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Ruan J, He Y, Li Q, Song M, Jiang Z, Mao K, Ai J, Yang R, Yang G, Li P, Gao D, Li Z. CT feature of irregular extensive ulceration as a predictor of liver metastasis in gastric gastrointestinal stromal tumours. Eur Radiol 2025; 35:2759-2768. [PMID: 39500800 DOI: 10.1007/s00330-024-11177-6] [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: 02/17/2024] [Revised: 08/09/2024] [Accepted: 10/03/2024] [Indexed: 04/25/2025]
Abstract
OBJECTIVES This study aimed to investigate whether the computed tomography (CT) finding of irregular extensive ulceration (IEU) can serve as a predictor of liver metastasis (LIM) in patients with gastric gastrointestinal stromal tumours (GISTs). METHODS This study retrospectively collected clinical and imaging data from 286 patients diagnosed with low-, intermediate-, or high-risk gastric GISTs, or primary lesions with LIM from three medical institutions. The patients were categorised into non-LIM and LIM groups according to whether they had synchronous or metachronous LIM. Multivariate logistic regression analyses were performed to identify significant predictors of LIM. Additionally, receiver operating characteristic (ROC) curve, subgroup, and pathologic-radiologic correlation analyses were conducted. RESULTS A total of 124 patients were ultimately enroled. There were significant differences in sex, site, growth pattern, size, shape, ulceration and Ki-67 expression between LIM and non-LIM groups. ROC curve analysis demonstrated that IEU had the highest area under the curve for predicting LIM (AUC = 0.842; 95% CI: 0.760-0.924; p < 0.001). Multivariate analysis indicated that IEU was the most significant independent predictor of high LIM risk (OR = 88.62; 95% CI: 2.80-2803.54; p = 0.011). Subgroup analysis showed that IEU was more frequently associated with male sex, age ≤ 55 years, proximal sites, irregular shapes, mixed growth patterns, and a high Ki-67 expression. CONCLUSIONS The CT feature of IEU serves as an independent predictor of LIM in gastric GISTs and is strongly associated with high Ki-67 expression. KEY POINTS Question Accurate assessment of LIM risk in patients with gastric GISTs is crucial, yet current non-invasive predictors remain inadequate. Findings IEU on CT is an independent predictor of LIM, with high diagnostic accuracy and a significant association with elevated Ki-67 expression. Clinical relevance IEU on CT scans enables non-invasive risk stratification for LIM in gastric GISTs. Our study refined the assessment of ulceration types, highlighting significant heterogeneity, which may guide personalised treatment strategies.
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Affiliation(s)
- Jinqiu Ruan
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
| | - Yinfu He
- Department of Radiology, Honghe Prefecture Third People's Hospital, Honghe, China
| | - Qingwan Li
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Mingxia Song
- Department of Pathology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhaojuan Jiang
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Keyu Mao
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jing Ai
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ruiling Yang
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Guangjun Yang
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Pinxiong Li
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
| | - Depei Gao
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
| | - Zhenhui Li
- Department of Radiology, Yunnan Cancer Centre, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
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Liu WH, Li M, Ren GQ, Tang ZY, Shan XH, Yang BQ. Radiomics model based on dual-energy CT venous phase parameters to predict Ki-67 levels in gastrointestinal stromal tumors. Front Oncol 2025; 15:1502062. [PMID: 40365339 PMCID: PMC12069033 DOI: 10.3389/fonc.2025.1502062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Accepted: 03/26/2025] [Indexed: 05/15/2025] Open
Abstract
Objective To develop and validate a radiomics model based on the features of the Dual-Energy CT (DECT) venous phase iodine density maps and effective atomic number maps to predict Ki-67 expression levels in gastrointestinal stromal tumors (GISTs). Methods A total of 91 patients with GIST were retrospectively analyzed, including 69 patients with low Ki-67 expression (≤5%) and 22 patients with high Ki-67 expression (>5%). Four clinical features (gender, age, maximum tumor diameter, and tumor location) were extracted to construct a clinical model. The venous phase enhanced CT iodine density maps and effective atomic number maps of DSCT were used to build radiomics models. Logistic regression was used to combine radiomics features with clinical features to build a combined model. Finally, the optimal model's discrimination, calibration, and clinical decision curve were validated using the Bootstrap method. Results The combined model was identified as the best model, with high predictive performance. The model's discrimination had an AUC of 0.982 (95% CI, 0.9603-1). The calibration test showed a Hosmer-Lemeshow test P-value of 0.99. The clinical decision curve demonstrated a probability threshold range of 15% to 98%, with a high net benefit. Conclusion The nomogram model combining clinical features and radiomics (iodine density map radscore + effective atomic number map radscore) has the highest accuracy for preoperative prediction of Ki-67 expression in GISTs.
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Affiliation(s)
- Wen-hua Liu
- Dalian Medical University, Dalian, Liaoning, China
- Department of Radiology, Jiangsu University affiliated People’s Hospital (Zhenjiang First People’s Hospital), Zhenjiang, Jiangsu, China
| | - Min Li
- Department of Radiology, Jiangsu University affiliated People’s Hospital (Zhenjiang First People’s Hospital), Zhenjiang, Jiangsu, China
| | - Guo-qiang Ren
- Department of Radiology, Jiangsu University affiliated People’s Hospital (Zhenjiang First People’s Hospital), Zhenjiang, Jiangsu, China
| | - Zhi-yang Tang
- Department of Radiology, Jiangsu University affiliated People’s Hospital (Zhenjiang First People’s Hospital), Zhenjiang, Jiangsu, China
| | - Xiu-hong Shan
- Department of Radiology, Jiangsu University affiliated People’s Hospital (Zhenjiang First People’s Hospital), Zhenjiang, Jiangsu, China
| | - Ben-qiang Yang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China
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Jia X, Xiao Y, Zhang H, Li J, Lv S, Zhang Y, Chai F, Feng C, Liu Y, Chen H, Ma F, Wei S, Cheng J, Zhang S, Gao Z, Hong N, Tang L, Wang Y. CT assessed morphological features can predict higher mitotic index in gastric gastrointestinal stromal tumors. Eur Radiol 2025; 35:2094-2105. [PMID: 39349725 DOI: 10.1007/s00330-024-11087-7] [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: 03/29/2024] [Revised: 06/13/2024] [Accepted: 08/02/2024] [Indexed: 03/18/2025]
Abstract
OBJECTIVES To investigate the correlation of the mitotic index (MI) of 1-5 cm gastric gastrointestinal stromal tumors (gGISTs) with CT-identified morphological and first-order radiomics features, incorporating subgroup analysis based on tumor size. METHODS We enrolled 344 patients across four institutions, each pathologically diagnosed with 1-5 cm gGISTs and undergoing preoperative contrast-enhanced CT scans. Univariate and multivariate analyses were performed to investigate the independent CT morphological high-risk features of MI. Lesions were categorized into four subgroups based on their pathological LD: 1-2 cm (n = 69), 2-3 cm (n = 96), 3-4 cm (n = 107), and 4-5 cm (n = 72). CT morphological high-risk features of MI were evaluated in each subgroup. In addition, first-order radiomics features were extracted on CT images of the venous phase, and the association between these features and MI was investigated. RESULTS Tumor size (p = 0.04, odds ratio, 1.41; 95% confidence interval: 1.01-1.96) and invasive margin (p < 0.01, odds ratio, 4.55; 95% confidence interval: 1.77-11.73) emerged as independent high-risk features for MI > 5 of 1-5 cm gGISTs from multivariate analysis. In the subgroup analysis, the invasive margin was correlated with MI > 5 in 3-4 cm and 4-5 cm gGISTs (p = 0.02, p = 0.03), and potentially correlated with MI > 5 in 2-3 cm gGISTs (p = 0.07). The energy was the sole first-order radiomics feature significantly correlated with gGISTs of MI > 5, displaying a strong correlation with CT-detected tumor size (Pearson's ρ = 0.85, p < 0.01). CONCLUSIONS The invasive margin stands out as the sole independent CT morphological high-risk feature for 1-5 cm gGISTs after tumor size-based subgroup analysis, overshadowing intratumoral morphological characteristics and first-order radiomics features. KEY POINTS Question How can accurate preoperative risk stratification of gGISTs be achieved to support treatment decision-making? Findings Invasive margins may serve as a reliable marker for risk prediction in gGISTs up to 5 cm, rather than surface ulceration, irregular shape, necrosis, or heterogeneous enhancement. Clinical relevance For gGISTs measuring up to 5 cm, preoperative prediction of the metastatic risk could help select patients who could be treated by endoscopic resection, thereby avoiding overtreatment.
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Affiliation(s)
- Xiaoxuan Jia
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Youping Xiao
- Department of Radiology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China
| | - Hui Zhang
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Jiazheng Li
- Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China
| | - Shiying Lv
- Department of Radiology, Shijiazhuang People's Hospital, Shijiazhuang, China
| | - Yinli Zhang
- Department of Pathology, Peking University People's Hospital, Beijing, China
| | - Fan Chai
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Caizhen Feng
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Yulu Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Haoquan Chen
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Feiyu Ma
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Shengcai Wei
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Jin Cheng
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Sen Zhang
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Zhidong Gao
- Department of Gastrointestinal Surgery, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Lei Tang
- Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, Beijing, China.
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Tang B, Liu X, Zhang W. CT features of gastric calcifying fibrous tumors: differentiation from gastrointestinal stromal tumors. Abdom Radiol (NY) 2025; 50:1498-1504. [PMID: 39320495 DOI: 10.1007/s00261-024-04600-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2024] [Revised: 09/11/2024] [Accepted: 09/16/2024] [Indexed: 09/26/2024]
Affiliation(s)
- Bo Tang
- Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
| | - Xisheng Liu
- The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Weidong Zhang
- Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Bai S, Sun Y, Xu H. Impact of Gastrointestinal Bleeding on Prognosis and Associated Risk Factors in Gastrointestinal Stromal Tumors: A Systematic Review and Meta-Analysis. Am Surg 2025; 91:434-443. [PMID: 39673549 DOI: 10.1177/00031348241307402] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2024]
Abstract
BackgroundGastrointestinal stromal tumors (GISTs) are common mesenchymal tumors of the digestive tract. The impact of gastrointestinal bleeding on the prognosis of GISTs remains controversial. This study aims to evaluate the prognostic significance of gastrointestinal bleeding in GIST patients and analyze associated risk factors.MethodsA systematic review and meta-analysis were conducted according to the PRISMA guidelines. PubMed, MEDLINE, Web of Science, EMBASE, and Cochrane Library databases were searched for relevant studies published up until December 31, 2023. The pooled hazard ratio (HR) with a 95% confidence interval (CI) was used to estimate the relationship between gastrointestinal bleeding and prognosis. Subgroup analyses were performed based on bleeding location and other risk factors.ResultsTwelve studies involving 3475 patients were included. Gastrointestinal bleeding significantly affected the prognosis of GIST patients, including recurrence-free survival (RFS) (HR = 1.57, 95% CI: 0.98-2.52, P < .01) and overall survival (OS) (HR = 3.04, 95% CI: 1.33-6.97, P < .01). Patients with gastric GIST bleeding had significantly worse prognoses (HR = 4.37, 95% CI: 2.36-8.11, P < .01), while small intestinal bleeding showed no significant difference. The bleeding risk was lower in the small intestine compared to the stomach (HR = .63, 95% CI: 0.48-0.83, P < .01). Age under 65, male gender, tumor size ≥5 cm, and mitotic index ≥5 HPF were identified as high-risk factors for GIST bleeding.ConclusionsGastrointestinal bleeding significantly impacts the prognosis of GIST patients, particularly in those with gastric bleeding.
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Affiliation(s)
- Shuchen Bai
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Yefei Sun
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Hao Xu
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, China
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Cai W, Guo K, Chen Y, Shi Y, Chen J. Sub-regional CT Radiomics for the Prediction of Ki-67 Proliferation Index in Gastrointestinal Stromal Tumors: A Multi-center Study. Acad Radiol 2024; 31:4974-4984. [PMID: 39033048 DOI: 10.1016/j.acra.2024.06.036] [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: 04/12/2024] [Revised: 06/18/2024] [Accepted: 06/22/2024] [Indexed: 07/23/2024]
Abstract
RATIONALE AND OBJECTIVES The objective was to assess and examine radiomics models derived from contrast-enhanced CT for their predictive capacity using the sub-regional radiomics regarding the Ki-67 proliferation index (PI) in patients with pathologically confirmed gastrointestinal stromal tumors (GIST). METHODS In this retrospective study, a total of 412 GIST patients across three institutions (223 from center 1, 106 from center 2, and 83 from center 3) was enrolled. Radiomic features were derived from various sub-regions of the tumor region of interest employing the K-means approach. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify features correlated with Ki-67 PI level in GIST patients. A support vector machine (SVM) model was then constructed to predict the high level of Ki-67 (Ki-67 index >8%), drawing on the radiomics features from each sub-region within the training cohort. RESULTS After features selection process, 6, 9, 9, 7 features were obtained to construct SVM models based on sub-region 1, 2, 3 and the entire tumor, respectively. Among different models, the model developed by the sub-region 1 achieved an area under the receiver operating characteristic curve (AUC) of 0.880 (95% confidence interval [CI]: 0.830 to 0.919), 0.852 (95% CI: 0.770-0.914), 0.799 (95% CI: 0.697-0.879) in the training, external test set 1, and 2, respectively. CONCLUSION The results of the present study suggested that SVM model based on the sub-regional radiomics features had the potential of predicting Ki-67 PI level in patients with GIST.
