-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/23/2023] [Indexed: 06/14/2023]
Abstract
PURPOSE To establish a nomogram integrating radiomics features based on ultrasound images and clinical parameters for predicting the prognosis of patients with endometrial cancer (EC). MATERIALS AND METHODS A total of 175 eligible patients with ECs were enrolled in our study between January 2011 and April 2018. They were divided into a training cohort (n = 122) and a validation cohort (n = 53). Least absolute shrinkage and selection operator (LASSO) regression were applied for selection of key features, and a radiomics score (rad-score) was calculated. Patients were stratified into high risk and low-risk groups according to the rad-score. Univariate and multivariable COX regression analysis was used to select independent clinical parameters for disease-free survival (DFS). A combined model based on radiomics features and clinical parameters was ultimately established, and the performance was quantified with respect to discrimination and calibration. RESULTS Nine features were selected from 1130 features using LASSO regression in the training cohort, which yielded an area under the curve (AUC) of 0.823 and 0.792 to predict DFS in the training and validation cohorts, respectively. Patients with a higher rad-score were significantly associated with worse DFS. The combined nomogram, which was composed of clinically significant variables and radiomics features, showed a calibration and favorable performance for DFS prediction (AUC 0.893 and 0.885 in the training and validation cohorts, respectively). CONCLUSION The combined nomogram could be used as a tool in predicting DFS and may assist individualized decision making and clinical treatment.
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Affiliation(s)
- Xiao-Wan Huang
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Jie Ding
- Department of Ultrasound Imaging, Yueqing Hospital of Wenzhou Medical University, Wenzhou, 325015, People's Republic of China
| | - Ru-Ru Zheng
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Jia-Yao Ma
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Meng-Ting Cai
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Martin Powell
- Nottingham Treatment Centre, Nottingham University Affiliated Hospital, Nottingham, NG7 2FT, UK
| | - Feng Lin
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Yun-Jun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Chu Jin
- Wenzhou Medical University Renji College, University Town, Chashan, Wenzhou, 325000, People's Republic of China.
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Li J, Su X, Xu X, Zhao C, Liu A, Yang L, Song B, Song H, Li Z, Hao X. Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma. Crit Rev Oncol Hematol 2023; 190:104107. [PMID: 37633349 DOI: 10.1016/j.critrevonc.2023.104107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/22/2023] [Indexed: 08/28/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.
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Affiliation(s)
- Jian Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Xin Su
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Xiao Xu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Changchun Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Ang Liu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Liwen Yang
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Baoling Song
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Hao Song
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Zihan Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Xiangyong Hao
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China.
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18
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Jiang D, Qian Y, Tan BB, Zhu XL, Dong H, Qian R. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using ultrasound features including elasticity. World J Gastrointest Surg 2023; 15:2042-2051. [PMID: 37901729 PMCID: PMC10600765 DOI: 10.4240/wjgs.v15.i9.2042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/23/2023] [Accepted: 07/27/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is an important predictor of poor prognosis in patients with hepatocellular carcinoma (HCC). Accurate preoperative prediction of MVI in HCC would provide useful information to guide the choice of therapeutic strategy. Shear wave elastography (SWE) plays an important role in hepatic imaging, but its value in the preoperative prediction of MVI in HCC has not yet been proven. AIM To explore the value of conventional ultrasound features and SWE in the preoperative prediction of MVI in HCC. METHODS Patients with a postoperative pathological diagnosis of HCC and a definite diagnosis of MVI were enrolled in this study. Conventional ultrasound features and SWE features such as maximal elasticity (Emax) of HCCs and Emax of the periphery of HCCs were acquired before surgery. These features were compared between MVI-positive HCCs and MVI-negative HCCs and between mild MVI HCCs and severe MVI HCCs. RESULTS This study included 86 MVI-negative HCCs and 102 MVI-positive HCCs, including 54 with mild MVI and 48 with severe MVI. Maximal tumor diameters, surrounding liver tissue, color Doppler flow, Emax of HCCs, and Emax of the periphery of HCCs were significantly different between MVI-positive HCCs and MVI-negative HCCs. In addition, Emax of the periphery of HCCs was significantly different between mild MVI HCCs and severe MVI HCCs. Higher Emax of the periphery of HCCs and larger maximal diameters were independent risk factors for MVI, with odds ratios of 2.820 and 1.021, respectively. CONCLUSION HCC size and stiffness of the periphery of HCC are useful ultrasound criteria for predicting positive MVI. Preoperative ultrasound and SWE can provide useful information for the prediction of MVI in HCCs.
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Affiliation(s)
- Dong Jiang
- Department of Ultrasound, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Yi Qian
- Department of Ultrasound, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Bi-Bo Tan
- Department of Ultrasound, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Xia-Ling Zhu
- Department of Ultrasound, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Hui Dong
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Rong Qian
- Department of Ultrasound, No. 905 Hospital of PLA Navy, Shanghai 200052, China
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Sheng R, Yang C, Zhang Y, Wang H, Zheng B, Han J, Sun W, Zeng M. The significance of the predominant component in combined hepatocellular-cholangiocarcinoma: MRI manifestation and prognostic value. LA RADIOLOGIA MEDICA 2023; 128:1047-1060. [PMID: 37474663 DOI: 10.1007/s11547-023-01682-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE To investigate the significance of the predominant component of combined hepatocellular-cholangiocarcinoma (cHCC-CC) in terms of MRI manifestation and its potential prognostic value compared to hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). MATERIALS AND METHODS A total of 300 patients with chronic liver disease from two centers were retrospectively enrolled, including 100 surgically proven cases of cHCC-CC, HCC, and ICC each. Univariate and multivariate regression analyses were performed to identify independent predictors for distinguishing HCC-predominant cHCC-CC and ICC-predominant cHCC-CC from HCC and ICC, respectively. Diagnostic models were constructed based on the independent features. Recurrence-free survival (RFS) was estimated and compared between groups. RESULTS The predominant component was an independent predictor for RFS in cHCC-CC (hazard ratio = 1.957, P = 0.044). The presence of targetoid appearance (odds ratio(OR) = 10.907, P = 0.001), lack of enhancing capsule (OR = 0.072, P = 0.001) and arterial peritumoral enhancement (OR = 0.091, P = 0.003) were independent predictors suggestive of HCC-predominant cHCC-CC over HCC; their combination yielded an area under the curve of 0.756. No significant differences were observed in RFS between HCC-predominant cHCC-CC and HCC (P = 0.864). Male gender (OR = 4.049, P = 0.015), higher alpha fetoprotein (OR = 16.789, P < 0.001) and normal carbohydrate antigen 19-9 (OR = 0.343, P = 0.036) levels, presence of enhancing capsule (OR = 7.819, P < 0.001) and hemorrhage (OR = 23.526, P = 0.004), and lack of targetoid appearance (OR = 0.129, P = 0.005) and liver surface retraction (OR = 0.190, P = 0.021) were independent predictors suggestive of ICC-predominant cHCC-CC over ICC; their combination yielded an area under the curve value of 0.898. ICC-predominant cHCC-CC exhibited poorer survival with shorter RFS than ICC (P = 0.009). CONCLUSION The predominant histopathological component is closely related to the imaging manifestation of cHCC-CC; and more importantly, it plays a significant prognostic role, which may alter the RFS prognosis of cHCC-CC.
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Affiliation(s)
- Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Shanghai , 361006, Fujian, China
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yunfei Zhang
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Central Research Institute, United Imaging Healthcare, Shanghai, 201800, China
| | - Heqing Wang
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Shanghai , 361006, Fujian, China
| | - Beixuan Zheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jing Han
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
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You H, Wang J, Ma R, Chen Y, Li L, Song C, Dong Z, Feng S, Zhou X. Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism. Bioengineering (Basel) 2023; 10:948. [PMID: 37627833 PMCID: PMC10451856 DOI: 10.3390/bioengineering10080948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/26/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023] Open
Abstract
Preoperative prediction of microvascular invasion (MVI) is essential for management decision in hepatocellular carcinoma (HCC). Deep learning-based prediction models of MVI are numerous but lack clinical interpretation due to their "black-box" nature. Consequently, we aimed to use an attention-guided feature fusion network, including intra- and inter-attention modules, to solve this problem. This retrospective study recruited 210 HCC patients who underwent gadoxetate-enhanced MRI examination before surgery. The MRIs on pre-contrast, arterial, portal, and hepatobiliary phases (hepatobiliary phase: HBP) were used to develop single-phase and multi-phase models. Attention weights provided by attention modules were used to obtain visual explanations of predictive decisions. The four-phase fusion model achieved the highest area under the curve (AUC) of 0.92 (95% CI: 0.84-1.00), and the other models proposed AUCs of 0.75-0.91. Attention heatmaps of collaborative-attention layers revealed that tumor margins in all phases and peritumoral areas in the arterial phase and HBP were salient regions for MVI prediction. Heatmaps of weights in fully connected layers showed that the HBP contributed the most to MVI prediction. Our study firstly implemented self-attention and collaborative-attention to reveal the relationship between deep features and MVI, improving the clinical interpretation of prediction models. The clinical interpretability offers radiologists and clinicians more confidence to apply deep learning models in clinical practice, helping HCC patients formulate personalized therapies.
