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Yan W, Yu H, Xu C, Zeng M, Wang M. The value of a nomogram model based on CT imaging features in differentiating duodenal gastrointestinal stromal tumors from pancreatic head neuroendocrine tumors. Abdom Radiol (NY) 2025; 50:1330-1341. [PMID: 39302444 DOI: 10.1007/s00261-024-04579-z] [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: 07/09/2024] [Revised: 09/07/2024] [Accepted: 09/09/2024] [Indexed: 09/22/2024]
Abstract
OBJECTIVE To construct a nomogram model based on multi-slice spiral CT imaging features to predict and differentiate between duodenal gastrointestinal stromal tumors (GISTs) and pancreatic head neuroendocrine tumors (NENs), providing imaging evidence for clinical treatment decisions. METHODS A retrospective collection of clinical information, pathological results, and imaging data was conducted on 115 cases of duodenal GISTs and 76 cases of pancreatic head NENs confirmed by surgical pathology at Zhongshan Hospital Fudan University from November 2013 to November 2022. Comparative analysis was performed on the tumor's maximum diameter, shortest diameter, long diameter/short diameter ratio, tumor morphology, tumor border, central position of the lesion, lesion long-axis direction, the relationship between tumor and common bile duct (CBD), duodenal side ulceration of the lesion, calcification, cystic and solid proportion within the tumor, thickened feeding arteries, tumor neovascularization, distant metastasis, and CT values during plain and enhanced scans in arterial and venous phases. Statistical analysis was conducted using t-tests, Mann-Whitney U tests, and χ2 tests. Univariate and multivariate logistic regression analyses were used to identify independent predictors for differentiating duodenal GISTs from pancreatic head NENs. Based on these independent predictors, a nomogram model was constructed, and the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. The nomogram was validated using a calibration curve, and decision curve analysis was applied to assess the clinical application value of the nomogram. RESULTS There were significant differences in the duodenal GISTs group and the pancreatic head NENs group in terms of longest diameter (P < 0.001), shortest diameter (P < 0.001), plain CT value (P < 0.001), arterial phase CT value (P < 0.001), venous phase CT value (P = 0.002), lesion long-axis direction (P < 0.001), central position of the lesion (P < 0.001), the relationship between tumor and CBD(< 0.001), border (P = 0.004), calcification (P = 0.017), and distant metastasis (P = 0.018). Multivariate logistic regression analysis identified uncertain location (OR 0.040, 95% CI 0.003-0.549), near the duodenum (OR 0, 95% CI 0-0.009), with the lesion long-axis direction along the pancreas as a reference, along the duodenum (OR 0.106, 95% CI 0.010-1.156) or no significant difference (OR 4.946, 95% CI 0.453-54.017), and the relationship between tumor and CBD (OR 0.013, 95% CI 0.001-0.180), shortest diameter (OR 0.705, 95% CI 0.546-0.909), and calcification (OR 18.638, 95% CI 1.316-263.878) as independent risk factors for differentiating between duodenal GISTs and pancreatic head NENs (all P values < 0.05). The combined diagnostic model's AUC values based on central position of the lesion, calcification, lesion long axis orientation, the relationship between tumor and CBD, shortest diameter, and the joint diagnostic model were 0.937 (0.902-0.972), 0.700(0.624-0.776), 0.717(0.631-0.802), 0.559 (0.473-0.644), 0.680 (0.603-0.758), and 0.991(0.982-0.999), respectively, with a sensitivity of 97.3% and a specificity of 93.0% for the joint diagnostic model. The nomogram model's AUC value was 0.985(0.973-0.996), with a sensitivity and specificity of 94.7% and 93.9%, respectively. The calibration curve indicated good agreement between predicted and actual risks. Decision curve analysis verified the clinical application value of the nomogram. CONCLUSION The nomogram model based on CT imaging features effectively differentiates between duodenal GISTs and pancreatic head NENs, aiding in more precise clinical treatment decisions.
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Affiliation(s)
- Wenjie Yan
- The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
- Zhongshan Hospital, Fudan University, Shanghai, China
| | - Haiyan Yu
- Weifang People's Hospital, Weifang, China
| | - Chuanfang Xu
- The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Mengshu Zeng
- Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Geriatric Medical Center, Shanghai, China
| | - Mingliang Wang
- Zhongshan Hospital, Fudan University, Shanghai, China.