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Affiliation(s)
- Wemin Cai
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, China; Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Kun Guo
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yongxian Chen
- Department of Chest cancer, Xiamen Second People's Hospital, Xiamen 36100, China
| | - Yubo Shi
- Department of Pulmonary, Yueqing People's Hospital, Wenzhou 325000, China
| | - Junkai Chen
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, China.
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Wang Y, Bai G, Liu Y, Huang M, Chen W, Wang F. Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors. Sci Rep 2024; 14:29336. [PMID: 39592767 PMCID: PMC11599915 DOI: 10.1038/s41598-024-80978-y] [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: 07/12/2024] [Accepted: 11/22/2024] [Indexed: 11/28/2024] Open
Abstract
To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs) patients. We retrospectively collected the clinical, imaging and pathological data of 149 GISTs patients. We randomly assigned the patients in a ratio of 7:3 to a training set (104 cases) and a validation (45 cases) set. We divided the patients into low and high Ki-67 expression group according to postoperative pathology. CT semantic features were analyzed from preoperative enhancement CT images and radiomics features were extracted from venous phase-enhanced images. We used intraclass correlation coefficient, maximal relevance and minimal redundancy and least absolute shrinkage and selection operator method to screen radiomics features and build radiomics label. 6 ML models were used for model construction. Receiver operating characteristic curves were used to evaluate the predictive efficiency of ML models. SHAP analysis was used to explain the contribution of different variables and their risk threshold. AUC of radscores in predicting Ki-67 expression of GIST patients were 0.749 and 0.729 in training and validation set. Among the 6 ML models, SVM exhibited best prediction accuracy. AUC of SVM model in predicting Ki-67 expression of GIST patients were 0.840, 0.767 and 0.832 in training, validation and test set. SHAP analysis showed that radscores and tumor diameter had highly positive contribution to the model. Therefore, the interpretable SVM model can predict Ki-67 expression of GISTs patients individually before surgery, which can provide reliable imaging biomarkers for clinical treatment decisions.
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Affiliation(s)
- Yating Wang
- Department of medical imaging, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, No.1, Huang he West Road, Huai'an, 223300, Jiangsu, China
| | - Genji Bai
- Department of medical imaging, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, No.1, Huang he West Road, Huai'an, 223300, Jiangsu, China
| | - Yan Liu
- Department of medical imaging, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, No.1, Huang he West Road, Huai'an, 223300, Jiangsu, China
| | - Min Huang
- Department of medical imaging, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, No.1, Huang he West Road, Huai'an, 223300, Jiangsu, China
| | - Wei Chen
- Department of medical imaging, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, No.1, Huang he West Road, Huai'an, 223300, Jiangsu, China.
| | - First Wang
- Department of medical imaging, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, No.1, Huang he West Road, Huai'an, 223300, Jiangsu, China
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Xie Z, Suo S, Zhang W, Zhang Q, Dai Y, Song Y, Li X, Zhou Y. Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases. Eur Radiol 2024; 34:2223-2232. [PMID: 37773213 PMCID: PMC10957607 DOI: 10.1007/s00330-023-10249-3] [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: 05/27/2023] [Revised: 07/12/2023] [Accepted: 07/23/2023] [Indexed: 10/01/2023]
Abstract
OBJECTIVES To evaluate and analyze radiomics models based on non-contrast-enhanced computed tomography (CT) and different phases of contrast-enhanced CT in predicting Ki-67 proliferation index (PI) among patients with pathologically confirmed gastrointestinal stromal tumors (GISTs). METHODS A total of 383 patients with pathologically proven GIST were divided into a training set (n = 218, vendor 1) and 2 validation sets (n = 96, vendor 2; n = 69, vendors 3-5). Radiomics features extracted from the most recent non-contrast-enhanced and three contrast-enhanced CT scan prior to pathological examination. Random forest models were trained for each phase to predict tumors with high Ki-67 proliferation index (Ki-67>10%) and were evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics on the validation sets. RESULTS Out of 107 radiomics features extracted from each phase of CT images, four were selected for analysis. The model trained using the non-contrast-enhanced phase achieved an AUC of 0.792 in the training set and 0.822 and 0.711 in the two validation sets, similar to models trained on different contrast-enhanced phases (p > 0.05). Several relevant features, including NGTDM Busyness and tumor size, remained predictive in non-contrast-enhanced and different contrast-enhanced images. CONCLUSION The results of this study indicate that a radiomics model based on non-contrast-enhanced CT matches that of models based on different phases of contrast-enhanced CT in predicting the Ki-67 PI of GIST. GIST may exhibit similar radiological patterns irrespective of the use of contrast agent, and such radiomics features may help quantify these patterns to predict Ki-67 PI of GISTs. CLINICAL RELEVANCE STATEMENT GIST may exhibit similar radiomics patterns irrespective of contrast agent; thus, radiomics models based on non-contrast-enhanced CT could be an alternative for risk stratification in GIST patients with contraindication to contrast agent. KEY POINTS • Performance of radiomics models in predicting Ki-67 proliferation based on different CT phases is evaluated. • Non-contrast-enhanced CT-based radiomics models performed similarly to contrast-enhanced CT in risk stratification in GIST patients. • NGTDM Busyness remains stable to contrast agents in GISTs in radiomics models.
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Affiliation(s)
- Zhenhui Xie
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wang Zhang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qingwei Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Xiaobo Li
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Barat M, Pellat A, Dohan A, Hoeffel C, Coriat R, Soyer P. CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives. Can Assoc Radiol J 2024; 75:107-117. [PMID: 37386745 DOI: 10.1177/08465371231180510] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Anna Pellat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Christine Hoeffel
- Reims Medical School, Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, Reims, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Paris, France
| | - Philippe Soyer
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
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Xin Y, Jiang Q, Liu C, Qiu J. Plumbagin has an inhibitory effect on the growth of TSCC PDX model and it enhances the anticancer efficacy of cisplatin. Aging (Albany NY) 2023; 15:12225-12250. [PMID: 37925175 PMCID: PMC10683608 DOI: 10.18632/aging.205175] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/02/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND Head and neck squamous cell carcinomas are the sixth most common malignant tumors worldwide. Tongue squamous cell carcinoma is a common malignant tumor of this type, and it is associated with poor prognosis, a high rate of recurrence and a low survival rate. Plumbagin is derived from Plumbago zeylanica L, several studies report that plumbagin could inhibit cell, tumor metastasis, induce apoptosis in various cancer cells. Patient-derived xenograft (PDX) model can maintain the heterogeneity and microenvironment of human tumors, is a powerful research tool for developing potentially effective therapies for TSCC. METHODS Tumor tissues obtained from TSCC patients were implanted into immunodeficient mice to establish TSCC PDX models. Subsequently, the PDX models were used to evaluate the anti-tumor effects of plumbagin on TSCC. Furthermore, we conducted next-generation sequencing (NGS) and explored the mRNA expression profiles between the treatment and control groups. We selected eight mRNAs related to the characteristics and prognosis of TSCC patients for further analysis. RESULTS Plumbagin could inhibit the growth of TSCC PDX models and inhibit expression of Akt/mTOR pathway. In addition, plumbagin was shown to increase drug sensitivity to cisplatin. The eight mRNAs selected for further analysis, AXL, SCG5, VOPP1, DCBLD2 and DRAM1 are cancer-promoting genes, DUSP1, AQP5 and BLNK are cancer suppressor genes. And they were related to the diagnosis, growth, prognosis, and immune cell infiltration in TSCC patients. CONCLUSION Plumbagin exhibits an inhibitory effect on the growth of the PDX model of TSCC. Moreover, plumbagin enhances the inhibitory effects of cisplatin.
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Affiliation(s)
- Yuqi Xin
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
- Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Qingkun Jiang
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
- Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Chenshu Liu
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
- Medical College, Nanchang University, Nanchang, Jiangxi 330006, China
| | - Jiaxuan Qiu
- Department of Stomatology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China
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Wang P, Yan J, Qiu H, Huang J, Yang Z, Shi Q, Yan C. A radiomics-clinical combined nomogram-based on non-enhanced CT for discriminating the risk stratification in GISTs. J Cancer Res Clin Oncol 2023; 149:12993-13003. [PMID: 37464150 DOI: 10.1007/s00432-023-05170-7] [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: 06/08/2023] [Accepted: 07/09/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To discriminate the risk stratification in gastrointestinal stromal tumors (GISTs) by preoperatively constructing a model of nonenhanced computed tomography (NECT). METHODS A total of 111 GISTs patients (77 in the training group and 34 in the validation Group) from two hospitals between 2015 and 2022 were collected retrospectively. One thousand and thirty-seven radiomics features were extracted from non-contract CT images, and the optimal radiomics signature was determined by univariate analysis and LASSO regression. The radiomics model was developed and validated from the ten optimal radiomics features by three methods. Covariates (clinical features, CT findings, and immunohistochemical characteristics) were collected to establish the clinical model, and both the radiomics features and the covariates were used to build the combined model. The effectiveness of the three models was evaluated by the Delong test. RESULTS The experimental results showed that the clinical models (75.3%, 70.6%), the radiomics models (79.2%, 79.4%) and the combined models (81.8%, 82.4%) all had high accuracy in predicting the pathological risk of GIST in both training and validation groups. The AUC values of the combined models were significantly higher in both the training groups (0.921 vs 0.822, p= 0.032) and the validation groups (0.913 vs 0.792, p= 0.019) than that of the clinical models. According to the calibration curve, the combined model nomogram is clinically useful. CONCLUSIONS The clinical-radiomics combined model and based on NECT performed well in discriminating the risk stratification in GISTs. As a quantitative technique, radiomics is capable of predicting the malignant potential and guiding treatment preoperatively.
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Affiliation(s)
- Peizhe Wang
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China
| | - Jingrui Yan
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Hui Qiu
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China
| | - Jingying Huang
- Department of Medical Imaging, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China
| | - Zhe Yang
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China
| | - Qiang Shi
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China
| | - Chengxin Yan
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China.
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Liu Y, He C, Fang W, Peng L, Shi F, Xia Y, Zhou Q, Zhang R, Li C. Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT. Eur Radiol 2023; 33:7609-7617. [PMID: 37266658 DOI: 10.1007/s00330-023-09727-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVE To study the value of radiomics models based on plain and multiphase contrast-enhanced CT to predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). METHODS A total of 215 patients with GISTs were retrospectively analyzed, including 150 patients in one hospital as the training set and 65 patients in another hospital as the external verification set. The tumor at the largest level of CT images was delineated as the region of interest (ROI). The maximum diameter of the ROI was defined as the tumor size. A total of 851 radiomics features were extracted from each ROI by 3D Slicer Radiomics. After dimensionality reduction, three machine learning classification algorithms including logistic regression (LR), random forest (RF), and support vector machine (SVM) were used for Ki-67 expression prediction. Using a multivariable logistic model, a nomogram was established to predict the expression of Ki-67 individually. RESULTS Delong tests showed that the SVM models had the highest accuracy in the arterial phase (Z value 0.217-1.139) and venous phase (Z value 0.022-1.396). For the plain phase, LR and SVM models had the highest accuracy (Z value 0.874-1.824, 1.139-1.763). For the delayed phase, LR models had the highest accuracy (Z value 0.056-1.824). For the combined phase, RF models had the highest accuracy (Z value 0.232-1.978). There was no significant difference among the above models for KI-67 expression prediction (Z value 0.022-1.978). A nomogram was developed with a C-index of 0.913 (95% CI, 0.878 to 0.956). CONCLUSIONS Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. CLINICAL RELEVANCE STATEMENT CT radiomics could accurately predict the expression of Ki-67 in GIST, which has a great clinical value in reflecting the proliferative activity of tumor cells and helping determine whether a patient is suitable for adjuvant therapy with imatinib. KEY POINTS • Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. • For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. • A radiomics nomogram was developed to allow personalized preoperative evaluation with high accuracy.
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Affiliation(s)
- Yun Liu
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - ChangYin He
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Weidong Fang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Peng
- Department of Pathology, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Qing Zhou
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Ronggui Zhang
- Department of Urology, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China.
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China.