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Affiliation(s)
| | | | | | | | | | | | | | - Shiting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58th the Second Zhongshan Road, Guangzhou 510080, China; (H.Y.); (J.W.); (R.M.); (Y.C.); (L.L.); (C.S.); (Z.D.)
| | - Xiaoqi Zhou
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, 58th the Second Zhongshan Road, Guangzhou 510080, China; (H.Y.); (J.W.); (R.M.); (Y.C.); (L.L.); (C.S.); (Z.D.)
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Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S. Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics. Invest Radiol 2023; 58:548-560. [PMID: 36822661 PMCID: PMC10332659 DOI: 10.1097/rli.0000000000000962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Indexed: 02/25/2023]
Abstract
ABSTRACT With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.
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Affiliation(s)
- Akifumi Hagiwara
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Christina Andica
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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22
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Li C, Wen Y, Xie J, Chen Q, Dang Y, Zhang H, Guo H, Long L. Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study. Front Oncol 2023; 13:1167209. [PMID: 37305565 PMCID: PMC10248416 DOI: 10.3389/fonc.2023.1167209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
Background Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. Purpose To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. Methods 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Results Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). Conclusion DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.
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Affiliation(s)
- Chenhui Li
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yan Wen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jinhuan Xie
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qianjuan Chen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare Ltd., Wuhan, Hubei, China
| | - Hu Guo
- MR Application, Siemens Healthcare Ltd., Changsha, Hunan, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Liu G, Ma D, Wang H, Zhou J, Shen Z, Yang Y, Chen Y, Sack I, Guo J, Li R, Yan F. Three-dimensional multifrequency magnetic resonance elastography improves preoperative assessment of proliferative hepatocellular carcinoma. Insights Imaging 2023; 14:89. [PMID: 37198348 PMCID: PMC10192481 DOI: 10.1186/s13244-023-01427-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/14/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND To investigate the viscoelastic signatures of proliferative hepatocellular carcinoma (HCC) using three-dimensional (3D) magnetic resonance elastography (MRE). METHODS This prospective study included 121 patients with 124 HCCs as training cohort, and validation cohort included 33 HCCs. They all underwent preoperative conventional magnetic resonance imaging (MRI) and tomoelastography based on 3D multifrequency MRE. Viscoelastic parameters of the tumor and liver were quantified as shear wave speed (c, m/s) and loss angle (φ, rad), representing stiffness and fluidity, respectively. Five MRI features were evaluated. Multivariate logistic regression analyses were used to determine predictors of proliferative HCC to construct corresponding nomograms. RESULTS In training cohort, model 1 (Combining cirrhosis, hepatitis virus, rim APHE, peritumoral enhancement, and tumor margin) yielded an area under the curve (AUC), sensitivity, specificity, accuracy of 0.72, 58.73%,78.69%, 67.74%, respectively. When adding MRE properties (tumor c and tumor φ), established model 2, the AUC increased to 0.81 (95% CI 0.72-0.87), with sensitivity, specificity, accuracy of 71.43%, 81.97%, 75%, respectively. The C-index of nomogram of model 2 was 0.81, showing good performance for proliferative HCC. Therefore, integrating tumor c and tumor φ can significantly improve the performance of preoperative diagnosis of proliferative HCC (AUC increased from 0.72 to 0.81, p = 0.012). The same finding was observed in the validation cohort, with AUC increasing from 0.62 to 0.77 (p = 0.021). CONCLUSIONS Proliferative HCC exhibits low stiffness and high fluidity. Adding MRE properties (tumor c and tumor φ) can improve performance of conventional MRI for preoperative diagnosis of proliferative HCC. CRITICAL RELEVANCE STATEMENT We investigated the viscoelastic signatures of proliferative hepatocellular carcinoma (HCC) using three-dimensional (3D) magnetic resonance elastography (MRE), and find that adding MRE properties (tumor c and tumor φ) can improve performance of conventional MRI for preoperative diagnosis of proliferative HCC.
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Affiliation(s)
- Guixue Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Di Ma
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huafeng Wang
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiahao Zhou
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Zhehan Shen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Yuchen Yang
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yongjun Chen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ingolf Sack
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jing Guo
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China.
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Zhang XP, Xu S, Lin ZY, Gao QL, Wang K, Chen ZL, Yan ML, Zhang F, Tang YF, Zhao ZM, Li CG, Lau WY, Cheng SQ, Hu MG, Liu R. Significance of anatomical resection and resection margin status in patients with HBV-related hepatocellular carcinoma and microvascular invasion: a multicenter propensity score-matched study. Int J Surg 2023; 109:679-688. [PMID: 36917129 PMCID: PMC10389431 DOI: 10.1097/js9.0000000000000204] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 01/02/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND Microvascular invasion (MVI) is a risk factor for postoperative survival outcomes for patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to evaluate the impact of anatomical resection (AR) versus nonanatomical resection (NAR) combined with resection margin (RM) (narrow RM <1 cm vs. wide RM ≥1 cm) on long-term prognosis in hepatitis B virus-related HCC patients with MVI. MATERIALS AND METHODS Data from multicenters on HCC patients with MVI who underwent hepatectomy was analyzed retrospectively. Propensity score matching analysis was performed in these patients. RESULTS The 1965 enrolled patients were divided into four groups: AR with wide RM ( n =715), AR with narrow RM ( n =387), NAR with wide RM ( n =568), and NAR with narrow RM ( n =295). Narrow RM ( P <0.001) and NAR ( P <0.001) were independent risk factors for both overall survival and recurrence-free survival in these patients based on multivariate analyses. For patients in both the AR and NAR groups, wide RM resulted in significantly lower operative margin recurrence rates than those patients in the narrow RM groups after propensity score matching ( P =0.002 and 0.001). Patients in the AR with wide RM group had significantly the best median overall survival (78.9 vs. 51.5 vs. 48.0 vs. 36.7 months, P <0.001) and recurrence-free survival (23.6 vs. 14.8 vs. 17.8 vs. 9.0 months, P <0.001) than those in the AR with narrow RM, NAR with wide RM or with narrow RM groups, respectively. CONCLUSIONS If technically feasible and safe, AR combined with wide RM should be the recommended therapeutic strategy for HCC patients who are estimated preoperatively with a high risk of MVI.
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Affiliation(s)
- Xiu-Ping Zhang
- Faculty of Hepato-Biliary-Pancreatic Surgery, Chinese People’s Liberation Army (PLA) General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing
| | - Shuai Xu
- Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan
| | - Zhao-Yi Lin
- Faculty of Hepato-Biliary-Pancreatic Surgery, Chinese People’s Liberation Army (PLA) General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing
| | - Qing-Lun Gao
- Department of Hepatobiliary Surgery, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Shandong
| | - Kang Wang
- Department of Hepatobiliary Surgery, Affiliated Hospital of Binzhou Medical College, Shandong
| | - Zi-Li Chen
- Department of Hepatobiliary Surgery, Affiliated Hospital of Guizhou Medical University, Guizhou
| | - Mao-Lin Yan
- Department of Hepato-Biliary-Pancreatic Surgery, Fujian Provincial Hospital, Fujian
| | - Fan Zhang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai
| | - Yu-Fu Tang
- Department of Hepatobiliary Surgery, Northern Theater General Hospital, Liaoning
| | - Zhi-Ming Zhao
- Faculty of Hepato-Biliary-Pancreatic Surgery, Chinese People’s Liberation Army (PLA) General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing
| | - Cheng-Gang Li
- Faculty of Hepato-Biliary-Pancreatic Surgery, Chinese People’s Liberation Army (PLA) General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing
| | - Wan Yee Lau
- Faculty of Hepato-Biliary-Pancreatic Surgery, Chinese People’s Liberation Army (PLA) General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing
- Faculty of Medicine, the Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China
| | - Shu-Qun Cheng
- Department of Hepatobiliary Surgery, Affiliated Hospital of Binzhou Medical College, Shandong
| | - Ming-Gen Hu
- Faculty of Hepato-Biliary-Pancreatic Surgery, Chinese People’s Liberation Army (PLA) General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing
| | - Rong Liu
- Faculty of Hepato-Biliary-Pancreatic Surgery, Chinese People’s Liberation Army (PLA) General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing
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Zhang Y, Yang Y, Ning G, Wu X, Yang G, Li Y. Contrast computed tomography-based radiomics is correlation with COG risk stratification of neuroblastoma. Abdom Radiol (NY) 2023; 48:2111-2121. [PMID: 36951989 DOI: 10.1007/s00261-023-03875-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/23/2023] [Accepted: 02/23/2023] [Indexed: 03/24/2023]
Abstract
PURPOSE Although a risk stratification strategy for neuroblastoma (NB) has been proposed, precise and convenient clinical risk estimation remains challenging. This study aimed to investigate the correlation of contrast computed tomography (CT)-based radiomics with NB risk stratification. METHODS Patients with NB (n = 289) from two centers (244 and 45 patients in the training/testing and external validation cohorts, respectively) were divided into nonhigh- and high-risk groups. A total of 1648 radiomics features were extracted from the arterial phase, and the radiomics signature was constructed using rad scores, whereas the clinical model was established based on clinical factors. Further, a combined nomogram was developed based on the clinical factors and radiomics signatures. Finally, receiver operating characteristic curve and decision curve analyses (DCA) were used to assess the performance of the established models. RESULTS Seventeen radiomics features were used to construct the radiomics signature. A significant difference was observed in the rad score between the two groups in the training (0.540 vs. 0.704, P < 0.001) and testing (0.563 vs. 0.969, P < 0.001) cohorts. The nomogram showed a higher area under the curve (AUC) in the training (AUC = 0.87), testing (AUC = 0.83), and external validation (AUC = 0.84) cohorts than other models. The Hosmer-Lemeshow test and calibration curves indicated that the nomogram fit perfectly. DCA demonstrated that the clinical-radiomics nomogram was more beneficial. CONCLUSIONS Contrast CT-based radiomics shows correlation with COG risk stratification of NB. Radiomics features combined with clinical factors showed the best performance, which may improve the management of patients with NB.