- Shanghai Geriatric Medical Center, Shanghai, China.
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Liu G, Gao YJ, Li XB, Huan Y, Chen J, Deng YM. Quantitative evaluation of pancreatic neuroendocrine tumors utilizing dual-source CT perfusion imaging. BMC Med Imaging 2024; 24:325. [PMID: 39623298 PMCID: PMC11613872 DOI: 10.1186/s12880-024-01511-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 11/21/2024] [Indexed: 12/06/2024] Open
Abstract
OBJECTIVE We aimed to quantitatively analyze the perfusion characteristics of pancreatic neuroendocrine tumors (pNETs) utilizing dual-source CT imaging. METHODS Dual-source CT perfusion scans were obtained from patients with pNETs confirmed by surgical or biopsy pathology. Perfusion parameters, including blood flow (BF), blood volume (BV), capillary permeability surface (PS), mean transit time (MTT), contrast transit time to the start (TTS), and contrast transit time to the peak (TTP), were statistically analyzed and compared with nearby healthy tissue. Time density curves (TDCs) were plotted to further understand the dynamic enhancement characteristics of the tumors. Additionally, receiver operating characteristic curves (ROCs) were generated to assess their diagnostic value. RESULTS Twenty patients with pNETs, containing 26 lesions, were enrolled in the study, including 6 males with 8 lesions and 14 females with 18 lesions. The average values of BF, BV, PS, MTT, TTP and TTS for the 26 lesions (336.61 ± 216.72 mL/100mL/min, 41.96 ± 16.99 mL/100mL, 32.90 ± 11.91 mL/100 mL/min, 9.44 ± 4.40 s, 19.14 ± 5.6 s, 2.57 ± 1.6 s) were different from those of the adjacent normal pancreatic tissue (44.32 ± 55.35 mL/100mL/min, 28.64 ± 7.95 mL/100mL, 26.69 ± 14.88 mL/100 mL/min, 12.89 ± 3.69 s, 20.33 ± 5.18 s, 2.69 ± 1.71 s). However, there were no statistical differences in PS and TTS between the lesions and the adjacent normal pancreatic tissue (P > 0.05). The areas under the ROC curve for BF, BV, and PS were all greater than 0.5, whereas the areas under the ROC curve for MTT, TTP, and TTS were all less than 0.5. CONCLUSION CT perfusion parameters such as BF, BV, MTT, and TTP can distinguish pNETs from healthy tissue. The area under the ROC curve for BF, BV, and PS demonstrates substantial differentiating power for diagnosing pNET lesions.
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Affiliation(s)
- Ge Liu
- Department of Radiology, Xi'an No. 3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, Shaanxi, 710018, China
| | - Yan-Jun Gao
- Department of Radiology, Xi'an No. 3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, Shaanxi, 710018, China
| | - Xiao-Bing Li
- Department of Peripheral Vascular Medicine, Xi'an Honghui Hospital, Xi'an, Shaanxi, 710018, China
| | - Yi Huan
- Department of Radiology, The First Hospital of Air Force Medical University, Xi'an, Shaanxi, 710032, China
| | - Jian Chen
- Department of Peripheral Vascular Medicine, Xi'an Honghui Hospital, Xi'an, Shaanxi, 710018, China
| | - Yan-Meng Deng
- Center of Radiology, Shaanxi Traditional Chinese Medicine Hospital, Xi'an, Shaanxi, 710003, China.