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Wang S, Dai P, Si G, Zeng M, Wang M. Multi-Slice CT Features Predict Pathological Risk Classification in Gastric Stromal Tumors Larger Than 2 cm: A Retrospective Study. Diagnostics (Basel) 2023; 13:3192. [PMID: 37892014 PMCID: PMC10606329 DOI: 10.3390/diagnostics13203192] [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: 08/16/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND The Armed Forces Institute of Pathology (AFIP) had higher accuracy and reliability in prognostic assessment and treatment strategies for patients with gastric stromal tumors (GSTs). The AFIP classification is frequently used in clinical applications. But the risk classification is only available for patients who are previously untreated and received complete resection. We aimed to investigate the feasibility of multi-slice MSCT features of GSTs in predicting AFIP risk classification preoperatively. METHODS The clinical data and MSCT features of 424 patients with solitary GSTs were retrospectively reviewed. According to pathological AFIP risk criteria, 424 GSTs were divided into a low-risk group (n = 282), a moderate-risk group (n = 72), and a high-risk group (n = 70). The clinical data and MSCT features of GSTs were compared among the three groups. Those variables (p < 0.05) in the univariate analysis were included in the multivariate analysis. The nomogram was created using the rms package. RESULTS We found significant differences in the tumor location, morphology, necrosis, ulceration, growth pattern, feeding artery, vascular-like enhancement, fat-positive signs around GSTs, CT value in the venous phase, CT value increment in the venous phase, longest diameter, and maximum short diameter (all p < 0.05). Two nomogram models were successfully constructed to predict the risk of GSTs. Low- vs. high-risk group: the independent risk factors of high-risk GSTs included the location, ulceration, and longest diameter. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.911 (95% CI: 0.872-0.951), and the sensitivity and specificity were 80.0% and 89.0%, respectively. Moderate- vs. high-risk group: the morphology, necrosis, and feeding artery were independent risk factors of a high risk of GSTs, with an AUC value of 0.826 (95% CI: 0.759-0.893), and the sensitivity and specificity were 85.7% and 70.8%, respectively. CONCLUSIONS The MSCT features of GSTs and the nomogram model have great practical value in predicting pathological AFIP risk classification between high-risk and non-high-risk groups before surgery.
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Affiliation(s)
- Sikai Wang
- Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, No. 182 Chunhui Road, Longmatan District, Luzhou 646000, China; (S.W.); (P.D.)
| | - Ping Dai
- Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, No. 182 Chunhui Road, Longmatan District, Luzhou 646000, China; (S.W.); (P.D.)
| | - Guangyan Si
- Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, No. 182 Chunhui Road, Longmatan District, Luzhou 646000, China; (S.W.); (P.D.)
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China;
| | - Mingliang Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China;
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Sacerdoţianu VM, Mirea CS, Popa P, Gheonea DI, Foarfă MC, Matei M, Săndulescu DL, Lungulescu CV, Ciurea T, Ungureanu BS. Giant exophytic gastrointestinal stromal tumor (GIST) causing gastric outlet obstruction: case report and review of literature. ROMANIAN JOURNAL OF MORPHOLOGY AND EMBRYOLOGY = REVUE ROUMAINE DE MORPHOLOGIE ET EMBRYOLOGIE 2023; 64:595-601. [PMID: 38184841 PMCID: PMC10863695 DOI: 10.47162/rjme.64.4.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 12/08/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND Gastrointestinal stromal tumors (GISTs) are rare mesenchymal tumors, mostly located within the stomach. About 30% of GISTs are incidentally diagnosed and as they become symptomatic may be associated with bleeding, bowel obstruction or spontaneous rupture. CASE PRESENTATION We present the case of a middle-aged patient diagnosed with a giant gastric GIST, which presented for intermittent gastric outlet obstruction symptoms, and emphasize the major imagistic, histopathological, and therapeutic challenges that may be encountered. There are only several cases of gastric exophytic gastric GIST provoking intermittent gastric outlet obstruction. Tumor resection should be adapted to every patient's status, focused on en bloc extraction, with preservation of invaded organs as much as possible.
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Affiliation(s)
- Victor-Mihai Sacerdoţianu
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Romania
| | - Cecil Sorin Mirea
- Department of Surgery, University of Medicine and Pharmacy of Craiova, Romania
| | - Petrică Popa
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Romania
| | - Dan Ionuţ Gheonea
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Romania
| | - Maria Camelia Foarfă
- Department of Pathology, University of Medicine and Pharmacy of Craiova, Romania
| | - Marius Matei
- Department of Histology, University of Medicine and Pharmacy of Craiova, Romania
| | - Daniela Larisa Săndulescu
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Romania
| | | | - Tudorel Ciurea
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Romania
| | - Bogdan Silviu Ungureanu
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Romania
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Wang TT, Liu WW, Liu XH, Gao RJ, Zhu CY, Wang Q, Zhao LP, Fan XM, Li J. Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors. World J Gastrointest Oncol 2023; 15:1073-1085. [PMID: 37389110 PMCID: PMC10303000 DOI: 10.4251/wjgo.v15.i6.1073] [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: 03/09/2023] [Revised: 04/02/2023] [Accepted: 04/25/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs).
AIM To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs.
METHODS The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification.
RESULTS There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 ± 0.94 vs 6.63 ± 3.26 cm, P < 0.001) between the low-grade and high-grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours (P = 0.028; OR = 7.750; 95%CI: 1.253-47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively.
CONCLUSION CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.
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Affiliation(s)
- Tian-Tian Wang
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Wei-Wei Liu
- Department of Rheumatology, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Xian-Hai Liu
- Department of Network Information Center, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Rong-Ji Gao
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Chun-Yu Zhu
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Qing Wang
- Department of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Lu-Ping Zhao
- Department of Medical Imaging, The Affiliated Hospital of Ji’ning Medical University, Jining 272000, Shandong Province, China
| | - Xiao-Ming Fan
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Juan Li
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
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Jia X, Wan L, Chen X, Ji W, Huang S, Qi Y, Cui J, Wei S, Cheng J, Chai F, Feng C, Liu Y, Zhang H, Sun Y, Hong N, Rao S, Zhang X, Xiao Y, Ye Y, Tang L, Wang Y. Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis. Eur Radiol 2023; 33:2768-2778. [PMID: 36449061 DOI: 10.1007/s00330-022-09228-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/15/2022] [Accepted: 10/09/2022] [Indexed: 12/03/2022]
Abstract
OBJECTIVES To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1-2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. METHODS A total of 151 pathologically confirmed 1-2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. RESULTS The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1-2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1-2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. CONCLUSIONS Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs. KEY POINTS • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1-2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs.
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Affiliation(s)
- Xiaoxuan Jia
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Lijuan Wan
- 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
| | - Xiaoshan Chen
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Wanying Ji
- Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, China
| | - Shaoqing Huang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuangang Qi
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Jingjing Cui
- United Imaging Intelligence (Beijing) Co., Ltd., Yongteng North Road, Haidian District, Beijing, 100094, China
| | - Shengcai Wei
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Jin Cheng
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Fan Chai
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Caizhen Feng
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Yulu Liu
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Hongmei Zhang
- 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
| | - Yingshi Sun
- Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Shengxiang Rao
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Xinhua Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
| | - Youping Xiao
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, China.
| | - Yingjiang Ye
- Department of Gastrointestinal Surgery, Peking University People's Hospital, Beijing, 100044, China.
| | - Lei Tang
- Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, China.
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China.
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Xiao L, Zhang Y, Wang Y, Liu L, Pan Y. The relationship between Ki-67 expression and imaging signs and pathological features in GISTs. Front Surg 2023; 10:1095924. [PMID: 36969752 PMCID: PMC10032371 DOI: 10.3389/fsurg.2023.1095924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/17/2023] [Indexed: 03/11/2023] Open
Abstract
IntroductionTo investigate the correlations between the Ki-67 index and plain-scan computerized tomography (CT) signs and pathological features of gastrointestinal stromal tumor (GIST) tissue.Materials and methodsData from 186 patients with GIST diagnosed by pathology and immunohistochemistry (IHC) in Peking University First Hospital from May 2016 to May 2022 were analyzed. The patients were divided into two groups: Ki-67 ≤5% and >5%. Correlation analysis, univariate and multivariate Logistic regression analysis were used to explore the correlations between CT signs, pathological features, and Ki-67 expression.ResultsUnivariate indicators correlated with the Ki-67 index were mitotic count, pathological grade, tumor hemorrhage, tumor necrosis, tumor size, and tumor density. Multivariate Logistic regression indicated that the mitotic count [odds ratio (OR) 10.222, 95% confidence interval (CI) 4.312–31.039], pathological grade (OR 2.139, 95% CI 1.397–3.350), and tumor size (OR 1.096, 95% CI 1.020–1.190) were independently associated with the Ki-67 expression level. The concordance indexes (C-index) for the pathological features and CT signs models were 0.876 (95% CI 0.822–0.929) and 0.697 (95% CI 0.620–0.774), respectively, with positive predictive values of 93.62% and 58.11% and negative predictive values of 81.29% and 75.89%, respectively. After internal verification by the Bootstrap method, the fitting degree of the pathological features model was found to be better than that of the CT signs model.ConclusionMitotic count, pathological risk grading, and tumor size are independent risk factors correlating with high Ki-67 index. These results indicate that the Ki-67 index reflects tumor malignancy and can predict recurrence and metastasis of GIST.
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Wang L, Ni Z, Xu W, Mei Y, Li C, Zhu Z, Liu W. Clinical characteristics and outcomes of gastrointestinal stromal tumor patients receiving surgery with or without TKI therapy: a retrospective real-world study. World J Surg Oncol 2023; 21:21. [PMID: 36691015 PMCID: PMC9869533 DOI: 10.1186/s12957-023-02897-y] [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: 07/22/2022] [Accepted: 01/10/2023] [Indexed: 01/25/2023] Open
Abstract
PURPOSE To retrospectively analyze the clinical characteristics of patients undergoing surgical treatment for gastrointestinal stromal tumors (GISTs) in Ruijin Hospital and explore the relevant prognosis clinical factors after surgical treatment. METHODS We screened out 1015 patients with GISTs diagnosed and treated during January 2010 to December 2019. We performed univariate analysis by the log-rank test and multivariate analysis by COX regression. The Kaplan-Meier method was used to estimate the disease-free survival (DFS) and overall survival (OS) of the whole group. RESULTS All 1015 patients in the whole group received radical surgery, and the proportion of patients with high, intermediate, and low risk was 31.1%, 21.7%, and 47.3%, respectively. Among the 480 low-risk patients, surgery could achieve radical therapy; only the Ki-67 index was related to DFS and OS (DFS: p = 0.032, OS: p = 0.009) among the 140 intermediate-risk patients with tumors located in the stomach, whether received Tyrosine kinase inhibitors (TKIs) therapy did not affect the prognosis of patients (DFS: p = 0.716, OS: p = 0.848). Among the 331 high-risk patients, those with non-gastric tumors (those outside the stomach, duodenum, and small intestine, HR 1.55, 95% CI 1.19-2.00, p < 0.001), tumor diameter > 10 cm (hazard ratio, HR 2.63, 95% confidence interval, CI 2.09-4.03, p < 0.001), as well as high-risk patients with mitotic rate > 10/50 HPF (HR 2.74, 95% CI 2.00-3.76, p < 0.001), the overall prognosis was obviously worse than that of other patients. For some high-risk patients, prolonged postoperative imatinib therapy could significantly improve the survival of patients (HR 0.43, 95% CI 0.15-0.66, p < 0.001). CONCLUSIONS For the vast majority of GIST patients, surgery can be curative; but in intermediate-risk patients, the Ki-67 index and postoperative TKI treatment are closely related to prognosis. For intermediate-risk patients whose primary tumor is the stomach, the value of TKI-targeted therapy after surgery seem be not necessary in our study. However, for some high-risk patients, the prognosis of patients can be improved by appropriately prolonging the treatment time of TKI.
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Affiliation(s)
- Lingquan Wang
- Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhentian Ni
- Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Wei Xu
- Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yu Mei
- Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chen Li
- Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhenggang Zhu
- Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. .,Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Wentao Liu
- Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. .,Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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19
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Liu M, Bian J. Radiomics signatures based on contrast-enhanced CT for preoperative prediction of the Ki-67 proliferation state in gastrointestinal stromal tumors. Jpn J Radiol 2023:10.1007/s11604-023-01391-5. [PMID: 36652141 DOI: 10.1007/s11604-023-01391-5] [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: 10/16/2022] [Accepted: 01/07/2023] [Indexed: 01/19/2023]
Abstract
PURPOSE This study aimed to evaluate the Ki-67 proliferation state in patients with gastrointestinal stromal tumors (GISTs) using radiomics prediction signatures based on contrast-enhanced computed tomography (CE-CT). MATERIALS AND METHODS This single-center, retrospective study involved 103 patients (48 men and 55 women, mean age 61.1 ± 10.6 years) who had pathologically confirmed GISTs after curative resection, including 63 with low Ki-67 proliferation level (Ki-67 labeling index ≤ 6%) and 40 with high Ki-67 proliferation level (Ki-67 labeling index > 6%). Radiomics features of the delineated lesions were preoperatively extracted from three-phase CE-CT images, including the arterial, venous, and delayed phases. The most relevant features were selected to construct the radiomics signatures using a logistic regression algorithm. Significant demographic characteristics and semantic features on CT were selected to develop a nomogram along with the optimal radiomics feature. We calculated the sensitivity, specificity, accuracy, F1 score, and area under the receiver operating characteristic (ROC) curve to evaluate the predictive performance of radiomics signatures. RESULTS Ten quantitative radiomics features (two first-order and eight texture features) were selected to construct radiomics signatures. The radiomics signature based on the three-phase CE-CT images showed better predictive performance than that based on the single-phase CE-CT images, with an area under the curve (AUC) of 0.83 (95% CI 0.73-0.92) and F1 score of 82% in the training dataset and an AUC of 0.80 (95% CI 0.63-0.95) and F1 score of 75% in the testing dataset. The nomogram showed good calibration. CONCLUSION Radiomics signatures using CE-CT images are generalizable and could be used in clinical practice to determine the proliferation state of Ki-67 in GISTs.