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Affiliation(s)
- Yimao Zhang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, China
| | - Yuhan Yang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, China
| | - Gang Ning
- Department of Radiology, West China Second Hospital, Sichuan University, No. 20, Section 3, Renmin South Road, Chengdu, Sichuan, China
| | - Xin Wu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, China
| | - Gang Yang
- Department of Pediatric Surgery, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, China
| | - Yuan Li
- Department of Pediatric Surgery, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, China.
- Laboratory of Digestive Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Delta computed tomography radiomics features-based nomogram predicts long-term efficacy after neoadjuvant chemotherapy in advanced gastric cancer. LA RADIOLOGIA MEDICA 2023; 128:402-414. [PMID: 36940007 DOI: 10.1007/s11547-023-01617-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 03/07/2023] [Indexed: 03/21/2023]
Abstract
BACKGROUND AND OBJECTIVE No effective preoperative tool is available for predicting the prognosis of advanced gastric cancer (AGC) treated by neoadjuvant chemotherapy (NAC). We aimed to explore the association between change values ("delta") in the radiomic signatures of computed tomography (CT) (delCT-RS) before and after NAC for AGC and overall survival(OS). METHODS AND DESIGN A total of 132 AGC patients with AGC were studied as a training cohort in our center, and 45 patients from another center were used as an external validation set. A radiomic signatures-clinical-nomogram(RS-CN) was established using delCT-RS and preoperative clinical variables. The prediction performance of RS-CN was evaluated using the area under the receiver operating characteristic (ROC)curve (AUC values), time-dependent ROC, decision curve analysis(DCA) and C-index. RESULTS Multivariable Cox regression analyses showed that delCT-RS, cT-stage, cN-stage, Lauren-type and the value of variation of carcinoma embryonic antigen (CEA) between NAC were independent risk factors for 3-year OS of AGC. In the training cohort, RS-CN had a good prediction performance for OS (C-Index 0.73) and AUC values were significantly better than those of delCT-RS, ypTNM-stage and tumor regression grade(TRG) (0.827 vs 0.704 vs 0.749 vs 0.571, p < 0.001). DCA and time-dependent ROC of RS-CN were better than those of ypTNM stage, TRG grade and delCT-RS. The prediction performance of the validation set was equivalent to that of the training set. The cut-off (177.2) of RS-CN score was obtained from X-Tile software, a score of > 177.2 was defined as high-risk group(HRG), and scores of ≤ 177.2 were defined as the low-risk group(LRG). The 3-year OS and disease free survival(DFS) of patients in the LRG were significantly better than those in the HRG. Adjuvant chemotherapy(AC) can only significantly improve the 3-year OS and DFS of the LRG. (p < 0.05). CONCLUSIONS Our nomogram based on delCT-RS has good prediction of prognosis before surgery and helps identify patients that are most likely to benefit from AC. It works well in precise and individualised NAC in AGC.
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Chen J, Liu D, Guo Y, Zhang Y, Guo Y, Jiang M, Dai Y, Yao X. Preoperative identification of cytokeratin 19 status of hepatocellular carcinoma based on diffusion kurtosis imaging. Abdom Radiol (NY) 2023; 48:579-589. [PMID: 36416905 DOI: 10.1007/s00261-022-03736-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE To explore the potential value of diffusion kurtosis imaging (DKI) for identification of cytokeratin 19 (CK19) status of HCCs. METHODS This study was approved by the local institute review board and written informed consent was obtained. 73 patients with pathologically confirmed HCCs were included in this prospective study. All the diffusion-weighted (DW) images were acquired using a 3.0-T MR scanner with 4 b-values (0, 800, 1500 and 2000 s/mm2). The mean diffusion value (MD) and mean kurtosis coefficient (MK) from DKI, apparent diffusion coefficient (ADC) from DW imaging (b = 0, 500 s/mm2), and tumor-to-liver signal intensity ratios on ADC map (SIRADC) and DW images with b-value of 500 s/mm2 (SIRb500) were calculated and compared between CK19-positive (n = 23) and CK19-negative (n = 50) HCC groups. Univariate and multivariate logistic regression analyses were used to identify risk factors for the positive expression of CK19. RESULTS Increased a-fetoprotein level (p = 0.021) and SIRb500 (p = 0.006) and decreased ADC (p = 0.021) and MD (p < 0.001) were significantly correlated with CK19-positive HCCs at univariate analysis. Decreased MD value (odds ratio: 0.042, p = 0.002) and a-fetoprotein level (odds ratio: 5.139, p = 0.015) were the independent risk factors for CK19-positive HCCs at multivariate analysis. The area under the curve of MD value by receiver operating characteristic analysis was 0.823 with a sensitivity of 86.96% and a specificity of 76% for the prediction of CK19-positive HCCs. CONCLUSION The decreased MD value derived from DKI is potential quantitative biomarker for predicting CK19-positive HCCs.
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Affiliation(s)
- Jiejun Chen
- Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 130, Fenglin Rd, Xuhui District, Shanghai, 200032, People's Republic of China
| | - Dingxia Liu
- Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 130, Fenglin Rd, Xuhui District, Shanghai, 200032, People's Republic of China
| | - Yixian Guo
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 130, Fenglin Rd, Xuhui District, Shanghai, 200032, People's Republic of China
| | - Yunfei Zhang
- Central Research Institute, United Imaging Healthcare, Shanghai, 200032, People's Republic of China
| | - Yinglong Guo
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 130, Fenglin Rd, Xuhui District, Shanghai, 200032, People's Republic of China
| | - Mengmeng Jiang
- Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 130, Fenglin Rd, Xuhui District, Shanghai, 200032, People's Republic of China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare, Shanghai, 200032, People's Republic of China
| | - Xiuzhong Yao
- Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China.
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, No 130, Fenglin Rd, Xuhui District, Shanghai, 200032, People's Republic of China.
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Elkomos BE, Abdo M, Mamdouh R, Abdelaal A. Can living donor liver transplantation provide similar outcomes to deceased-donor liver transplantation for hepatocellular carcinoma? A systematic review and meta-analysis. Hepatol Int 2023; 17:18-37. [PMID: 36564609 PMCID: PMC9894961 DOI: 10.1007/s12072-022-10435-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/03/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIM A potential solution to the deceased organ shortage is to include live organ donations and to identify patients with lower rates of HCC recurrence to fairly allocate liver grafts. Our aims were to detect the long-term outcomes of LDLT versus DDLT for HCC and predictors of recurrence after transplantation. METHODS PubMed, Scopus, Web of Science, Cochrane library were searched for eligible studies from inception to July 2021 and a systematic review and meta-analysis were done. RESULTS 35 studies with a total of 7822 patients were included. The 1-, 3-, 4 year-OS showed trivial improvement for LDLT recipients. However, the two modalities had similar 5-, 6- and 10-year OS. A significant improvement in the ITT-OS was observed for LDLT recipients. Regarding the DFS and recurrence after transplantation, no significant difference was observed between LDLT and DDLT. In addition to that, the pooled hazard ratio of the included studies showed that Milan criteria, level of AFP, presence of vascular invasion, tumor differentiation were significant predictors of recurrence. CONCLUSION The cancer biology (not the graft type) is the most important determinant of recurrence and survival after LT. However, LDLT provided much better survival benefits to HCC patients especially in regions that suffer from low deceased organ availability.