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Horng A, Ingenerf M, Berger F, Steffinger D, Rübenthaler J, Zacherl M, Wenter V, Ricke J, Schmid-Tannwald C. Synchronous neuroendocine liver metastases in comparison to primary pancreatic neuroendocrine tumors on MRI and SSR-PET/CT. Front Oncol 2024; 14:1352538. [PMID: 38884077 PMCID: PMC11179428 DOI: 10.3389/fonc.2024.1352538] [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: 12/08/2023] [Accepted: 05/17/2024] [Indexed: 06/18/2024] Open
Abstract
Background The study aimed to compare and correlate morphological and functional parameters in pancreatic neuroendocrine tumors (pNET) and their synchronous liver metastases (NELM), while also assessing prognostic imaging parameters. Methods Patients with G1/G2 pNET and synchronous NELM underwent pretherapeutic abdominal MRI with DWI and 68Ga-DOTATATE/TOC PET/CT were included. ADC (mean, min), SNR_art and SNT_T2 (SNR on arterial phase and on T2) and SUV (max, mean) for three target NELM and pNET, as well as tumor-free liver and spleen (only in PET/CT) were measured. Morphological parameters including size, location, arterial enhancement, cystic components, T2-hyperintensity, ductal dilatation, pancreatic atrophy, and vessel involvement were noted. Response evaluation used progression-free survival (PFS) with responders (R;PFS>24 months) and non-responders (NR;PFS ≤ 24 months). Results 33 patients with 33 pNETs and 95 target NELM were included. There were no significant differences in ADC and SUV values between NELM and pNET. 70% of NELM were categorized as hyperenhancing lesions, whereas the pNETs exhibited significantly lower rate (51%) of hyperenhancement (p<0.01) and significant lower SNR_art. NELM were qualitatively and quantitatively (SNR_T2) significantly more hyperintense on T2 compared to pNET (p=0.01 and p<0.001). NELM of R displayed significantly lower ADCmean value in comparison to the ADC mean value of pNET (0.898 versus 1.037x10-3mm²/s,p=0.036). In NR, T2-hyperintensity was notably higher in NELM compared to pNET (p=0.017). The hepatic tumor burden was significantly lower in the R compared to the NR (10% versus 30%). Conclusions Arterial hyperenhancement and T2-hyperintensity differ between synchronous NELM and pNET. These findings emphasize the importance of a multifaceted approach to imaging and treatment planning in patients with these tumors as well as in predicting treatment responses.
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Affiliation(s)
- Annie Horng
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Maria Ingenerf
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Frank Berger
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Denise Steffinger
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Johannes Rübenthaler
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Matthias Zacherl
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Vera Wenter
- Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
- European Neuroendocrine Tumor Society (ENETS) Centre of Excellence, Interdisciplinary Center of Neuroendocrine Tumours of the GastroEnteroPancreatic System at the University Hospital of Munich (GEPNET-KUM), University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Christine Schmid-Tannwald
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
- European Neuroendocrine Tumor Society (ENETS) Centre of Excellence, Interdisciplinary Center of Neuroendocrine Tumours of the GastroEnteroPancreatic System at the University Hospital of Munich (GEPNET-KUM), University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
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Ren S, Qian LC, Cao YY, Daniels MJ, Song LN, Tian Y, Wang ZQ. Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma. World J Gastrointest Oncol 2024; 16:1256-1267. [PMID: 38660647 PMCID: PMC11037050 DOI: 10.4251/wjgo.v16.i4.1256] [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: 11/05/2023] [Revised: 12/27/2023] [Accepted: 02/01/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma (PDAC) is that most patients are usually diagnosed at late stages. There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages. AIM To evaluate the potential value of radiomics analysis in the differentiation of early-stage PDAC from late-stage PDAC. METHODS A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography (CT) within 30 d prior to surgery were included in the study. Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system. Radiomics features were extracted from the region of interest (ROI) for each patient using Analysis Kit software. The most important and predictive radiomics features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy maximum relevance (MRMR) method. Random forest (RF) method was used to construct the radiomics model, and 10-times leave group out cross-validation (LGOCV) method was used to validate the robustness and reproducibility of the model. RESULTS A total of 792 radiomics features (396 from late arterial phase and 396 from portal venous phase) were extracted from the ROI for each patient using Analysis Kit software. Nine most important and predictive features were selected using Mann-Whitney U test, univariate logistic regression analysis, and MRMR method. RF method was used to construct the radiomics model with the nine most predictive radiomics features, which showed a high discriminative ability with 97.7% accuracy, 97.6% sensitivity, 97.8% specificity, 98.4% positive predictive value, and 96.8% negative predictive value. The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models. CONCLUSION The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Li-Chao Qian