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Affiliation(s)
- Meijun Liu
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No.467 Zhongshan Road, Shahekou District, Dalian, 116027, Liaoning Province, China
| | - Jie Bian
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No.467 Zhongshan Road, Shahekou District, Dalian, 116027, Liaoning Province, China.
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20
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Wang X, Abi-Raad R, Tang H, Cai G. Ki-67 index assessment on FNA specimens of gastrointestinal stromal tumor: Correlation with mitotic rate and potential predictive value for risk stratification. Cancer Cytopathol 2022; 130:974-982. [PMID: 35876606 DOI: 10.1002/cncy.22630] [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: 04/25/2022] [Revised: 06/12/2022] [Accepted: 06/28/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND Risk assessment of gastrointestinal stromal tumor (GIST) is challenging on cytology specimens. This study aims to determine whether Ki-67 index evaluated on fine-needle aspiration (FNA) specimens can correlate with the mitotic rate of GIST in surgical specimens and provide further risk assessment. METHODS Cases with cell blocks containing adequate tumor cells and surgical resections were included. Ki-67 immunostain was retrospectively performed on cell block sections, and Ki-67 index was calculated on the "hot spot" areas. RESULTS This study included 50 GIST cases from stomach (n = 45; 90%), duodenum (n = 4; 8%), and distal esophagus (n = 1; 2%). The tumor size ranged from 1.5 cm to 21 cm (mean, 5.4 cm). Based on the mitotic count, 37 GISTs (74%) had low mitotic rate (LMR) and 13 GISTs (26%) had high mitotic rate (HMR). The spindle cell, epithelioid, and mixed types accounted for 60%, 14%, and 26% of GIST, respectively. Ki-67 index counted on cell block sections correlated well with mitotic count evaluated in surgical specimens (r = 0.8031). Mean Ki-67 index was higher in HMR than LMR groups (3.5% vs. 1%, p < .001). The receiver operating characteristic curve using Ki-67 index to predict mitotic rate was further analyzed, and area under the curve was 0.839. Using a cutoff of 2.5% yielded a sensitivity of 70% at 92% specificity. CONCLUSIONS This study demonstrates good correlations between Ki-67 index and mitotic count or risk stratification, suggesting that Ki-67 index evaluated on cytology specimens may offer a promising approach to preoperatively predict the mitotic rate and risk of GIST.
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Affiliation(s)
- Xi Wang
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Rita Abi-Raad
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Haiming Tang
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Guoping Cai
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA.,Yale Cancer Center, Yale University School of Medicine, New Haven, Connecticut, USA
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21
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Mitrovic-Jovanovic M, Djuric-Stefanovic A, Ebrahimi K, Dakovic M, Kovac J, Šarac D, Saponjski D, Jankovic A, Skrobic O, Sabljak P, Micev M. The Utility of Conventional CT, CT Perfusion and Quantitative Diffusion-Weighted Imaging in Predicting the Risk Level of Gastrointestinal Stromal Tumors of the Stomach: A Prospective Comparison of Classical CT Features, CT Perfusion Values, Apparent Diffusion Coefficient and Intravoxel Incoherent Motion-Derived Parameters. Diagnostics (Basel) 2022; 12:2841. [PMID: 36428901 PMCID: PMC9689886 DOI: 10.3390/diagnostics12112841] [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: 09/28/2022] [Revised: 11/01/2022] [Accepted: 11/09/2022] [Indexed: 11/19/2022] Open
Abstract
Background: The role of advanced functional imaging techniques in prediction of pathological risk categories of gastrointestinal stromal tumors (GIST) is still unknown. The purpose of this study was to evaluate classical CT features, CT-perfusion and magnetic-resonance-diffusion-weighted-imaging (MR-DWI)-related parameters in predicting the metastatic risk of gastric GIST. Patients and methods: Sixty-two patients with histologically proven GIST who underwent CT perfusion and MR-DWI using multiple b-values were prospectively included. Morphological CT characteristics and CT-perfusion parameters of tumor were comparatively analyzed in the high-risk (HR) and low-risk (LR) GIST groups. Apparent diffusion coefficient (ADC) and intravoxel-incoherent-motion (IVIM)-related parameters were also analyzed in 45 and 34 patients, respectively. Results: Binary logistic regression analysis revealed that greater tumor diameter (p < 0.001), cystic structure (p < 0.001), irregular margins (p = 0.007), irregular shape (p < 0.001), disrupted mucosa (p < 0.001) and visible EFDV (p < 0.001), as well as less ADC value (p = 0.001) and shorter time-to-peak (p = 0.006), were significant predictors of HR GIST. Multivariate analysis extracted irregular shape (p = 0.006) and enlarged feeding or draining vessels (EFDV) (p = 0.017) as independent predictors of HR GIST (area under curve (AUC) of predicting model 0.869). Conclusion: Although certain classical CT imaging features remain most valuable, some functional imaging parameters may add the diagnostic value in preoperative prediction of HR gastric GIST.
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Affiliation(s)
- Milica Mitrovic-Jovanovic
- Department of Digestive Radiology, Center for Radiology and Magnetic Resonance Imaging, University Clinical Center of Serbia, Pasterova 2, 11000 Belgrade, Serbia
| | - Aleksandra Djuric-Stefanovic
- Department of Digestive Radiology, Center for Radiology and Magnetic Resonance Imaging, University Clinical Center of Serbia, Pasterova 2, 11000 Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11000 Belgrade, Serbia
| | - Keramatollah Ebrahimi
- Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11000 Belgrade, Serbia
- Department of Surgery, First University Surgical Clinic, University Clinical Center of Serbia, Koste Todorovica 6, 11000 Belgrade, Serbia
| | - Marko Dakovic
- Faculty of Physical Chemistry, University of Belgrade, 11000 Belgrade, Serbia
| | - Jelena Kovac
- Department of Digestive Radiology, Center for Radiology and Magnetic Resonance Imaging, University Clinical Center of Serbia, Pasterova 2, 11000 Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11000 Belgrade, Serbia
| | - Dimitrije Šarac
- Department of Digestive Radiology, Center for Radiology and Magnetic Resonance Imaging, University Clinical Center of Serbia, Pasterova 2, 11000 Belgrade, Serbia
| | - Dusan Saponjski
- Department of Digestive Radiology, Center for Radiology and Magnetic Resonance Imaging, University Clinical Center of Serbia, Pasterova 2, 11000 Belgrade, Serbia
| | - Aleksandra Jankovic
- Department of Digestive Radiology, Center for Radiology and Magnetic Resonance Imaging, University Clinical Center of Serbia, Pasterova 2, 11000 Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11000 Belgrade, Serbia
| | - Ognjan Skrobic
- Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11000 Belgrade, Serbia
- Department of Surgery, First University Surgical Clinic, University Clinical Center of Serbia, Koste Todorovica 6, 11000 Belgrade, Serbia
| | - Predrag Sabljak
- Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11000 Belgrade, Serbia
- Department of Surgery, First University Surgical Clinic, University Clinical Center of Serbia, Koste Todorovica 6, 11000 Belgrade, Serbia
| | - Marjan Micev
- Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11000 Belgrade, Serbia
- Department of Pathology, First University Surgical Clinic, University Clinical Center of Serbia, Koste Todorovica 6, 11000 Belgrade, Serbia
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22
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Weeda YA, Kalisvaart GM, van Velden FHP, Gelderblom H, van der Molen AJ, Bovee JVMG, van der Hage JA, Grootjans W, de Geus-Oei LF. Early Prediction and Monitoring of Treatment Response in Gastrointestinal Stromal Tumors by Means of Imaging: A Systematic Review. Diagnostics (Basel) 2022; 12:2722. [PMID: 36359564 PMCID: PMC9689665 DOI: 10.3390/diagnostics12112722] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 05/11/2025] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms. Tyrosine kinase inhibitor (TKI) therapy is currently part of routine clinical practice for unresectable and metastatic disease. It is important to assess the efficacy of TKI treatment at an early stage to optimize therapy strategies and eliminate futile ineffective treatment, side effects and unnecessary costs. This systematic review provides an overview of the imaging features obtained from contrast-enhanced (CE)-CT and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT to predict and monitor TKI treatment response in GIST patients. PubMed, Web of Science, the Cochrane Library and Embase were systematically screened. Articles were considered eligible if quantitative outcome measures (area under the curve (AUC), correlations, sensitivity, specificity, accuracy) were used to evaluate the efficacy of imaging features for predicting and monitoring treatment response to various TKI treatments. The methodological quality of all articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies, v2 (QUADAS-2) tool and modified versions of the Radiomics Quality Score (RQS). A total of 90 articles were included, of which 66 articles used baseline [18F]FDG-PET and CE-CT imaging features for response prediction. Generally, the presence of heterogeneous enhancement on baseline CE-CT imaging was considered predictive for high-risk GISTs, related to underlying neovascularization and necrosis of the tumor. The remaining articles discussed therapy monitoring. Clinically established imaging features, including changes in tumor size and density, were considered unfavorable monitoring criteria, leading to under- and overestimation of response. Furthermore, changes in glucose metabolism, as reflected by [18F]FDG-PET imaging features, preceded changes in tumor size and were more strongly correlated with tumor response. Although CE-CT and [18F]FDG-PET can aid in the prediction and monitoring in GIST patients, further research on cost-effectiveness is recommended.
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Affiliation(s)
- Ylva. A. Weeda
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Gijsbert M. Kalisvaart
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | | | - Hans Gelderblom
- Department of Medical Oncology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Aart. J. van der Molen
- Department of Radiology, Section of Abdominal Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Judith V. M. G. Bovee
- Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Jos A. van der Hage
- Department of Surgical Oncology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Willem Grootjans
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Biomedical Photonic Imaging Group, University of Twente, 7522 NB Enschede, The Netherlands
- Department of Radiation Science & Technology, Technical University of Delft, 2629 JB Delft, The Netherlands
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23
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Zhu MP, Ding QL, Xu JX, Jiang CY, Wang J, Wang C, Yu RS. Building contrast-enhanced CT-based models for preoperatively predicting malignant potential and Ki67 expression of small intestine gastrointestinal stromal tumors (GISTs). ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3161-3173. [PMID: 33765174 DOI: 10.1007/s00261-021-03040-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/02/2021] [Accepted: 03/05/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE To assess contrast-enhanced computed tomography (CE-CT) features for predicting malignant potential and Ki67 in small intestinal gastrointestinal stromal tumors (GISTs) and the correlation between them. METHODS We retrospectively analyzed the pathological and imaging data for 123 patients (55 male/68 female, mean age: 57.2 years) with a histopathological diagnosis of small intestine GISTs who received CE-CT followed by curative surgery from May 2009 to August 2019. According to postoperatively pathological and immunohistochemical results, patients were categorized by malignant potential and the Ki67 index, respectively. CT features were analyzed to be associated with malignant potential or the Ki67 index using univariate analysis, logistic regression and receiver operating curve analysis. Then, we explored the correlation between the Ki67 index and malignant potential by using the Spearman rank correlation. RESULTS Based on univariate and multivariate analysis, a predictive model of malignant potential of small intestine GISTs, consisting of tumor size (p < 0.001) and presence of necrosis (p = 0.033), was developed with the area under the receiver operating curve (AUC) of 0.965 (95% CI, 0.915-0.990; p < 0.001), with 91.53% sensitivity, 96.87% specificity, 96.43% PPV, 92.54% NPV, 94.31% diagnostic accuracy. For high Ki67 expression, a model made up of tumor size (p = 0.051), presence of ulceration (p = 0.054) and metastasis (p = 0.001) may be the best predictive combination with an AUC of 0.785 (95% CI, 0.702-0.854; p < 0.001), 63.33% sensitivity, 76.34% specificity, 46.34% PPV, 86.59% NPV, 73.17% diagnostic accuracy. Ki67 index showed a moderate positive correlation with mitotic count (r = 0.578, p < 0.001), a weak positive correlation with tumor size (r = 0.339, p < 0.001) and with risk stratification (r = 0.364, p < 0.001). CONCLUSION Features on CE-CT could preoperatively predict malignant potential and high Ki67 expression of small intestine GISTs, and Ki67 index may be a promising prognostic factor in predicting the prognosis of small intestine GISTs, independent of the risk stratification system.