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Affiliation(s)
| | - Mostafa Abdo
- General Surgery Department, Ain Shams University Hospital, Cairo, Egypt
| | - Remon Mamdouh
- General Surgery Department, Ain Shams University Hospital, Cairo, Egypt
| | - Amr Abdelaal
- General Surgery Department, Ain Shams University Hospital, Cairo, Egypt
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Combining Preoperative Clinical and Imaging Characteristics to Predict MVI in Hepatitis B Virus-Related Combined Hepatocellular Carcinoma and Cholangiocarcinoma. J Pers Med 2023; 13:jpm13020246. [PMID: 36836479 PMCID: PMC9968216 DOI: 10.3390/jpm13020246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/23/2023] [Accepted: 01/28/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA) is a rare form of primary liver malignancy. Microvascular invasion (MVI) indicates poor postsurgical prognosis in cHCC-CCA. The objective of this study was to investigate preoperative predictors of MVI in hepatitis B virus (HBV) -related cHCC-CCA patients. METHODS A total of 69 HBV-infected patients with pathologically confirmed cHCC-CCA who underwent hepatectomy were included. Univariate and multivariate analyses were conducted to determine independent risk factors that were then incorporated into the predictive model associated with MVI. Receiver operating characteristic analysis was used to assess the predictive performance of the new model. RESULTS For the multivariate analysis, γ-glutamyl transpeptidase (OR, 3.69; p = 0.034), multiple nodules (OR, 4.41; p = 0.042) and peritumoral enhancement (OR, 6.16; p = 0.004) were independently associated with MVI. Active replication of HBV indicated by positive HBeAg showed no differences between MVI-positive and MVI-negative patients. The prediction score using the independent predictors achieved an area under the curve of 0.813 (95% CI 0.717-0.908). A significantly lower recurrence-free survival was observed in the high-risk group with a score of ≥1 (p < 0.001). CONCLUSION γ-glutamyl transpeptidase, peritumoral enhancement and multiple nodules were independent preoperative predictors of MVI in HBV-related cHCC-CCA patients. The established prediction score demonstrated satisfactory performance in predicting MVI pre-operatively and may facilitate prognostic stratification.
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Rajesh A, Chartier C, Asaad M, Butler CE. A Synopsis of Artificial Intelligence and its Applications in Surgery. Am Surg 2023; 89:20-24. [PMID: 35713389 DOI: 10.1177/00031348221109450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) has made steady in-roads into the healthcare scenario over the last decade. While widespread adoption into clinical practice remains elusive, the outreach of this discipline has progressed beyond the physician scientist, and different facets of this technology have been incorporated into the care of surgical patients. New AI applications are developing at rapid pace, and it is imperative that the general surgeon be aware of the broad utility of AI as applicable in his or her day-to-day practice, so that healthcare continues to remain up-to-date and evidence based. This review provides a broad account of the tip of the AI iceberg and highlights it potential for positively impacting surgical care.
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Affiliation(s)
- Aashish Rajesh
- Department of Surgery, 14742University of Texas Health Science Center, San Antonio, TX, USA
| | | | - Malke Asaad
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Charles E Butler
- Department of Plastic & Reconstructive Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study. Magn Reson Imaging 2023; 95:110-117. [PMID: 34506910 DOI: 10.1016/j.mri.2021.09.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 09/05/2021] [Accepted: 09/05/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE To assess the clinical potential of a set of new diffusion parameters (D, β, and μ) derived from fractional order calculus (FROC) diffusion model in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS Between January 2019 to November 2020, a total of 63 patients with HCC were enrolled in this study. Diffusion-weighted images were acquired by using ten b-values (0-2000 s/mm2). The FROC model parameters including diffusion coefficient (D), fractional order parameter (β), a microstructural quantity (μ) together with a conventional apparent diffusion coefficient (ADC) were calculated. Intraclass coefficients were calculated for assessing the agreement of parameters quantified by two radiologists. The differences of these values between the MVI-positive and MVI-negative HCC groups were compared by using independent sample t-test or the Mann-Whitney U test. Then the parameters showing significant differences between subgroups, including the β and D, were integrated to develop a comprehensive predictive model via binary logistic regression. The diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS Among all the studied diffusion parameters, significant differences were found in D, β, and ADC between the MVI-positive and MVI-negative groups. MVI-positive HCCs showed significantly higher β values (0.65 ± 0.17 vs. 0.51 ± 0.13, P = 0.001), along with lower D values (0.84 ± 0.11 μm2/ms vs. 1.03 ± 0.13 μm2/ms, P < 0.001) and lower ADC values (1.38 ± 0.46 μm2/ms vs. 2.09 ± 0.70 μm2/ms, P < 0.001) than those of MVI-negative HCCs. According to the ROC analysis, the combination of D and β demonstrated the largest area under the ROC curve (0.920) compared with individual parameters (D: 0.912; β: 0.733; and ADC: 0.831) for differentiating MVI-positive from MVI-negative HCCs. CONCLUSIONS The FROC parameters can be used as noninvasive quantitative imaging markers for preoperatively predicting the MVI status of HCCs.
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Gao S, Zhang Y, Sun W, Jin K, Dai Y, Wang F, Qian X, Han J, Sheng R, Zeng M. Assessment of an
MR
Elastography‐Based Nomogram as a Potential Imaging Biomarker for Predicting Microvascular Invasion of Hepatocellular Carcinoma. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/20/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Affiliation(s)
- Shanshan Gao
- Department of Radiology, Zhongshan Hospital Fudan University Shanghai China
- Central Research Institute United Imaging Healthcare Shanghai China
| | - Yunfei Zhang
- Central Research Institute United Imaging Healthcare Shanghai China
- Shanghai Institute of Medical Imaging Shanghai China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital Fudan University Shanghai China
- Central Research Institute United Imaging Healthcare Shanghai China
| | - Kaipu Jin
- Department of Radiology, Zhongshan Hospital Fudan University Shanghai China
- Central Research Institute United Imaging Healthcare Shanghai China
| | - Yongming Dai
- Shanghai Institute of Medical Imaging Shanghai China
| | - Feihang Wang
- Central Research Institute United Imaging Healthcare Shanghai China
- Department of Interventional Radiology, Zhongshan Hospital Fudan University Shanghai China
| | - Xianling Qian
- Department of Radiology, Zhongshan Hospital Fudan University Shanghai China
- Central Research Institute United Imaging Healthcare Shanghai China
| | - Jing Han
- Department of Pathology, Zhongshan Hospital Fudan University Shanghai China
| | - Ruofan Sheng
- Department of Radiology, Zhongshan Hospital Fudan University Shanghai China
- Department of Radiology, Zhongshan Hospital (Xiamen) Fudan University Xiamen China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital Fudan University Shanghai China
- Central Research Institute United Imaging Healthcare Shanghai China
- Department of Cancer Center, Zhongshan Hospital Fudan University Shanghai China
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Hao Y, Yang Q, He Q, Hu H, Weng Z, Su Z, Chen S, Peng S, Kuang M, Chen Z, Xu L. Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction. Eur J Med Res 2022; 27:276. [PMID: 36464701 PMCID: PMC9720918 DOI: 10.1186/s40001-022-00910-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/22/2022] [Indexed: 12/07/2022] Open
Abstract
BACKGROUND AND AIM Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers. METHODS A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients' plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used. RESULTS We grouped 35 HCC patients into 2 categories, including the MVI- group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI- group than that in the predicted MVI + group. CONCLUSIONS In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy.