- Department of Geratology, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing 210022, Jiangsu Province, China
| | - Ying-Ying Cao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Marcus J Daniels
- Department of Radiology, NYU Langone Health, New York, NY 10016, United States
| | - Li-Na Song
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Ying Tian
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Zhong-Qiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
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Feng N, Chen HY, Lu YF, Pan Y, Yu JN, Wang XB, Deng XY, Yu RS. Duodenal neuroendocrine neoplasms on enhanced CT: establishing a diagnostic model with duodenal gastrointestinal stromal tumors in the non-ampullary area and analyzing the value of predicting prognosis. J Cancer Res Clin Oncol 2023; 149:15143-15157. [PMID: 37634206 PMCID: PMC10602948 DOI: 10.1007/s00432-023-05295-9] [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: 07/26/2023] [Accepted: 08/14/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE To identify CT features and establish a diagnostic model for distinguishing non-ampullary duodenal neuroendocrine neoplasms (dNENs) from non-ampullary duodenal gastrointestinal stromal tumors (dGISTs) and to analyze overall survival outcomes of all dNENs patients. MATERIALS AND METHODS This retrospective study included 98 patients with pathologically confirmed dNENs (n = 44) and dGISTs (n = 54). Clinical data and CT characteristics were collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model between non-ampullary dNENs (n = 22) and dGISTs (n = 54). The ROC curve was created to determine diagnostic ability. Cox proportional hazards models were created and Kaplan-Meier survival analyses were performed for survival analysis of dNENs (n = 44). RESULTS Three CT features were identified as independent predictors of non-ampullary dNENs, including intraluminal growth pattern (OR 0.450; 95% CI 0.206-0.983), absence of intratumoral vessels (OR 0.207; 95% CI 0.053-0.807) and unenhanced lesion > 40.76 HU (OR 5.720; 95% CI 1.575-20.774). The AUC was 0.866 (95% CI 0.765-0.968), with a sensitivity of 90.91% (95% CI 70.8-98.9%), specificity of 77.78% (95% CI 64.4-88.0%), and total accuracy rate of 81.58%. Lymph node metastases (HR: 21.60), obstructive biliary and/or pancreatic duct dilation (HR: 5.82) and portal lesion enhancement ≤ 99.79 HU (HR: 3.02) were independent prognostic factors related to poor outcomes. CONCLUSION We established a diagnostic model to differentiate non-ampullary dNENs from dGISTs. Besides, we found that imaging features on enhanced CT can predict OS of patients with dNENs.
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Affiliation(s)
- Na Feng
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hai-Yan Chen
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Yuan-Fei Lu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Pan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie-Ni Yu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xin-Bin Wang
- Department of Radiology, The First People's Hospital of Xiaoshan District, 199 Shixinnan Road, Hangzhou, China
| | - Xue-Ying Deng
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
| | - Ri-Sheng Yu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Karmazanovsky GG, Abuladze LR. Computer-assisted and magnetic resonance imaging assessment of tumors and tumor invasion of the duodenum. ANNALY KHIRURGICHESKOY GEPATOLOGII = ANNALS OF HPB SURGERY 2022; 27:12-21. [DOI: 10.16931/1995-5464.2022-1-12-21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2025]
Abstract
Aim: To present the computed tomography and magnetic resonance imaging assessments of benign and malignant duodenal tumors, pancreatic head adenocarcinoma invading the duodenum, and duodenal dystrophy.Methods: We searched for scientific papers and clinical guidelines in the information and analytical databases PubMed and Google Scholar from the 2013–2021 period using the following search terms: duodenal neoplasms, adenocarcinoma, duodenum, duodenal neuroendocrine tumors, duodenal adenoma, gastrointestinal stromal tumor, cholangiocarcinoma, radiology, magnetic resonance imaging, computed tomography, pancreatic head cancer. Then, we examined the reference lists of all the identified studies to collate the papers that would meet the eligibility criteria.Results: We analyzed 1494 articles, 22 of which were included in our review. From the papers published within 1992–2021, 35 articles from the reference lists were additionally included. Based on the search results, several domains of articles were clustered; the articles from those domains were reviewed and evaluated that involved the abovementioned diagnostic features.Conclusion: The early diagnosis and selection of appropriate management methods remain extremely relevant for the treatment of duodenal tumors, and hence, require careful attention from diagnosticians and clinicians.