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Affiliation(s)
- Miao-Ping Zhu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
- Department of Radiology, Hangzhou Women's Hospital, Hangzhou, China
| | - Qiao-Ling Ding
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Jian-Xia Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chun-Yan Jiang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
- Department of Radiology, People's Hospital of Songyang County, Lishui, China
| | - Jing Wang
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo, China
| | - Chao Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China.
| | - Ri-Sheng Yu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China.
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24
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Webb EM, Mongan J. Gastrointestinal Stromal Tumors: Radiomics may Increase the Role of Imaging in Malignant Risk Assessment. Acad Radiol 2022; 29:817-818. [PMID: 35248459 DOI: 10.1016/j.acra.2022.01.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 01/31/2022] [Indexed: 11/20/2022]
Affiliation(s)
- Emily M Webb
- University of California, San Francisco Department of Radiology and Biomedical Imaging, 505 Parnassus Ave., San Francisco, California 94143-0628.
| | - John Mongan
- University of California, San Francisco Department of Radiology and Biomedical Imaging, 505 Parnassus Ave., San Francisco, California 94143-0628
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25
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Ki67 for evaluating the prognosis of gastrointestinal stromal tumors: A systematic review and meta‑analysis. Oncol Lett 2022; 23:189. [PMID: 35527778 PMCID: PMC9073573 DOI: 10.3892/ol.2022.13309] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Overexpression of Ki67 is observed in tumor cells, and it has been suggested to be a marker for cancer prognosis. However, the relationship between Ki67 expression and the risk of recurrence of gastrointestinal stromal tumors (GISTs) remains poorly defined. In the present study, a meta-analysis was used to examine the associations between Ki67 levels and GIST recurrence. Studies reporting GIST and Ki67 were found by searching Cochrane Library, PubMed and Embase until October 14, 2021. The Newcastle-Ottawa Scale (NOS) was used to verify the quality of the evidence. Totally, 1682 patient cases were included. The odds ratio (OR) estimates and 95% confidence interval (CI) for each publication were determined by a fixed-effects (Mantel-Haenszel) model. A total of 20 studies that fulfilled the inclusion criteria were finally included in the analysis. The average score of quality evaluation was 6.4 points according to NOS. It was found that Ki67 levels were significantly higher in the NIH L group compared with the NIH VL group (OR: 0.51; 95% CI: 0.26-0.99; P=0.04; P heterogeneity=0.44). There was also greater Ki67 overexpression in the NIH I group compared with the NIH L group (OR: 0.45, 95% CI: 0.31-0.65; P<0.0001; P heterogeneity=0.32), while Ki67 levels were greater in the NIH H group than in the NIH I group (OR: 0.20; 95% CI: 0.15-0.28; P<0.00001; P heterogeneity=0.56). In conclusion, Ki67 overexpression may be a useful marker of the risk of recurrent GIST transformation.
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Guo JJ, Tang XB, Qian QF, Zhuo ML, Lin LW, Xue ES, Chen ZK. Application of ultrasonography in predicting the biological risk of gastrointestinal stromal tumors. Scand J Gastroenterol 2022; 57:352-358. [PMID: 34779685 DOI: 10.1080/00365521.2021.2002396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To explore and establish a reliable and noninvasive ultrasound model for predicting the biological risk of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS We retrospectively reviewed 266 patients with pathologically-confirmed GISTs and 191 patients were included. Data on patient sex, age, tumor location, biological risk classification, internal echo, echo homogeneity, boundary, shape, blood flow signals, presence of necrotic cystic degeneration, long diameter, and short/long (S/L) diameter ratio were collected. All patients were divided into low-, moderate-, and high-risk groups according to the modified NIH classification criteria. All indicators were analyzed by univariate analysis. The indicators with inter-group differences were used to establish regression and decision tree models to predict the biological risk of GISTs. RESULTS There were statistically significant differences in long diameter, S/L ratio, internal echo level, echo homogeneity, boundary, shape, necrotic cystic degeneration, and blood flow signals among the low-, moderate-, and high-risk groups (all p < .05). The logistic regression model based on the echo homogeneity, shape, necrotic cystic degeneration and blood flow signals had an accuracy rate of 76.96% for predicting the biological risk, which was higher than the 72.77% of the decision tree model (based on the long diameter, the location of tumor origin, echo homogeneity, shape, and internal echo) (p = .008). In the low-risk and high-risk groups, the predicting accuracy rates of the regression model reached 87.34 and 81.82%, respectively. CONCLUSIONS Transabdominal ultrasound is highly valuable in predicting the biological risk of GISTs. The logistic regression model has greater predictive value than the decision tree model.
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Affiliation(s)
- Jing-Jing Guo
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, Fuzhou, Fujian, China
| | - Xiu-Bin Tang
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, Fuzhou, Fujian, China
| | - Qing-Fu Qian
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, Fuzhou, Fujian, China
| | - Min-Ling Zhuo
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, Fuzhou, Fujian, China
| | - Li-Wu Lin
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, Fuzhou, Fujian, China
| | - En-Sheng Xue
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, Fuzhou, Fujian, China
| | - Zhi-Kui Chen
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, Fuzhou, Fujian, China
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Comparison of Computed Tomography Features of Gastric and Small Bowel Gastrointestinal Stromal Tumors With Different Risk Grades. J Comput Assist Tomogr 2022; 46:175-182. [PMID: 35297574 DOI: 10.1097/rct.0000000000001262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This study aimed to compare the computed tomography (CT) features of gastric and small bowel gastrointestinal stromal tumors (GISTs) and further identify the predictors for risk stratification of them, respectively. METHODS According to the modified National Institutes of Health criteria, patients were classified into low-malignant potential group and high-malignant potential group. Two experienced radiologists reviewed the CT features including the difference of CT values between arterial phase and portal venous phase (PVPMAP) by consensus. The CT features of gastric and small bowel GISTs were compared, and the association of CT features with risk grades was analyzed, respectively. Determinant CT features were used to construct corresponding models. RESULTS Univariate analysis showed that small bowel GISTs tended to present with irregular contour, mixed growth pattern, ill-defined margin, severe necrosis, ulceration, tumor vessels, heterogeneous enhancement, larger size, and marked enhancement compared with gastric GISTs. According to multivariate analysis, tumor size (P < 0.001; odds ratio [OR], 3.279), necrosis (P = 0.008; OR, 2.104) and PVPMAP (P = 0.045; OR, 0.958) were the independent influencing factors for risk stratification of gastric GISTs. In terms of small bowel GISTs, the independent predictors were tumor size (P < 0.001; OR, 3.797) and ulceration (P = 0.031; OR, 4.027). Receiver operating characteristic curve indicated that the CT models for risk stratification of gastric and small bowel GISTs both achieved the best predictive performance. CONCLUSIONS Computed tomography features of gastric and small bowel GISTs are different. Furthermore, the qualitative and quantitative CT features of GISTs may be favorable for preoperative risk stratification.
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Prediction of the Ki-67 expression level and prognosis of gastrointestinal stromal tumors based on CT radiomics nomogram. Int J Comput Assist Radiol Surg 2022; 17:1167-1175. [PMID: 35195831 DOI: 10.1007/s11548-022-02575-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/31/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE To build and validate a radiomics nomogram integrated with the radiomics signature and subjective CT characteristics to predict the Ki-67 expression level of gastrointestinal stromal tumors (GISTs). Moreover, the purpose was to compare the performance of pathological Ki-67 expression level with predicted Ki-67 expression level in estimating the prognosis of GISTs patients. METHODS According to pathological results, patients were classified into high-Ki-67 labeling index group (Ki-67 LI ≥ 5%) and low-Ki-67 LI group (Ki-67 LI < 5%). Radiomics features extracted from contrast-enhanced CT(CECT) images were selected and classified to build a radiomics signature. A combined model was built by incorporating radiomics signature and determinant subjective CT characteristics using multivariate logistic regression analysis. The diagnostic performance of the radiomics signature, subjective CT model and combined model were explored by receiver operating characteristic (ROC) curve analysis and Delong test. The model with best diagnostic performance was then set up for the prediction nomogram. Recurrence-free survival (RFS) rates were compared utilizing Kaplan-Meier curve. RESULTS The generated combined model yielded the best diagnostic performance with area under the curve (AUC) values of 0.738 [95% confidence interval (CI): 0.669-0.807] and 0.772 (95% CI 0.683-0.860) in the training set and testing set respectively. The nomogram based on the combined model demonstrated good calibration in the training set and testing set (both P > 0.05). Patients of high-Ki-67 LI group predicted by our nomogram had a poorer RFS than patients of low-Ki-67 LI group (P < 0.0001). CONCLUSION This radiomics nomogram based on CECT had a satisfactory performance in predicting both the Ki-67 expression level and prognosis noninvasively in patients with GISTs, which may serve as an effective imaging tool that can assist in guiding personalized clinical treatment.
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Li Y, Chen X, Ma X, Lu X. Computed tomography in the size measurement of gastric gastrointestinal stromal tumors: Implication to risk stratification and "wait-and-see" tactics. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2022; 48:1739-1745. [PMID: 35033400 DOI: 10.1016/j.ejso.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/22/2021] [Accepted: 01/04/2022] [Indexed: 02/07/2023]
Abstract
INTRODUCTION This study aimed to compare the radiologic size of gastric gastrointestinal stromal tumors (GISTs) on computed tomography (CT) with the pathologic size in a Chinese population, and elucidate the potential significance of the CT size in the preoperative risk stratification. MATERIALS AND METHODS The study enrolled 314 patients treated by endoscopic/surgical resection of gastric lesions that proved postoperatively to be GISTs. Bland-Altman analysis and intraclass correlation coefficient (ICC) were adopted to assess the size agreement between CT and pathology. Independent predictors of risk category underestimation and the optimal cut-off value of CT size were determined by logistic regression analysis and the receiver operating characteristic (ROC) curve. RESULTS CT underestimated gastric GISTs size by 0.30 cm [95% confidence interval (CI): (-0.42, - 0.19); p < 0.001]. In the subgroup analysis, the size underestimation was 0.10 cm in GISTs ≤ 5 cm [95% CI: (-0.19, -0.01); p = 0.024]; and 0.75 cm in GISTs >5 cm [95% CI: (-1.05, 0.45), p < 0.001]. Though ICC values showed well reliability for the corresponding pathologic size, with 0.95 in all size, 0.86 in size ≤ 5 cm, and 0.92 in size >5 cm respectively. Risk underestimation by CT imaging mainly occurred in gastric GISTs with smaller size (≤5 cm; p = 0.010) and lower mitotic index (≤5 per 50 high-power fields; p = 0.011). CT size of 3.65 cm was defined as an absolute cut-off to differentiate intermediate/high-risk patients from low-risk group, with 87.5% sensitivity at a specificity of 57.8%. CONCLUSION Preoperative CT underestimated the mean size by 0.30 cm in gastric GISTs. A CT size of 3.65 cm would facilitate the selection of potential intermediate/high-risk patients, instant intervention should be encouraged in the absence of contraindications.
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Affiliation(s)
- Yuyi Li
- Department of Gastroenterology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Xuyong Chen
- Department of Gastroenterology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China
| | - Xu Ma
- Department of Gastroenterology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China; Department of Gastroenterology, Lishui City People's Hospital, Lishui, 323000, Zhejiang, China
| | - Xinliang Lu
- Department of Gastroenterology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
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Kang B, Yuan X, Wang H, Qin S, Song X, Yu X, Zhang S, Sun C, Zhou Q, Wei Y, Shi F, Yang S, Wang X. Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors. Front Oncol 2021; 11:750875. [PMID: 34631589 PMCID: PMC8496403 DOI: 10.3389/fonc.2021.750875] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 08/31/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs). Methods Preoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping. Results In the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review. Conclusion The DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.
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Affiliation(s)
- Bing Kang
- Cheeloo College of Medicine, School of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Xianshun Yuan
- Cheeloo College of Medicine, School of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Songnan Qin
- Cheeloo College of Medicine, School of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Xuelin Song
- Department of Radiology, Hospital of Traditional Chinese Medicine of Liaocheng City, Liaocheng, China
| | - Xinxin Yu
- Cheeloo College of Medicine, School of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Shuai Zhang
- School of Medicine, Shandong First Medical University, Jinan, China
| | - Cong Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
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Zhao Y, Feng M, Wang M, Zhang L, Li M, Huang C. CT Radiomics for the Preoperative Prediction of Ki67 Index in Gastrointestinal Stromal Tumors: A Multi-Center Study. Front Oncol 2021; 11:689136. [PMID: 34595107 PMCID: PMC8476965 DOI: 10.3389/fonc.2021.689136] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/30/2021] [Indexed: 12/15/2022] Open
Abstract
Purpose This study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs). Materials and Methods A total of 344 patients with GISTs from three hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model’s performance and generalization ability. Results After dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761–0.908) in the training set and 0.784 (95% CI: 0.691–0.874) in the external validation cohort. Conclusion The radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.