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Affiliation(s)
- Yijie Hao
- grid.412615.50000 0004 1803 6239Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China ,grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China
| | - Qingxia Yang
- grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China ,grid.412615.50000 0004 1803 6239Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
| | - Qiye He
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201203 China
| | - Huanjing Hu
- grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China
| | - Zongpeng Weng
- grid.12981.330000 0001 2360 039XDepartment of Biology and Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
| | - Zhixi Su
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201203 China
| | - Shuling Chen
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
| | - Sui Peng
- grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China ,grid.412615.50000 0004 1803 6239Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
| | - Ming Kuang
- grid.412615.50000 0004 1803 6239Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China ,grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China
| | - Zhihang Chen
- grid.412615.50000 0004 1803 6239Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China
| | - Lixia Xu
- grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan Er Road, Guangzhou, 510080 Guangdong Province China ,grid.412615.50000 0004 1803 6239Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080 Guangdong Province China
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Zhang L, Li M, Zhu J, Zhang Y, Xiao Y, Dong M, Zhang L, Wang J. The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma. Eur Radiol 2022; 33:4103-4114. [PMID: 36435877 DOI: 10.1007/s00330-022-09290-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate the potential diagnostic value of MR elastography (MRE)-based stiffness to noninvasively predict the microvascular invasion (MVI) grade in hepatocellular carcinoma (HCC). METHODS One hundred eighty-five patients with histopathology-proven HCC who underwent MRI and MRE examinations before hepatectomy were retrospectively enrolled. According to the three-tiered MVI grading system, the MVI was divided into negative-MVI (n = 89) and positive-MVI (n = 96) groups, and the latter group was categorized into mild-MVI (n = 49) and severe-MVI (n = 47) subgroups. Logistic regression and area under the receiver operating characteristic curve (AUC) analyses were used to determine the predictors associated with MVI grade and analyze their performances, respectively. RESULTS Among the 185 patients, tumor size ≥ 50 mm (p = 0.031), tumor stiffness (TS)/liver stiffness (LS) > 1.47 (p = 0.001), TS > 4.33 kPa (p < 0.001), and nonsmooth tumor margin (p = 0.006) were significant independent predictors for positive-MVI. Further analyzing the subgroups, tumor size ≥ 50 mm (p < 0.001), TS > 5.35 kPa (p = 0.001), and AFP level > 400 ng/mL (p = 0.044) were independently associated with severe-MVI. The models incorporating MRE and clinical-radiological features together performed better for evaluating positive-MVI (AUC: 0.846) and severe-MVI (AUC: 0.802) than the models using clinical-radiological predictors alone (AUC: positive-/severe-MVI, 0.737/0.743). Analysis of recurrence-free survival and overall survival showed the predicted positive-MVI/severe-MVI groups based on combined models had significantly poorer prognoses than predicted negative-MVI/mild-MVI groups, respectively (all p < 0.05). CONCLUSIONS MRE-based stiffness was an independent predictor for both the positive-MVI and severe-MVI. The combination of MRE and clinical-radiological models might be a useful tool for evaluating HCC patients' prognoses underwent hepatectomy by preoperatively predicting the MVI grade. KEY POINTS • The severe-microvascular invasion (MVI) grade had the highest tumor stiffness (TS), followed by mild-MVI and non-MVI, and there were significances among the three different MVI grades. • MR elastography (MRE)-based stiffness value was an independent predictor of positive-MVI and severe-MVI in hepatocellular carcinoma (HCC) preoperatively. • When combined with clinical-radiological models, MRE could significantly improve the predictive performance for MVI grade. Patients with predicted positive-MVI/severe-MVI based on the combined models had worse recurrence-free survival and overall survival than those with negative-MVI/mild-MVI, respectively.
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Affiliation(s)
- Lina Zhang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People's Republic of China
| | - Mengsi Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People's Republic of China
| | - Jie Zhu
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People's Republic of China
| | - Yao Zhang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People's Republic of China
| | - Yuanqiang Xiao
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People's Republic of China
| | - Mengshi Dong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People's Republic of China
| | - Linqi Zhang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People's Republic of China
- Department of Nuclear Medicine, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 78 Hengzhigang Rd, Guangzhou, Guangdong, 510095, People's Republic of China
| | - Jin Wang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No. 600, Tianhe Road, Guangzhou, Guangdong, 510630, People's Republic of China.
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Wang S, Zheng W, Zhang Z, Zhang GH, Huang DJ. Microvascular invasion risk scores affect the estimation of early recurrence after resection in patients with hepatocellular carcinoma: a retrospective study. BMC Med Imaging 2022; 22:204. [PMID: 36419016 PMCID: PMC9682687 DOI: 10.1186/s12880-022-00940-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 11/15/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is a histological factor that is closely related to the early recurrence of hepatocellular carcinoma (HCC) after resection. To investigate whether a noninvasive risk score system based on MVI status can be established to estimate early recurrence of HCC after resection. METHODS Between January 2018 to March 2021, a total of 108 patients with surgically treated single HCC was retrospectively included in our study. Fifty-one patients were pathologically confirmed with MVI and 57 patients were absent of MVI. Univariate and multivariate logistic regression analysis of preoperative laboratory and magnetic resonance imaging (MRI) features were used to screen noninvasive risk factors in association with MVI in HCC. Risk scores based on the odds ratio (OR) values of MVI-related risk factors were calculated to estimate the early recurrence after resection of HCC. RESULTS In multivariate logistic regression analysis, tumor size > 2 cm (P = 0.024, OR 3.05, 95% CI 1.19-11.13), Prothrombin induced by vitamin K absence-II > 32 mAU/ml (P = 0.001, OR 4.13, 95% CI 1.23-11.38), irregular tumor margin (P = 0.018, OR 3.10, 95% CI 1.16-8.31) and apparent diffusion coefficient value < 1007 × 10- 3mm2/s (P = 0.035, OR 2.27, 95% CI 1.14-7.71) were independent risk factors correlated to MVI in HCC. Risk scores of patients were calculated and were then categorized into high or low-risk levels. In multivariate cox survival analysis, only high-risk score of MVI was the independent risk factor of early recurrence (P = 0.009, OR 2.11, 95% CI 1.20-3.69), with a sensitivity and specificity of 0.52, 0.88, respectively. CONCLUSION A risk score system based on MVI status can help stratify patients in high-risk of early recurrence after resection of HCC.
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Affiliation(s)
- Sheng Wang
- grid.469601.cDepartment of Radiology, Taizhou First People’s Hospital, 218 Hengjie Rd., Dongcheng Street, Huangyan District, Taizhou City, 318020 Zhejiang Province China
| | - Weizhi Zheng
- grid.469601.cDepartment of Pathology, Taizhou First People’s Hospital, Taizhou City, 318020 Zhejiang Province China
| | - Zhencheng Zhang
- grid.469601.cDepartment of Laboratory, Taizhou First People’s Hospital, Taizhou City, 318020 Zhejiang Province China
| | - Guo-hua Zhang
- grid.469601.cDepartment of Radiology, Taizhou First People’s Hospital, 218 Hengjie Rd., Dongcheng Street, Huangyan District, Taizhou City, 318020 Zhejiang Province China
| | - Dan-jiang Huang
- grid.469601.cDepartment of Radiology, Taizhou First People’s Hospital, 218 Hengjie Rd., Dongcheng Street, Huangyan District, Taizhou City, 318020 Zhejiang Province China
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Qin YL, Wang S, Chen F, Liu HX, Yue KT, Wang XZ, Ning HF, Dong P, Yu XR, Wang GZ. Prediction of outcomes by diffusion kurtosis imaging in patients with large (≥5 cm) hepatocellular carcinoma after liver resection: A retrospective study. Front Oncol 2022; 12:939358. [DOI: 10.3389/fonc.2022.939358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/18/2022] [Indexed: 11/18/2022] Open
Abstract
PurposeTo evaluate preoperative diffusion kurtosis imaging (DKI) in predicting the outcomes of large hepatocellular carcinoma (HCC) after liver resection (LR).Materials and methodsFrom January 2015 to December 2017, patients with a large (≥5cm) HCC who underwent preoperative DKI were retrospectively reviewed. The correlations of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) with microvascular invasion (MVI) or histological grade were analyzed. Cox regression analyses were performed to identify the predictors of recurrence-free survival (RFS) and overall survival (OS). A nomogram to predict RFS was established. P<0.05 was considered as statistically significant.ResultsA total of 97 patients (59 males and 38 females, 56.0 ± 10.9 years) were included in this study. The MK, MD, and ADC values were correlated with MVI or histological grade (P<0.01). With a median follow-up time of 41.2 months (range 12-69 months), 67 patients (69.1%) experienced recurrence and 41 patients (42.3%) were still alive. The median RFS and OS periods after LR were 29 and 45 months, respectively. The 1-, 3-, and 5-year RFS and OS rates were 88.7%, 41.2%, and 21.7% and 99.0%, 68.3%, and 25.6%, respectively. MK (P<0.001), PVT (P<0.001), and ADC (P=0.033) were identified as independent predictor factors for RFS. A nomogram including the MK value for RFS showed the best performance, and the C-index was 0.895.ConclusionThe MK value obtained from DKI is a potential predictive factor for recurrence and poor survival, which could provide valuable information for guiding the efficacy of LR in patients with large HCC.