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Affiliation(s)
- G. G. Karmazanovsky
- A.V. Vishnevsky National Medical Research Center of Surgery of the Ministry of Healthcare of the Russian Federation; N.I. Pirogov Russian National Research Medical University of the Ministry of Healthcare of the Russian
Federation
| | - L. R. Abuladze
- A.V. Vishnevsky National Medical Research Center of Surgery of the Ministry of Healthcare of the Russian Federation; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow
Healthcare Department
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Assessment of morphological CT imaging features for the prediction of risk stratification, mutations, and prognosis of gastrointestinal stromal tumors. Eur Radiol 2021; 31:8554-8564. [PMID: 33881567 DOI: 10.1007/s00330-021-07961-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 03/08/2021] [Accepted: 03/29/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To investigate the correlation between CT imaging features and risk stratification of gastrointestinal stromal tumors (GISTs), prediction of mutation status, and prognosis. METHODS This retrospective dual-institution study included patients with pathologically proven GISTs meeting the following criteria: (i) preoperative contrast-enhanced CT performed between 2008 and 2019; (ii) no treatments before imaging; (iii) available pathological analysis. Tumor risk stratification was determined according to the National Institutes of Health (NIH) 2008 criteria. Two readers evaluated the CT features, including enhancement patterns and tumor characteristics in a blinded fashion. The differences in distribution of CT features were assessed using univariate and multivariate analyses. Survival analyses were performed by using the Cox proportional hazard model, Kaplan-Meier method, and log-rank test. RESULTS The final population included 88 patients (59 men and 29 women, mean age 60.5 ± 11.1 years) with 45 high-risk and 43 low-to-intermediate-risk GISTs (median size 6.3 cm). At multivariate analysis, lesion size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) were independently associated with the high-risk GISTs. Hyperenhancement was significantly more frequent in PDGFRα-mutated/wild-type GISTs compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). Ill-defined margins were associated with shorter progression-free survival (HR 9.66) at multivariate analysis, while ill-defined margins and hemorrhage remained independently associated with shorter overall survival (HR 44.41 and HR 30.22). Inter-reader agreement ranged from fair to almost perfect (k: 0.32-0.93). CONCLUSIONS Morphologic contrast-enhanced CT features are significantly different depending on the risk status or mutations and may help to predict prognosis. KEY POINTS • Lesions size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) are independent predictors of high-risk GISTs. • PDGFRα-mutated/wild-type GISTs demonstrate more frequently hyperenhancement compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). • Ill-defined margins (hazard ratio 9.66) were associated with shorter progression-free survival at multivariate analysis, while ill-defined margins (hazard ratio 44.41) and intralesional hemorrhage (hazard ratio 30.22) were independently associated with shorter overall survival.
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Cannella R, La Grutta L, Midiri M, Bartolotta TV. New advances in radiomics of gastrointestinal stromal tumors. World J Gastroenterol 2020; 26:4729-4738. [PMID: 32921953 PMCID: PMC7459199 DOI: 10.3748/wjg.v26.i32.4729] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/16/2020] [Accepted: 08/01/2020] [Indexed: 02/06/2023] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are uncommon neoplasms of the gastrointestinal tract with peculiar clinical, genetic, and imaging characteristics. Preoperative knowledge of risk stratification and mutational status is crucial to guide the appropriate patients’ treatment. Predicting the clinical behavior and biological aggressiveness of GISTs based on conventional computed tomography (CT) and magnetic resonance imaging (MRI) evaluation is challenging, unless the lesions have already metastasized at the time of diagnosis. Radiomics is emerging as a promising tool for the quantification of lesion heterogeneity on radiological images, extracting additional data that cannot be assessed by visual analysis. Radiomics applications have been explored for the differential diagnosis of GISTs from other gastrointestinal neoplasms, risk stratification and prediction of prognosis after surgical resection, and evaluation of mutational status in GISTs. The published researches on GISTs radiomics have obtained excellent performance of derived radiomics models on CT and MRI. However, lack of standardization and differences in study methodology challenge the application of radiomics in clinical practice. The purpose of this review is to describe the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors, discuss the potential clinical applications that may impact patients’ management, report limitations of current radiomics studies, and future directions.