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Affiliation(s)
- Yilei Zhao
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Meibao Feng
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Minhong Wang
- First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Liang Zhang
- Zhejiang Cancer Hospital, University of Chinese Academy of Sciences, Hangzhou, China
| | - Meirong Li
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chencui Huang
- Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
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Yang CW, Liu XJ, Zhao L, Che F, Yin Y, Chen HJ, Zhang B, Wu M, Song B. Preoperative prediction of gastrointestinal stromal tumors with high Ki-67 proliferation index based on CT features. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1556. [PMID: 34790762 PMCID: PMC8576677 DOI: 10.21037/atm-21-4669] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 10/13/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND To determine whether preoperative computed tomography (CT) features can be used for the prediction of gastrointestinal stromal tumors (GISTs) with a high Ki-67 proliferation index (Ki-67 PI). METHODS A total of 198 patients with surgically and pathologically proven GISTs were retrospectively included. All GISTs were divided into a low Ki-67 PI group (<10%) and a high Ki-67 PI group (≥10%). All imaging features were blindly interpreted by two radiologists. Receiver operating characteristic (ROC) curve analyses were conducted to evaluate the predictive performance of the imaging features. RESULTS Imaging features were found to be significantly different between the low and the high Ki-67 PI groups (P<0.05). Wall thickness of necrosis showed the highest predictive ability, with an area under the curve (AUC) of 0.838 [95% confidence interval (CI): 0.627-0.957], followed by necrosis, necrosis degree, hyperenhancement of the overlying mucosa (HYOM), and long diameter (LD) (AUC >0.7, P<0.05). HYOM was the strongest predictive feature for the high Ki-67 PI GISTs group, with an odds ratio (OR) value of 30.037 (95% CI: 5.707-158.106). CONCLUSIONS Imaging features, including the presence of necrosis, high necrosis degree, thick wall of necrosis, and HYOM were significant predictive indicators for the high Ki-67 PI GISTs group.
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Affiliation(s)
- Cai-Wei Yang
- West China School of Medicine, Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xi-Jiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Lian Zhao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Feng Che
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Yuan Yin
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Hui-Jiao Chen
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Zhang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Clinic Medical Center, Dazhou Central Hospital, Dazhou, China
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University, Stanford, CA, USA
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Kuwabara H, Katayanagi S, Koganezawa I, Nakagawa M, Katsumata K, Tsuchida A, Kawachi S. Sporadic intra-abdominal desmoid tumor with a very unusual onset: two case reports. J Med Case Rep 2021; 15:457. [PMID: 34526110 PMCID: PMC8444561 DOI: 10.1186/s13256-021-03058-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 08/11/2021] [Indexed: 01/10/2023] Open
Abstract
Background Intra-abdominal desmoid tumors are rare soft tissue tumors that arise mainly in the mesentery and pelvis. Their etiology may include genetic mutations, estrogen-associated changes after childbirth, and mechanical factors such as a history of abdominal surgery. However, there are cases of intra-abdominal desmoid tumors that develop in the absence of such causes. Since they are rare, diagnosis is often difficult based on clinical findings. We encountered two cases of patients with sporadic intra-abdominal desmoid tumors with a very unusual onset and contrasting features. Case presentation The first patient was a 51-year-old asian man who presented with sudden onset of abdominal pain. He was referred to our department because of a giant tumor detected on abdominal ultrasonography. Imaging revealed a 19-cm tumor with internal tumoral hemorrhage; however, no definitive diagnosis was made. Tumor resection was performed for diagnostic and therapeutic purposes. The second patient was a 41-year-old asian man, and right hydronephrosis was detected on abdominal ultrasonography during a periodic medical checkup. We diagnosed invasion of the primary mesenteric tumor into the right ureter using diagnostic imaging and performed ileocecal resection with partial right ureteral resection for a definitive diagnosis and therapeutic purposes. Although the tumors of both patients had developed from the ileal mesentery, the tumors were substantially different from each other based on their imaging findings, macroscopic morphology, and progression pattern. Meanwhile, they showed similar pathological characteristics. Both consisted of bundles of collagen fibrils of spindle-shaped fibroblasts with low cell atypia. Moreover, they were diagnosed as desmoid tumors using positive immunohistochemical staining for β-catenin. Conclusions Neither patient had susceptibility factors for desmoid tumors, and to our knowledge, there have been very few reports to date of intra-abdominal desmoid tumors that were diagnosed because of acute abdominal pain caused by tumoral hemorrhage or asymptomatic obstructive uropathy. Furthermore, it is clinically interesting that the two patients showed contrasting progression patterns and imaging findings. Intra-abdominal desmoid tumors are rare and may present with various symptoms and findings similar to those observed in our patients. Diagnosis therefore requires experience and knowledge that is not bound by preconceptions.
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Affiliation(s)
- Hiroshi Kuwabara
- Department of Digestive and Transplantation Surgery, Tokyo Medical University Hachioji Medical Center, 1163 Tatemachi, Hachioji, Tokyo, 193-0998, Japan. .,Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku Ward, Tokyo, 160-0012, Japan.
| | - Sou Katayanagi
- Department of Digestive and Transplantation Surgery, Tokyo Medical University Hachioji Medical Center, 1163 Tatemachi, Hachioji, Tokyo, 193-0998, Japan
| | - Itsuki Koganezawa
- Department of Digestive and Transplantation Surgery, Tokyo Medical University Hachioji Medical Center, 1163 Tatemachi, Hachioji, Tokyo, 193-0998, Japan
| | - Masashi Nakagawa
- Department of Digestive and Transplantation Surgery, Tokyo Medical University Hachioji Medical Center, 1163 Tatemachi, Hachioji, Tokyo, 193-0998, Japan
| | - Kenji Katsumata
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku Ward, Tokyo, 160-0012, Japan
| | - Akihiko Tsuchida
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku Ward, Tokyo, 160-0012, Japan
| | - Shigeyuki Kawachi
- Department of Digestive and Transplantation Surgery, Tokyo Medical University Hachioji Medical Center, 1163 Tatemachi, Hachioji, Tokyo, 193-0998, Japan
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Chen XS, Shan YC, Dong SY, Wang WT, Yang YT, Liu LH, Xu ZH, Zeng MS, Rao SX. Utility of preoperative computed tomography features in predicting the Ki-67 labeling index of gastric gastrointestinal stromal tumors. Eur J Radiol 2021; 142:109840. [PMID: 34237492 DOI: 10.1016/j.ejrad.2021.109840] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/31/2021] [Accepted: 06/27/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate the value of preoperative computed tomography (CT) features including morphologic and quantitative features for predicting the Ki-67 labeling index (Ki-67LI) of gastric gastrointestinal stromal tumors (GISTs). METHODS We retrospectively included 167 patients with gastric GISTs who underwent preoperative contrast-enhanced CT. We assessed the morphologic features of preoperative CT images and the quantitative features including the maximum diameter of tumor, total tumor volume, mean total tumor CT value, necrosis volume, necrosis volume ratio, enhanced tissue volume, and mean CT value of enhanced tissue. Potential predictive parameters to distinguish the high-level Ki-67LI group (>4%, n = 125) from the low-level Ki-67LI group (≤4%, n = 42) were compared and subsequently determined in multivariable logistic regression analysis. RESULTS Growth pattern (p = 0.036), shape (p = 0.000), maximum diameter (p = 0.018), total tumor volume (p = 0.021), mean total tumor CT value (p = 0.009), necrosis volume (p = 0.006), necrosis volume ratio (p = 0.000), enhanced tissue volume (p = 0.027), and mean CT value of enhanced tissue (p = 0.004) were significantly different between the two groups. Multivariate logistic regression analysis indicated that lobulated/irregular shape (odds ratio [OR] = 3.817; p = 0.000) and high necrosis volume ratio (OR = 1.935; p = 0.024) were independent factors of high-level Ki-67LI. CONCLUSIONS Higher necrosis volume ratio in combination with lobulated/irregular shape could potentially predict high expression of Ki-67LI for gastric GISTs.
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Affiliation(s)
- Xiao-Shan Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China
| | - Ying-Chan Shan
- Department of Radiology, Zhongshan Hospital, Fudan University, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China
| | - San-Yuan Dong
- Department of Radiology, Zhongshan Hospital, Fudan University, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China
| | - Wen-Tao Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China
| | - Yu-Tao Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China
| | - Li-Heng Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China
| | - Zhi-Han Xu
- Department of CT Collaboration, Siemens Healthineers, China
| | - Meng-Su Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China
| | - Sheng-Xiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China.
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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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Apte SS, Radonjic A, Wong B, Dingley B, Boulva K, Chatterjee A, Purgina B, Ramsay T, Nessim C. Preoperative imaging of gastric GISTs underestimates pathologic tumor size: A retrospective, single institution analysis. J Surg Oncol 2021; 124:49-58. [PMID: 33857332 DOI: 10.1002/jso.26494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/21/2021] [Accepted: 04/02/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND How well imaging size agrees with pathologic size of gastric gastrointestinal stromal tumors (GISTs) is unknown. GIST risk stratification is based on pathologic size, location, and mitotic rate. To inform decision making, the size discrepancy between imaging and pathology for gastric GISTs was investigated. METHODS Imaging and pathology reports were reviewed for 113 patients. Bland-Altman analyses and intraclass correlation (ICC) assessed agreement of imaging and pathology. Changes in clinical risk category due to size discrepancy were identified. RESULTS Computed tomography (CT) (n = 110) and endoscopic ultrasound (EUS) (n = 50) underestimated pathologic size for gastric GISTs by 0.42 cm, 95% confidence interval (CI): (0.11, 0.73), p = 0.008 and 0.54 cm, 95% CI: (0.25, 0.82), p < 0.001, respectively. ICCs were 0.94 and 0.88 for CT and EUS, respectively. For GISTs ≤ 3 cm, size underestimation was 0.24 cm for CT (n = 28), 95% CI: (0.01, 0.47), p = 0.039 and 0.56 cm for EUS (n = 26), 95% CI: (0.27, 0.84), p < 0.0001. ICCs were 0.72 and 0.55 for CT and EUS, respectively. Spearman's correlation was ≥0.84 for all groups. For GISTs ≤ 3 cm, 6/28 (21.4% p = 0.01) on CT and 7/26 (26.9% p = 0.005) on EUS upgraded risk category using pathologic size versus imaging size. No GISTs ≤ 3 cm downgraded risk categories. Size underestimation persisted for GISTs ≤ 2 cm on EUS (0.39 cm, 95% CI: [0.06, 0.72], p = 0.02, post hoc analysis). CONCLUSION Imaging, particularly EUS, underestimates gastric GIST size. Caution should be exercised using imaging alone to risk-stratify gastric GISTs, and to decide between surveillance versus surgery.
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Affiliation(s)
- Sameer S Apte
- Department of Surgery, The Ottawa Hospital, Ottawa, Ontario, Canada.,Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada
| | - Aleksandar Radonjic
- Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada
| | - Boaz Wong
- Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada
| | - Brittany Dingley
- Department of Surgery, The Ottawa Hospital, Ottawa, Ontario, Canada.,Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada
| | - Kerianne Boulva
- Department of Surgery, The Ottawa Hospital, Ottawa, Ontario, Canada.,Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada
| | - Avijit Chatterjee
- Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada.,Department of Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Bibiana Purgina
- Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada.,Department of Pathology, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Timothy Ramsay
- Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada
| | - Carolyn Nessim
- Department of Surgery, The Ottawa Hospital, Ottawa, Ontario, Canada.,Cancer Therapeutics, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Faculty of Medicine, The University of Ottawa, Ottawa, Ontario, Canada
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Grazzini G, Guerri S, Cozzi D, Danti G, Gasperoni S, Pradella S, Miele V. Gastrointestinal stromal tumors: relationship between preoperative CT features and pathologic risk stratification. TUMORI JOURNAL 2021; 107:556-563. [PMID: 33620027 DOI: 10.1177/0300891621996447] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE To investigate a relationship between contrast-enhanced computed tomography (CECT) features of gastrointestinal stromal tumors (GISTs) and risk of relapse according to Miettinen stratified risk classifications. METHODS After ethical committee approval, a retrospective analysis was conducted on the preoperative CECT of patients with pathologically proven GIST undergoing surgery between June 2009 and December 2019. Chi-square analysis was used to evaluate the correlation between Miettinen stratified risk categories and the following imaging features: tumor size and location, growth pattern, margins, type and degree of contrast enhancement, presence of calcifications, necrosis, signs of ulceration/fistulation, internal hemorrhagic foci, enlarged feeding or draining vessels (EFDV), ascites, peritoneal implants, lymphadenopathy, or metastasis. RESULTS A total of 54 patients (mean age 65 ± 11, 29 men) were included in the study with a total of 56 GISTs. Necrosis, ulceration/fistulation, hemorrhage, margins, enlarged vessels, type of contrast enhancement, and metastasis turned out to be associated with Miettinen risk categories (p < 0.005). Logistic regression analysis identified the presence of necrosis and EFDV as predictors of pathologic risk of relapse (overall accuracy of 89.3%). CONCLUSION Preoperative CECT may be helpful in predicting pathologic risk categories of GISTs, as determined by the Miettinen classification system.