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TED: Two-stage expert-guided interpretable diagnosis framework for microvascular invasion in hepatocellular carcinoma. Med Image Anal 2022; 82:102575. [DOI: 10.1016/j.media.2022.102575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 07/08/2022] [Accepted: 08/11/2022] [Indexed: 12/16/2022]
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Contrast-enhanced magnetic resonance imaging perfusion can predict microvascular invasion in patients with hepatocellular carcinoma (between 1 and 5 cm). ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3264-3275. [PMID: 35113174 DOI: 10.1007/s00261-022-03423-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/12/2022] [Accepted: 01/12/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To evaluate the role of perfusion parameters with MR imaging of the liver in diagnosing MVI in hepatocellular carcinoma (HCC) (between 1 and 5 cm). MATERIALS AND METHODS This retrospective study was approved by the institutional review board. In 80 patients with 43 MVI( +) and 42 MVI( -) HCC, whole-liver perfusion MR imaging with Cartesian k-space undersampling and compressed sensing reconstruction was performed after injection of 0.1 mmol/kg gadopentetate dimeglumine. Parameters derived from a dual-input single-compartment model of arterial flow (Fa), portal venous flow (Fp), total blood flow (Ft = Fa + Fp), arterial fraction (ART), distribution volume (DV), and mean transit time (MTT) were measured. The significant parameters between the two groups were included to correlate with the presence of MVI at simple and multiple regression analysis. RESULTS In MVI-positive HCC, Fp was significantly higher than in MVI-negative HCC, whereas the reverse was seen for ART (p < 0.001). Tumor size (β = 1.2, p = 0.004; odds ratio, 3.20; 95% CI 1.45, 7.06), Fp (β = 1.1, p = 0.004; odds ratio, 3.09; 95% CI 1.42, 6.72), and ART (β = - 3.1, p = 0.001; odds ratio, 12.13; 95% CI 2.85, 51.49) were independent risk factors for MVI. The AUC value of the combination of all three metrics was 0.931 (95% CI 0.855, 0.975), with sensitivity of 97.6% and specificity of 76.2%. CONCLUSION The combination of Fp, ART, and tumor size demonstrated a higher diagnostic accuracy compared with each parameter used individually when evaluating MVI in HCC (between 1 and 5 cm).
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Deng Y, Li J, Xu H, Ren A, Wang Z, Yang D, Yang Z. Diagnostic Accuracy of the Apparent Diffusion Coefficient for Microvascular Invasion in Hepatocellular Carcinoma: A Meta-analysis. J Clin Transl Hepatol 2022; 10:642-650. [PMID: 36062283 PMCID: PMC9396311 DOI: 10.14218/jcth.2021.00254] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/13/2021] [Accepted: 10/27/2021] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND AIMS Microvascular invasion (MVI) is a major risk factor for the early recurrence of hepatocellular carcinoma (HCC) and it seriously worsens the prognosis. Accurate preoperative evaluation of the presence of MVI could greatly benefit the treatment management and prognosis prediction of HCC patients. The study aim was to evaluate the diagnostic performance of the apparent diffusion coefficient (ADC), a quantitative parameter for the preoperative diagnosis MVI in HCC patients. METHODS Original articles about diffusion-weighted imaging (DWI) and/or intravoxel incoherent motion (IVIM) conducted on a 3.0 or 1.5 Tesla magnetic resonance imaging (MRI) system indexed through January 17, 2021were collected from MEDLINE/PubMed, Web of Science, EMBASE, and the Cochrane Library. Methodological quality was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The pooled sensitivity, specificity, and summary area under the receiver operating characteristic curve (AUROC) were calculated, and meta-regression analysis was performed using a bivariate random effects model through a meta-analysis. RESULTS Nine original articles with a total of 988 HCCs were included. Most studies had low bias risk and minimal applicability concerns. The pooled sensitivity, specificity and AUROC of the ADC value were 73%, 70%, and 0.78, respectively. The time interval between the index test and the reference standard was identified as a possible source of heterogeneity by subgroup meta-regression analysis. CONCLUSIONS Meta-analysis showed that the ADC value had moderate accuracy for predicting MVI in HCC. The time interval accounted for the heterogeneity.
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Affiliation(s)
- Yuhui Deng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Medical Imaging Division, Heilongjiang Provincial Hospital, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Jisheng Li
- Department of Interventional Radiology, Yantai Penglai Traditional Chinese Medicine Hospital, Yantai, Shandong, China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ahong Ren
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Correspondence to: Dawei Yang and Zhenghan Yang, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing 100050, China. ORCID: https://orcid.org/0000-0002-1868-2746 (DY) and https://orcid.org/0000-0003-3986-1732 (ZY). Tel: +86-13488676354 (DY) and +86-13910831365 (ZY), Fax: +86-10-63138490, E-mail: (DY) and (ZY)
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Correspondence to: Dawei Yang and Zhenghan Yang, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing 100050, China. ORCID: https://orcid.org/0000-0002-1868-2746 (DY) and https://orcid.org/0000-0003-3986-1732 (ZY). Tel: +86-13488676354 (DY) and +86-13910831365 (ZY), Fax: +86-10-63138490, E-mail: (DY) and (ZY)
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Enhanced CT-based radiomics predicts pathological complete response after neoadjuvant chemotherapy for advanced adenocarcinoma of the esophagogastric junction: a two-center study. Insights Imaging 2022; 13:134. [PMID: 35976518 PMCID: PMC9385906 DOI: 10.1186/s13244-022-01273-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 07/20/2022] [Indexed: 01/19/2023] Open
Abstract
Purpose This study aimed to develop and validate CT-based models to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG). Methods Pre-NAC clinical and imaging data of AEG patients who underwent surgical resection after preoperative-NAC at two centers were retrospectively collected from November 2014 to September 2020. The dataset included training (n = 60) and external validation groups (n = 32). Three models, including CT-based radiomics, clinical and radiomics–clinical combined models, were established to differentiate pCR (tumor regression grade (TRG) = grade 0) and nonpCR (TRG = grade 1–3) patients. For the radiomics model, tumor-region-based radiomics features in the arterial and venous phases were extracted and selected. The naïve Bayes classifier was used to establish arterial- and venous-phase radiomics models. The selected candidate clinical factors were used to establish a clinical model, which was further incorporated into the radiomics–clinical combined model. ROC analysis, calibration and decision curves were used to assess the model performance. Results For the radiomics model, the AUC values obtained using the venous data were higher than those obtained using the arterial data (training: 0.751 vs. 0.736; validation: 0.768 vs. 0.750). Borrmann typing, tumor thickness and degree of differentiation were utilized to establish the clinical model (AUC-training: 0.753; AUC-validation: 0.848). The combination of arterial- and venous-phase radiomics and clinical factors further improved the discriminatory performance of the model (AUC-training: 0.838; AUC-validation: 0.902). The decision curve reflects the higher net benefit of the combined model. Conclusion The combination of CT imaging and clinical factors pre-NAC for advanced AEG could help stratify potential responsiveness to NAC. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01273-w.
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He Y, Chen H, Zhang H, Grimm R, Zhao C, Guo X, Liu Y, Yuan Z. Optimization of scan parameters to reduce acquisition time for RESOLVE-based diffusion kurtosis imaging (DKI) in nasopharyngeal carcinoma (NPC). Br J Radiol 2022; 95:20210641. [PMID: 35704453 PMCID: PMC10162055 DOI: 10.1259/bjr.20210641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 03/10/2022] [Accepted: 05/23/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To shorten acquisition time of readout segmentation of long variable echo trains (RESOLVE)-based diffusion kurtosis imaging (DKI) via Readout Partial Fourier (RPF) and b-value combinations. METHODS The RESOLVE-based DKI images of 38 patients with nasopharyngeal carcinoma (NPC) were prospectively enrolled. For RESOLVE-based DKI images with 5/8 RPF and without RPF, objective and subjective evaluations of image quality were performed. A total of nine groups with different b-value combinations were simulated, and the influence of different b-value combinations for RESOLVE-RPF-based DKI sequences was assessed using the intraclass correlation coefficient (ICC). RESULTS The mean values of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in DKI images without RPF were higher than those with 5/8 RPF (252.9 ± 77.7 vs 247.3 ± 85.5 and 5.8 ± 2.8 vs 5.4 ± 2.3, respectively), but not significantly (p = 0.460 and p = 0.180, respectively). In comparing the ICCs between nine groups of different b-value combinations in RESOLVE-RPF-based DKI, group (200, 800, 2000 s/mm2), group (200, 400, 800, 2000 s/mm2) and group (200, 800, 1500, 2000 s/mm2) were not significantly different (p > 0.001) and showed excellent agreement (0.81-1.00) with that of group (200, 400, 800, 1500, 2000 s/mm2). Using b-value optimization and RPF technology, the group with RPF (200, 400, 800, 2000 s/mm2) showed a 56% reduced scanning compared with the group without RPF (200, 400, 800, 1500, 2000 s/mm2; 3 min 46 s vs 8 min 31 s, respectively). CONCLUSION DKI with RPF did not significantly affect image quality, but both RPF and different b-value combinations can affect the scanning time. The combination of RPF and b-value optimization can ensure the stability of DKI parameters and reduce the scanning time by 56%. ADVANCES IN KNOWLEDGE This work is to optimize scan parameters, e.g. RPF and b-value combinations, to reduce acquisition time for RESOLVE-based DKI in NPC. To our knowledge, the effect of RESOLVE-RPF and b-value combinations on DKI has not been reported.