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Affiliation(s)
- Roberto Cannella
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Ludovico La Grutta
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Massimo Midiri
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Tommaso Vincenzo Bartolotta
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
- Department of Radiology, Fondazione Istituto Giuseppe Giglio, Ct.da Pietrapollastra, Cefalù (Palermo) 90015, Italy
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Chu LC, Park S, Kawamoto S, Yuille AL, Hruban RH, Fishman EK. Pancreatic Cancer Imaging: A New Look at an Old Problem. Curr Probl Diagn Radiol 2020; 50:540-550. [PMID: 32988674 DOI: 10.1067/j.cpradiol.2020.08.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/21/2020] [Indexed: 12/18/2022]
Abstract
Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.
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Affiliation(s)
- Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Seyoun Park
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alan L Yuille
- Department of Computer Science, Johns Hopkins University, Baltimore, MD
| | - Ralph H Hruban
- Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
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Guo CG, Ren S, Chen X, Wang QD, Xiao WB, Zhang JF, Duan SF, Wang ZQ. Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance. Cancer Manag Res 2019; 11:1933-1944. [PMID: 30881119 PMCID: PMC6407516 DOI: 10.2147/cmar.s195376] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance. Patients and methods PNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher’s exact tests or Kruskal–Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades. Results There were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703–0.989. Conclusion Our data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.
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Affiliation(s)
- Chuan-Gen Guo
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Shuai Ren
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
| | - Xiao Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
| | - Qi-Dong Wang
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Wen-Bo Xiao
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Jing-Feng Zhang
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | | | - Zhong-Qiu Wang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
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Ren S, Chen X, Cui W, Chen R, Guo K, Zhang H, Chen S, Wang Z. Differentiation of chronic mass-forming pancreatitis from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography. Cancer Manag Res 2019; 11:7857-7866. [PMID: 31686905 PMCID: PMC6709381 DOI: 10.2147/cmar.s217033] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 08/05/2019] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Both chronic mass-forming pancreatitis (CMFP) and pancreatic ductal adenocarcinoma (PDAC) are focal pancreatic lesions and share very similar clinical symptoms and imaging performance. There is great clinical value in preoperative differentiation of those two lesions. The purpose of this study was to investigate the value of computed tomography (CT) features in discriminating CMFP from PDAC. PATIENTS AND METHODS Forty-seven patients with pathologically confirmed PDAC and 21 patients with CMFP were included in this study. Demographic and CT features, including tumor location, size, margin, pancreatic or bile duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, and tumor contrast enhancement, were retrospectively analyzed and compared. Multivariate logistic regression analyses were adopted to identify relevant CT imaging features to discriminate CMFP from PDAC. RESULTS There were significant differences between CMFP and PDAC with respect to main pancreatic duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, and tumor contrast enhancement. Delayed contrast enhancement (>70.5 Hounsfield units) showed high sensitivity and specificity of 84.2% and 84.7%. The areas under the curve (AUCs) of the predicting models based on qualitative and quantitative variables were 0.770 (95% CI: 0.660-0.880) and 0.943 (95% CI: 0.888-0.999), respectively. When all significant variables were used in combination to build a predicting model, the AUC was 0.969 (95% CI: 0.930-1.000) with 84.2% sensitivity and 94.7% specificity. CONCLUSION Main pancreatic duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, tumor size, and tumor contrast enhancement were shown to be useful CT imaging features in discriminating CMFP from PDAC.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province210029, People’s Republic of China
| | - Xiao Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province210029, People’s Republic of China
| | - Wenjing Cui
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province210029, People’s Republic of China
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD21201, USA
| | - Kai Guo
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province210029, People’s Republic of China
| | - Huifeng Zhang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province210029, People’s Republic of China
| | - Shuai Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province210029, People’s Republic of China
| | - Zhongqiu Wang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province210029, People’s Republic of China
- Correspondence: Zhongqiu WangDepartment of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, Jiangsu Province210029, People’s Republic of ChinaTel +86 258 086 1278Email
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