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Affiliation(s)
- Giulia Grazzini
- Radiodiagnostica di Emergenza Urgenza, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Sara Guerri
- Radiodiagnostica di Emergenza Urgenza, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Diletta Cozzi
- Radiodiagnostica di Emergenza Urgenza, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Ginevra Danti
- Radiodiagnostica di Emergenza Urgenza, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Silvia Gasperoni
- SOD Oncologia Traslazionale Dipartimento Oncologico AOUC, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Silvia Pradella
- Radiodiagnostica di Emergenza Urgenza, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
| | - Vittorio Miele
- Radiodiagnostica di Emergenza Urgenza, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy
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Xu SJ, Lin GS, Ling HJ, Guo RJ, Chen J, Liao YM, Lin T, Zhou YJ. Nomogram to Predict Preoperative Occult Peritoneal Metastasis of Gastrointestinal Stromal Tumors (GIST) Based on Imaging and Inflammatory Indexes. Cancer Manag Res 2020; 12:11713-11721. [PMID: 33239911 PMCID: PMC7681585 DOI: 10.2147/cmar.s275422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 10/06/2020] [Indexed: 12/27/2022] Open
Abstract
Background Preoperative imaging examination is the primary method for diagnosing metastatic gastrointestinal stromal tumor (GIST), but it is associated with a high rate of missed diagnosis. Therefore, it is important to establish an accurate model for predicting occult peritoneal metastasis (PM) of GIST. Patients and Methods GIST patients seen between April 2002 and December 2018 were selected from an institutional database. Using multivariate logistic regression analyses, we created a nomogram to predict occult PM of GIST and validated it with an independent cohort from the same center. The concordance index (C-index), decision curve analysis (DCA) and a clinical impact curve (CIC) were used to evaluate its predictive ability. Results A total of 522 eligible GIST patients were enrolled in this study and divided into training (n=350) and validation cohorts (n=172). Factors associated with occult PM were included in the model: tumor size (odds ratio [OR] 1.194 95% confidence interval [CI], 1.034-1.378; p=0.016), primary location (OR 7.365 95% CI, 2.192-24.746; p=0.001), tumor capsule (OR 4.282 95% CI, 1.209-15.166; p=0.024), Alb (OR 0.813 95% CI, 0.693-0.954; p=0.011) and FIB (OR 2.322 95% CI, 1.410-3.823; p=0.001). The C-index was 0.951 (95% CI, 0.917-0.985) in the training cohort and 0.946 (95% CI, 0.900-0.992) in the validation cohort. In the training cohort, the prediction model had a sensitivity of 82.8%, a specificity of 93.8%, a positive predictive value of 54.7%, and a negative predictive value of 98.4%; the validation cohort values were 94.7%, 85.0%, 43.9% and 99.2%, respectively. DCA and CIC results showed that the nomogram had clinical value in predicting occult PM in GIST patients. Conclusion Imaging and inflammatory indexes are significantly associated with microscopic metastases of GIST. A nomogram including these factors would have an excellent ability to predict occult PM.
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Affiliation(s)
- Shao-Jun Xu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
| | - Guo-Sheng Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
| | - Hong-Jian Ling
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
| | - Ren-Jie Guo
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
| | - Jie Chen
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
| | - Yi-Ming Liao
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
| | - Tao Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
| | - Yong-Jian Zhou
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, People's Republic of China
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Yang CW, Liu XJ, Liu SY, Wan S, Ye Z, Song B. Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2020; 2020:6058159. [PMID: 33304203 PMCID: PMC7714601 DOI: 10.1155/2020/6058159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/18/2020] [Accepted: 10/31/2020] [Indexed: 02/05/2023]
Abstract
The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk staging, therapy, and follow-up. The recommended imaging method for staging and follow-up is computed tomography (CT) according to current guidelines. Artificial intelligence (AI) applies and elaborates theses, procedures, modes, and utilization systems for simulating, enlarging, and stretching the intellectual capacity of humans. Recently, researchers have done a few studies to explore AI applications in GIST imaging. This article reviews the present AI studies in GISTs imaging, including preoperative diagnosis, risk stratification and prediction of prognosis, gene mutation, and targeted therapy response.
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Affiliation(s)
- Cai-Wei Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xi-Jiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Si-Yun Liu
- GE Healthcare (China), Beijing 100176, China
| | - Shang Wan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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Xu J, Zhou J, Wang X, Fan S, Huang X, Xie X, Yu R. A multi-class scoring system based on CT features for preoperative prediction in gastric gastrointestinal stromal tumors. Am J Cancer Res 2020; 10:3867-3881. [PMID: 33294273 PMCID: PMC7716157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/20/2020] [Indexed: 06/12/2023] Open
Abstract
Our study aimed to establish and validate a multi-class scoring system for preoperative gastric gastrointestinal stromal tumors (GISTs) risk stratifications based on CT features. 150 gastric GIST patients who underwent contrast-enhanced CT examination and surgical resection from hospital 1 were retrospectively analyzed as the training cohort, and 61 patients from hospitals 2 and 3 were included as the validation cohort. A model was established by logistic regression analysis and weighted to be a scoring model. A calibration test, area under the receiver operating characteristic (ROC) curve (AUC), and cutoff points were determined for the score model. The model was also divided into three score ranges for convenient clinical evaluation. Five CT features were included in the score model, including tumor size (4 points), ill-defined margin (6 points), intratumoral enlarged vessels (5 points), heterogeneous enhancement pattern (4 points), and exophytic or mixed growth pattern (2 points). Then, based on the calibration results, performance was merely assessed as very low and high* risk. The AUCs of the score model for very low risk and high* risk were 0.973 and 0.977, and the cutoff points were 3 points (97.30%, 93.81%) and 7 points (92.19%, 94.19%), respectively. In the validation cohort, the AUCs were 0.912 and 0.972, and the cutoff values were 3 points (92.31%, 85.42%) and 5 points (100%, 87.88%), respectively. The model was stratified into 3 ranges: 0-3 points for very low risk, 4-8 points for low risk, and 9-21 points for high* risk. A concise and practical score system for gastric GISTs risk stratification was proposed.
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Affiliation(s)
- Jianxia Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University318 Chaowang Road, Hangzhou 310005, Zhejiang Province, China
| | - Jiaping Zhou
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University88 Jiefang Road, Hangzhou 310009, Zhejiang Province, China
| | - Xiaojie Wang
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University88 Jiefang Road, Hangzhou 310009, Zhejiang Province, China
| | - Shufeng Fan
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University318 Chaowang Road, Hangzhou 310005, Zhejiang Province, China
| | - Xiaoshan Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University318 Chaowang Road, Hangzhou 310005, Zhejiang Province, China
| | - Xingwu Xie
- Department of Radiology, Renmin Hospital, Hubei University of MedicineShiyan 442000, Hubei Province, China
| | - Risheng Yu
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University88 Jiefang Road, Hangzhou 310009, Zhejiang Province, China
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Yang J, Chen Z, Liu W, Wang X, Ma S, Jin F, Wang X. Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors. Korean J Radiol 2020; 22:344-353. [PMID: 33169545 PMCID: PMC7909867 DOI: 10.3348/kjr.2019.0851] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 05/29/2020] [Accepted: 06/15/2020] [Indexed: 11/24/2022] Open
Abstract
Objective The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with
the risk of planting and metastasis. The purpose of this study was to develop a
predictive model for the mitotic index of local primary GIST, based on deep learning
algorithm. Materials and Methods Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were
retrospectively collected for the development of a deep learning classification
algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an
experienced radiologist. The postoperative pathological mitotic count was considered as
the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low
mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the
basis of the VGG16 convolutional neural network, using the CT images with the training
set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity,
specificity, positive predictive value (PPV), and negative predictive value (NPV) were
calculated at both, the image level and the patient level. The receiver operating
characteristic curves were generated on the basis of the model prediction results and
the area under curves (AUCs) were calculated. The risk categories of the tumors were
predicted according to the Armed Forces Institute of Pathology criteria. Results At the image level, the classification prediction results of the mitotic counts in the
test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]:
0.834–0.877), specificity 67.5% (95% CI: 0.636–0.712), PPV 82.1% (95% CI:
0.797–0.843), NPV 73.0% (95% CI: 0.691–0.766), and AUC 0.771 (95% CI:
0.750–0.791). At the patient level, the classification prediction results in the
test cohort were as follows: sensitivity 90.0% (95% CI: 0.541–0.995), specificity
70.0% (95% CI: 0.354–0.919), PPV 75.0% (95% CI: 0.428–0.933), NPV 87.5%
(95% CI: 0.467–0.993), and AUC 0.800 (95% CI: 0.563–0.943). Conclusion We developed and preliminarily verified the GIST mitotic count binary prediction model,
based on the VGG convolutional neural network. The model displayed a good predictive
performance.
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Affiliation(s)
- Jiejin Yang
- Department of Radiology, Peking University First Hospital, Peking University, Beijing, China
| | - Zeyang Chen
- Department of General Surgery, Peking University First Hospital, Peking University, Beijing, China
| | - Weipeng Liu
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Shuai Ma
- Department of Radiology, Peking University First Hospital, Peking University, Beijing, China
| | - Feifei Jin
- Department of Biostatistics, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Peking University, Beijing, China.
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Seven G, Kochan K, Caglar E, Kiremitci S, Koker IH, Senturk H. Evaluation of Ki67 Index in Endoscopic Ultrasound-Guided Fine Needle Aspiration Samples for the Assessment of Malignancy Risk in Gastric Gastrointestinal Stromal Tumors. Dig Dis 2020; 39:407-414. [PMID: 33017820 DOI: 10.1159/000511994] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 10/05/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND The risk of malignancy in resected gastrointestinal stromal tumors (GISTs) depends on tumor size, location, and mitotic index. Reportedly, the Ki67 index has a prognostic value in resected GISTs. We aimed to analyze the accuracy of endoscopic ultrasound (EUS)-guided fine needle aspiration (FNA) samples with reference to Ki67 index, using surgical specimens as the gold standard. METHODS Fifty-five patients who underwent EUS-FNA followed by surgical resection for gastric GISTs were retrospectively analyzed. Patients' age and sex; tumors' size and location; mitotic index, cell type, cellularity, pleomorphism, presence of ulceration, hemorrhage, necrosis, mucosal or serosal invasion, growth pattern, and Ki67 index based on pathology were investigated. RESULTS Location in fundus, ulceration, hemorrhage, mucosal invasion, and Ki67 index in surgical specimens were significant in predicting high-risk groups (p < 0.05) on univariate analysis. Frequency of bleeding (p = 0.034) and the Ki67 index (p = 0.018) were the only independent significant factors in multivariate analysis. The optimal cutoff level of Ki67 was 5%, with 88.2% sensitivity and 52.8% specificity (p = 0.021). The mean Ki67 index was lower in EUS-FNA samples than in surgical specimens (2% [1-15] versus 10% [1-70], p = 0.001). The rank correlation coefficient value of Ki67 was 0.199 (p = 0.362) between EUS-FNA and surgical samples and showed no reliability for EUS-FNA samples. CONCLUSION The Ki67 index in resected specimens correlated with high-risk GISTs, although it had no additive value to the current criteria. The Ki67 index in EUS-guided FNA samples is not a reliable marker of proliferation in GISTs.
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Affiliation(s)
- Gulseren Seven
- Division of Gastroenterology, Bezmialem Vakif University, Istanbul, Turkey
| | - Koray Kochan
- Division of Gastroenterology, Bezmialem Vakif University, Istanbul, Turkey
| | - Erkan Caglar
- Division of Gastroenterology, Balikesir University School of Medicine, Balikesir, Turkey
| | - Sercan Kiremitci
- Division of Gastroenterology, Bezmialem Vakif University, Istanbul, Turkey
| | | | - Hakan Senturk
- Division of Gastroenterology, Bezmialem Vakif University, Istanbul, Turkey
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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: 24] [Impact Index Per Article: 4.8] [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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Zhang H, Liu Q. Prognostic Indicators for Gastrointestinal Stromal Tumors: A Review. Transl Oncol 2020; 13:100812. [PMID: 32619820 PMCID: PMC7327422 DOI: 10.1016/j.tranon.2020.100812] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/25/2020] [Accepted: 05/27/2020] [Indexed: 02/08/2023] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are potentially malignancies that can occur anywhere in the digestive tract. Tyrosine kinase inhibitors (TKIs) such as imatinib have proven effective since the discovery of KIT and PDGFRA. The current version of NCNN, ESMO and EURACAN guidelines recognized that the three main prognostic factors are the mitotic rate, tumor size and tumor site. In addition, tumor rupture is also recognized as an independent risk factor. However, recent evidence shows that various types of gene mutations are associated with prognosis, and influencing factors such as gastrointestinal bleeding and high Ki67 index have been associated with poor prognosis. It shows that the current risk classification is still insufficient and controversial. With the emergence of more and more lack mutation in KIT/PDGFRA GISTs (KIT/PDGFRA wild-type GISTs) or drug resistance genes, primary and secondary drug resistance problems are caused, which makes the treatment of late or metastatic GIST face challenges. Therefore, this article will review the clinicopathological characteristics of GIST, the special molecular subtypes and other factors that may affect prognosis. We will also explore reliable prognostic markers for better postoperative management and improve the prognosis of patients with GIST.