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Affiliation(s)
- Yaoyao He
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Hao Chen
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare Ltd., Wuhan, PR, China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Cecheng Zhao
- College of Informatics, Huazhong Agricultural University, Wuhan, PR China
| | - Xiaofang Guo
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
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Chu T, Zhao C, Zhang J, Duan K, Li M, Zhang T, Lv S, Liu H, Wei F. Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma. Ann Surg Oncol 2022; 29:6774-6783. [PMID: 35754067 PMCID: PMC9492610 DOI: 10.1245/s10434-022-12000-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
Abstract
Background Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer death worldwide, and the prognosis remains dismal. In this study, two pivotal factors, microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC) were preoperatively predicted simultaneously to assess prognosis. Methods A total of 133 HCC patients who underwent surgical resection and preoperative gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) were included. The statuses of MVI and VETC were obtained from the pathological report and CD34 immunohistochemistry, respectively. A three-dimensional convolutional neural network (3D CNN) for single-task learning aimed at MVI prediction and for multitask learning aimed at simultaneous prediction of MVI and VETC was established by using multiphase Gd-EOB-DTPA-enhanced MRI. Results The 3D CNN for single-task learning achieved an area under receiver operating characteristics curve (AUC) of 0.896 (95% CI: 0.797–0.994). Multitask learning with simultaneous extraction of MVI and VETC features improved the performance of MVI prediction, with an AUC value of 0.917 (95% CI: 0.825–1.000), and achieved an AUC value of 0.860 (95% CI: 0.728–0.993) for the VETC prediction. The multitask learning framework could stratify high- and low-risk groups regarding overall survival (p < 0.0001) and recurrence-free survival (p < 0.0001), revealing that patients with MVI+/VETC+ were associated with poor prognosis. Conclusions A deep learning framework based on 3D CNN for multitask learning to predict MVI and VETC simultaneously could improve the performance of MVI prediction while assessing the VETC status. This combined prediction can stratify prognosis and enable individualized prognostication in HCC patients before curative resection. Supplementary Information The online version contains supplementary material available at 10.1245/s10434-022-12000-6.
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Affiliation(s)
- Tongjia Chu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Chen Zhao
- College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China
| | - Jian Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Kehang Duan
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Tianqi Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Shengnan Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Huan Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Feng Wei
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China.
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Wang F, Yan CY, Wang CH, Yang Y, Zhang D. The Roles of Diffusion Kurtosis Imaging and Intravoxel Incoherent Motion Diffusion-Weighted Imaging Parameters in Preoperative Evaluation of Pathological Grades and Microvascular Invasion in Hepatocellular Carcinoma. Front Oncol 2022; 12:884854. [PMID: 35646649 PMCID: PMC9131658 DOI: 10.3389/fonc.2022.884854] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/31/2022] [Indexed: 12/14/2022] Open
Abstract
Background Currently, there are disputes about the parameters of diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and diffusion-weighted imaging (DWI) in predicting pathological grades and microvascular invasion (MVI) in hepatocellular carcinoma (HCC). The aim of our study was to investigate and compare the predictive power of DKI and IVIM-DWI parameters for preoperative evaluation of pathological grades and MVI in HCC. Methods PubMed, Web of Science, and Embase databases were searched for relevant studies published from inception to October 2021. Review Manager 5.3 was used to summarize standardized mean differences (SMDs) of mean kurtosis (MK), mean diffusivity (MD), tissue diffusivity (D), pseudo diffusivity (D*), perfusion fraction (f), mean apparent diffusion coefficient (ADCmean), and minimum apparent diffusion coefficient (ADCmin). Stata12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC). Overall, 42 up-to-standard studies with 3,807 cases of HCC were included in the meta-analysis. Results The SMDs of ADCmean, ADCmin, and D values, but not those of D* and f values, significantly differed between well, moderately, and poorly differentiated HCC (P < 0.01). The sensitivity, specificity, and AUC of the MK, D, ADCmean, and ADCmin for preoperative prediction of poorly differentiated HCC were 69%/94%/0.89, 87%/80%/0.89, 82%/75%/0.86, and 83%/64%/0.81, respectively. In addition, the sensitivity, specificity, and AUC of the D and ADCmean for preoperative prediction of well-differentiated HCC were 87%/83%/0.92 and 82%/88%/0.90, respectively. The SMDs of ADCmean, ADCmin, D, MD, and MK values, but not f values, showed significant differences (P < 0.01) between MVI-positive (MVI+) and MVI-negative (MVI-) HCC. The sensitivity and specificity of D and ADCmean for preoperative prediction of MVI+ were 80%/80% and 74%/71%, respectively; the AUC of the D (0.87) was significantly higher than that of ADCmean (0.78) (Z = −2.208, P = 0.027). Sensitivity analysis showed that the results of the above parameters were stable and reliable, and subgroup analysis confirmed a good prediction effect. Conclusion DKI parameters (MD and MK) and IVIM-DWI parameters (D value, ADCmean, and ADCmin) can be used as a noninvasive and simple preoperative examination method to predict the grade and MVI in HCC. Compared with ADCmean and ADCmin, MD and D values have higher diagnostic efficacy in predicting the grades of HCC, and D value has superior diagnostic efficacy to ADCmean in predicting MVI+ in HCC. However, f value cannot predict the grade or MVI in HCC.
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Affiliation(s)
- Fei Wang
- Department of Medical Imaging, Luzhou People's Hospital, Luzhou, China.,Department of Radiology, Xinqiao Hospital, Third Military Medical University, Chongqing, China
| | - Chun Yue Yan
- Department of Obstetrics, Luzhou People's Hospital, Luzhou, China
| | - Cai Hong Wang
- Department of Radiology, Xinqiao Hospital, Third Military Medical University, Chongqing, China
| | - Yan Yang
- Department of Radiology, Xinqiao Hospital, Third Military Medical University, Chongqing, China
| | - Dong Zhang
- Department of Radiology, Xinqiao Hospital, Third Military Medical University, Chongqing, China
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Zhong X, Long H, Su L, Zheng R, Wang W, Duan Y, Hu H, Lin M, Xie X. Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY) 2022; 47:2071-2088. [PMID: 35364684 DOI: 10.1007/s00261-022-03496-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models. RESULTS Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82-0.89), 0.87 (95% CI 0.83-0.92), and 0.74 (95% CI 0.67-0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region. CONCLUSION Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.
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Affiliation(s)
- Xian Zhong
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Haiyi Long
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Liya Su
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Ruiying Zheng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Yu Duan
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Hangtong Hu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Manxia Lin
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
| | - Xiaoyan Xie
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
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Xiao H, Guo Y, Zhou Q, Chen Q, Du Q, Chen S, Fu S, Lin J, Li D, Song X, Peng S, Huang Y, Shen J, Kuang M. Prediction of microvascular invasion in hepatocellular carcinoma with expert-inspiration and skeleton sharing deep learning. Liver Int 2022; 42:1423-1431. [PMID: 35319151 DOI: 10.1111/liv.15254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 02/28/2022] [Accepted: 03/13/2022] [Indexed: 01/15/2023]
Abstract
BACKGROUND AND AIMS Radiological prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is essential but few models were clinically implemented because of limited interpretability and generalizability. METHODS Based on 2096 patients in three independent HCC cohorts, we established and validated an MVI predicting model. First, we used data from the primary cohort to train a 3D-ResNet network for MVI prediction and then optimised the model with "expert-inspired training" for model construction. Second, we implemented the model to the other two cohorts using three implementation strategies, the original model implementation, data sharing model implementation and skeleton sharing model implementation, the latter two of which used part of the cohorts' data for fine-tuning. The areas under the receiver operating characteristic curve (AUCs) were calculated to compare the performances of different models. RESULTS For the MVI predicting model, the AUC of the expert-inspired model was 0.83 (95% CI: 0.77-0.88) compared to 0.54 (95% CI: 0.46-0.62) of model before expert-inspiring. Taking this model as an original model, AUC on the second cohort was 0.76 (95% CI: 0.67-0.84). The AUC was improved to 0.83 (95% CI: 0.77-0.90) with the data-sharing model, and further improved to 0.85 (95% CI: 0.79-0.92) with the skeleton sharing model. The trend that the skeleton sharing model had an advantage in performance was similar in the third cohort. CONCLUSIONS We established an expert-inspired model with better predictive performance and interpretability than the traditional constructed model. Skeleton sharing process is superior to data sharing and direct model implementation in model implementation.
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Affiliation(s)
- Han Xiao
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuchen Guo
- Institute for Brain and Cognitive Sciences, BRNist, Tsinghua University, Beijing, China
| | - Qian Zhou
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Qiaofeng Chen
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | | | - Shuling Chen
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shunjun Fu
- General Surgery Center, Department of Hepatobiliary Surgery II, Guangdong Provincial Research Center for Artificial Organ and Tissue Engineering, Guangzhou Clinical Research and Transformation Center for Artificial Liver, Institute of Regenerative Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China
| | - Jie Lin
- Department of Liver and Pancreatobiliary Surgery, Shunde Hospital of Southern Medical University, Shunde, Guangdong, China
| | | | - Xinming Song
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Sui Peng
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.,Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuhua Huang
- Department of Pathology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Jingxian Shen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Ming Kuang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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Jiang H, Wei J, Fu F, Wei H, Qin Y, Duan T, Chen W, Xie K, Lee JM, Bashir MR, Wang M, Song B, Tian J. Predicting microvascular invasion in hepatocellular carcinoma: A dual-institution study on gadoxetate disodium-enhanced MRI. Liver Int 2022; 42:1158-1172. [PMID: 35243749 PMCID: PMC9314889 DOI: 10.1111/liv.15231] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 12/06/2021] [Accepted: 12/14/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND & AIMS Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but its diagnosis mandates postoperative histopathologic analysis. We aimed to develop and externally validate a predictive scoring system for MVI. METHODS From July 2015 to November 2020, consecutive patients underwent surgery for HCC with preoperative gadoxetate disodium (EOB)-enhanced MRI was retrospectively enrolled. All MR images were reviewed independently by two radiologists who were blinded to the outcomes. In the training centre, a radio-clinical MVI score was developed via logistic regression analysis against pathology. In the testing centre, areas under the receiver operating curve (AUCs) of the MVI score and other previous MVI schemes were compared. Overall survival (OS) and recurrence-free survival (RFS) were analysed by the Kaplan-Meier method with the log-rank test. RESULTS A total of 417 patients were included, 195 (47%) with pathologically-confirmed MVI. The MVI score included: non-smooth tumour margin (odds ratio [OR] = 4.4), marked diffusion restriction (OR = 3.0), internal artery (OR = 3.0), hepatobiliary phase peritumoral hypointensity (OR = 2.5), tumour multifocality (OR = 1.6), and serum alpha-fetoprotein >400 ng/mL (OR = 2.5). AUCs for the MVI score were 0.879 (training) and 0.800 (testing), significantly higher than those for other MVI schemes (testing AUCs: 0.648-0.684). Patients with model-predicted MVI had significantly shorter OS (median 61.0 months vs not reached, P < .001) and RFS (median 13.0 months vs. 42.0 months, P < .001) than those without. CONCLUSIONS A preoperative MVI score integrating five EOB-MRI features and serum alpha-fetoprotein level could accurately predict MVI and postoperative survival in HCC. Therefore, this score may aid in individualized treatment decision making.
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Affiliation(s)
- Hanyu Jiang
- Department of Radiology, West China HospitalSichuan UniversityChengduSichuanChina
| | - Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina,Beijing Key Laboratory of Molecular ImagingBeijingChina
| | - Fangfang Fu
- Department of Medical ImagingHenan Provincial People’s HospitalZhengzhouChina,Department of Medical ImagingPeople’s Hospital of Zhengzhou UniversityZhengzhouChina
| | - Hong Wei
- Department of Radiology, West China HospitalSichuan UniversityChengduSichuanChina
| | - Yun Qin
- Department of Radiology, West China HospitalSichuan UniversityChengduSichuanChina
| | - Ting Duan
- Department of Radiology, West China HospitalSichuan UniversityChengduSichuanChina
| | - Weixia Chen
- Department of Radiology, West China HospitalSichuan UniversityChengduSichuanChina
| | - Kunlin Xie
- Department of Liver Surgery & Liver Transplantation, West China HospitalSichuan UniversityChengduChina
| | - Jeong Min Lee
- Department of RadiologySeoul National University Hospital and Seoul National University College of MedicineSeoulSouth Korea
| | - Mustafa R. Bashir
- Department of RadiologyDuke University Medical CenterDurhamNorth CarolinaUSA,Center for Advanced Magnetic Resonance in MedicineDuke University Medical CenterDurhamNorth CarolinaUSA,Division of Gastroenterology, Department of MedicineDuke University Medical CenterDurhamNorth CarolinaUSA
| | - Meiyun Wang
- Department of Medical ImagingHenan Provincial People’s HospitalZhengzhouChina,Department of Medical ImagingPeople’s Hospital of Zhengzhou UniversityZhengzhouChina
| | - Bin Song
- Department of Radiology, West China HospitalSichuan UniversityChengduSichuanChina
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina,Beijing Key Laboratory of Molecular ImagingBeijingChina,Beijing Advanced Innovation Center for Big Data‐Based Precision Medicine, School of MedicineBeihang UniversityBeijingChina,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and TechnologyXidian UniversityXi’anChina,Key Laboratory of Big Data‐Based Precision Medicine (Beihang University)Ministry of Industry and Information TechnologyBeijingChina
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Li Q, Cao B, Liu K, Sun H, Ding Y, Yan C, Wu PY, Dai C, Rao S, Zeng M, Jiang S, Zhou J. Detecting the muscle invasiveness of bladder cancer: an application of diffusion kurtosis imaging and tumor contact length. Eur J Radiol 2022; 151:110329. [DOI: 10.1016/j.ejrad.2022.110329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/17/2022] [Accepted: 04/19/2022] [Indexed: 11/03/2022]
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Tang Y, Xu L, Ren Y, Li Y, Yuan F, Cao M, Zhang Y, Deng M, Yao Z. Identification and Validation of a Prognostic Model Based on Three MVI-Related Genes in Hepatocellular Carcinoma. Int J Biol Sci 2022; 18:261-275. [PMID: 34975331 PMCID: PMC8692135 DOI: 10.7150/ijbs.66536] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/20/2021] [Indexed: 12/13/2022] Open
Abstract
MVI has significant clinical value for treatment selection and prognosis evaluation in hepatocellular carcinoma (HCC). We aimed to construct a model based on MVI-Related Genes (MVIRGs) for risk assessment and prognosis prediction in patients with HCC. This study utilized various statistical analysis methods for prognostic model construction and validation in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts, respectively. In addition, immunohistochemistry and qRT-PCR were used to analyze and identify the value of the model in our cohort. After the analyses, 153 differentially expressed MVIRGs were identified, and three key genes were selected to construct a prognostic model. The high-risk group showed significantly lower overall survival (OS), and this trend was observed in all subgroups: different age groups, genders, stages, and grades. Risk score was a risk factor independent of age, gender, stage, and grade. Moreover, the ICGC cohort validated the prognostic value of the model corresponding to the TCGA. In our cohort, qRT-PCR and immunohistochemistry showed that all three genes had higher expression levels in HCC samples than in normal controls. High expression levels of genes and high-risk scores showed significantly lower recurrence-free survival (RFS) and OS, especially in MVI-positive HCC samples. Therefore, the prognostic model constructed by three MVIRGs can reliably predict the RFS and OS of patients with HCC and is valuable for guiding clinical treatment selection and prognostic assessment of HCC.
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Affiliation(s)
- Yongchang Tang
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Lei Xu
- Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518107, China.,Department of Nuclear Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Yupeng Ren
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Yuxuan Li
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Feng Yuan
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Mingbo Cao
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Yong Zhang
- Department of Nuclear Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Meihai Deng
- Department of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
| | - Zhicheng Yao
- Department of General Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China
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Wei M, Lin M, Zhong X, Dai Z, Shen S, Li S, Peng Z, Kuang M. Role of Preoperational Imaging Traits for Guiding Treatment in Single ≤ 5 cm Hepatocellular Carcinoma. Ann Surg Oncol 2022; 29:5144-5153. [DOI: 10.1245/s10434-022-11344-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/03/2022] [Indexed: 12/12/2022]
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Chen W, Wei Q, Huang W, Chen J, Hu S, Lv X, Mao L, Liu B, Zhou W, Liu X. Combining diffusion kurtosis imaging and clinical data for predicting the extramural venous invasion of rectal adenocarcinoma. Eur J Radiol 2022; 148:110155. [DOI: 10.1016/j.ejrad.2022.110155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 12/23/2021] [Accepted: 01/06/2022] [Indexed: 11/28/2022]
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