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Affiliation(s)
- Haixin Zhang
- Department of Trauma center, The First Hospital of China Medical University, Shenyang, China
| | - Qi Liu
- Department of Trauma center, The First Hospital of China Medical University, Shenyang, China.
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Inoue A, Ota S, Nitta N, Murata K, Shimizu T, Sonoda H, Tani M, Ban H, Inatomi O, Ando A, Kushima R, Watanabe Y. Difference of computed tomographic characteristic findings between gastric and intestinal gastrointestinal stromal tumors. Jpn J Radiol 2020; 38:771-781. [PMID: 32246352 DOI: 10.1007/s11604-020-00962-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/24/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE We aimed to compare the computed tomography (CT) imaging differences between gastric and intestinal gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS Thirty-eight patients with 38 gastric GISTs and 27 with 31 intestinal GISTs were enrolled. Tumors were classified as small (< 5 cm) or large (≥ 5 cm). Qualitative and quantitative CT imaging characteristics on non-contrast and contrast-enhanced CT were evaluated by two radiologists independently and statistically compared. RESULTS Early venous return and higher CT number of the draining vein in the arterial phase were more frequent in small-sized intestinal GISTs than in small-sized gastric GISTs (p < 0.001). Small-sized intestinal GISTs demonstrated a wash-out pattern, whereas small-sized gastric GISTs showed a plateau pattern. Contrast enhancement was higher in small-sized intestinal GISTs than in small-sized gastric GISTs (p < 0.001). CT number was inversely proportional to the diameter of intestinal GISTs in both arterial and venous phases but not to that of gastric GISTs. CONCLUSION Strong enhancement with wash-out pattern and early venous return are characteristic findings of small-sized intestinal GISTs. Radiologists should be aware that CT findings of GISTs have a wide spectrum and may differ according to size and site of origin.
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Affiliation(s)
- Akitoshi Inoue
- Department of Radiology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan.
| | - Shinichi Ota
- Department of Radiology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Norihisa Nitta
- Department of Radiology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Kiyoshi Murata
- Department of Radiology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Tomoharu Shimizu
- Department of Surgery, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Hiromichi Sonoda
- Department of Surgery, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Masaji Tani
- Department of Surgery, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Hiromitsu Ban
- Department of Gastroenterology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Osamu Inatomi
- Department of Gastroenterology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Akira Ando
- Department of Gastroenterology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Ryoji Kushima
- Department of Pathology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
| | - Yoshiyuki Watanabe
- Department of Radiology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
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Rare Tumors GI Group, Farhat F, Farsi AA, Mohieldin A, Bahrani BA, Sbaity E, Jaffar H, Kattan J, Rasul K, Saad K, Assi T, Morsi WE, Abood RA. Comprehensive review into the challenges of gastrointestinal tumors in the Gulf and Levant countries. World J Clin Cases 2020; 8:487-503. [PMID: 32110658 PMCID: PMC7031830 DOI: 10.12998/wjcc.v8.i3.487] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 12/13/2019] [Accepted: 01/01/2020] [Indexed: 02/05/2023] Open
Abstract
Although gastrointestinal stromal tumors (GISTs) are rare, with an incidence of 1/100000 per year, they are the most common sarcomas in the peritoneal cavity. Despite considerable progress in the diagnosis and treatment of GIST, about half of all patients are estimated to experience recurrence. With only two drugs, sunitinib and regorafenib, approved by the Food and Drug Administration, selecting treatment options after imatinib failure and coordinating multidisciplinary care remain challenging. In addition, physicians across the Middle East face some additional and unique challenges such as lack of published local data from clinical trials, national disease registries and regional scientific research, limited access to treatment, lack of standardization of care, and limited access to mutational analysis. Although global guidelines set a framework for the management of GIST, there are no standard local guidelines to guide clinical practice in a resource-limited environment. Therefore, a group of 11 experienced medical oncologists from across the Gulf and Levant region, part of the Rare Tumors Gastrointestinal Group, met over a period of one year to conduct a narrative review of the management of GIST and to describe regional challenges and gaps in patient management as an essential step to proposing local clinical practice recommendations.
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Affiliation(s)
| | - Fadi Farhat
- Hammoud Hospital UMC, Saida PO Box 652, Lebanon
| | | | - Ahmed Mohieldin
- Medical Oncology Department, Kuwait Cancer Control Center, Kuwait PO Box 42262, Kuwait
| | - Bassim Al Bahrani
- Medical Oncology Department, Royal Hospital, Muscat PO Box 1331, Oman
| | - Eman Sbaity
- Division of General Surgery, American University of Beirut, Beirut 1107 2180, Lebanon
| | - Hassan Jaffar
- Oncology Department, Tawam Hospital, Al Ain PO Box 15258, United Arab Emirates
| | - Joseph Kattan
- Hemato-oncology Department, Hotel Dieu de France, Beirut, Lebanon
| | - Kakil Rasul
- Hemato-oncology Department, National Center for Cancer Care and Research, Doha, Qatar
| | - Khairallah Saad
- Pathology Department, Institute National de Pathologic, Beirut, Lebanon
| | - Tarek Assi
- Oncology Department, Faculty of Medicine, Saint-Joseph University, Beirut, Lebanon
| | - Waleed El Morsi
- Pfizer Oncology-Emerging Markets, Dubai Media City, Dubai, United Arab Emirates
| | - Rafid A Abood
- Oncology Department, Basra College of Medicine, Basra, Iraq
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Wei SC, Xu L, Li WH, Li Y, Guo SF, Sun XR, Li WW. Risk stratification in GIST: shape quantification with CT is a predictive factor. Eur Radiol 2020; 30:1856-1865. [PMID: 31900704 PMCID: PMC7062662 DOI: 10.1007/s00330-019-06561-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 10/19/2019] [Accepted: 10/30/2019] [Indexed: 12/13/2022]
Abstract
Background Tumor shape is strongly associated with some tumor’s genomic subtypes and patient outcomes. Our purpose is to find the relationship between risk stratification and the shape of GISTs. Methods A total of 101 patients with primary GISTs were confirmed by pathology and immunohistochemistry and underwent enhanced CT examination. All lesions’ pathologic sizes were 1 to 10 cm. Points A and B were the extremities of the longest diameter (LD) of the tumor and points C and D the extremities of the small axis, which was the longest diameter perpendicular to AB. The four angles of the quadrangle ABCD were measured and each angle named by its summit (A, B, C, D). For regular lesions, we took angles A and B as big angle (BiA) and small angle (SmA). For irregular lesions, we compared A/B ratio and D/C ratio and selected the larger ratio for analysis. The chi-square test, t test, ROC analysis, and hierarchical or binary logistic regression analysis were used to analyze the data. Results The BiA/SmA ratio was an independent predictor for risk level of GISTs (p = 0.019). With threshold of BiA at 90.5°, BiA/SmA ratio at 1.35 and LD at 6.15 cm, the sensitivities for high-risk GISTs were 82.4%, 85.3%, and 83.8%, respectively; the specificities were 87.1%, 71%, and 77.4%, respectively; and the AUCs were 0.852, 0.818, and 0.844, respectively. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could (p < 0.05). Shape and Ki-67 were independent predictors of the mitotic value (p = 0.036 and p < 0.001, respectively), and the accuracy was 87.8%. Conclusions Quantifying tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs, especially for high-risk grading and mitotic value > 5/50HPF. Key Points • The BiA/SmA ratio was an independent predictor affecting the risk level of GISTs. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could. • Shape and Ki-67 were independent predictors of the mitotic value. • The method for quantifying the tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs.
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Affiliation(s)
- Sheng-Cai Wei
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No 440 Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Liang Xu
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No 440 Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Wan-Hu Li
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No 440 Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Yun Li
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No 440 Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Shou-Fang Guo
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No 440 Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China
| | - Xiao-Rong Sun
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No 440 Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China.
| | - Wen-Wu Li
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No 440 Jiyan Road, Jinan, 250117, Shandong Province, People's Republic of China.
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Chen Z, Yang J, Sun J, Wang P. Gastric gastrointestinal stromal tumours (2-5 cm): Correlation of CT features with malignancy and differential diagnosis. Eur J Radiol 2019; 123:108783. [PMID: 31841880 DOI: 10.1016/j.ejrad.2019.108783] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 10/20/2019] [Accepted: 11/24/2019] [Indexed: 01/19/2023]
Abstract
PURPOSE The purpose of this study was to investigate the computed tomography (CT) features of 2-5 cm gastric gastrointestinal stromal tumors (GISTs), schwannomas and leimyomas which have close relationship with malignancy evaluation and differential diagnosis. METHOD Seventy-six patients with pathologically confirmed gastric submucosal tumors (SMTs) between 2-5 cm were included in this study, including 60 GISTs, 10 schwannomas and 6 leiomyomas. CT imaging features were reviewed and quantitative parameters including CT values during nonenhanced phase (CTV-N), portal phase (CTV-P) and delayed phase (CTV-D) were recorded. The association of CT features with mitotic counts of GISTs and the significantly different CT features between GISTs and benign SMTs were analyzed. RESULTS The lobulated contour was significantly more common in GISTs with high mitoses (P < 0.05). The value of CTV-D/CTV-P was significantly lower in GISTs with high mitoses (P < 0.05) and it was an independent predictor for high-mitosis GISTs (P = 0.049, odds ratio [OR] = 186.445) with an area under the curve (AUC) of 0.722. CT features including heterogeneous enhancement and presence of necrosis or cystic degeneration were significantly suggestive of GISTs instead of benign SMTs (P < 0.05). The value of CTV-D/CTV-P was significantly higher in schwannomas than that in GISTs (P < 0.05) with an AUC of 0.853. The value of CTV-P/CTV-N was significantly lower in leiomyomas than that in GISTs (P < 0.05) with an AUC of 0.883. CONCLUSIONS Some qualitative and quantitative parameters on contrast-enhanced CT can be helpful in preoperative diagnosis and risk stratification of 2-5 cm gastric GISTs.
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Affiliation(s)
- Zeyang Chen
- Department of General Surgery, Peking University First Hospital, Peking University, 8 Xi ShiKu Street, Beijing 100034, People's Republic of China
| | - Jiejin Yang
- Department of Radiology, Peking University First Hospital, Peking University, 8 Xi ShiKu Street, Beijing 100034, People's Republic of China
| | - Jiali Sun
- Department of Radiology, Peking University First Hospital, Peking University, 8 Xi ShiKu Street, Beijing 100034, People's Republic of China
| | - Pengyuan Wang
- Department of General Surgery, Peking University First Hospital, Peking University, 8 Xi ShiKu Street, Beijing 100034, People's Republic of China.
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Ylä-Outinen H, Loponen N, Kallionpää RA, Peltonen S, Peltonen J. Intestinal tumors in neurofibromatosis 1 with special reference to fatal gastrointestinal stromal tumors (GIST). Mol Genet Genomic Med 2019; 7:e927. [PMID: 31397088 PMCID: PMC6732307 DOI: 10.1002/mgg3.927] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 07/23/2019] [Accepted: 07/26/2019] [Indexed: 12/18/2022] Open
Abstract
Background Type 1 neurofibromatosis (NF1) is a genetic tumor predisposing Rasopathy. NF1 patients have an increased risk for developing benign and malignant tumors, but the occurrence of intestinal tumors has not been investigated at the population level. Methods In this retrospective register‐based total population study, diagnoses of gastrointestinal tract tumors were retrieved from the Finnish Care Register for Health Care for 1,410 NF1 patients and 14,030 reference persons. We also reviewed the death certificates of 232 NF1 patients who died during years 1987–2013, and specifically searched for diagnosis of gastrointestinal stromal tumor (GIST). Results The register analysis revealed an increased overall hazard ratio (HR) of 2.6 (95% CI 1.9–3.6) for intestinal tumors in NF1 compared to general population. The highest HR of 15.6 (95% CI 6.9–35.1) was observed in the small intestine. The focused analysis of NF1 death certificates and GISTs demonstrated that the GIST was the primary cause of death in seven patients. Conclusion This study emphasizes the need for careful evaluation of NF1 patients with gastrointestinal complaints. The challenge in diagnosis is that the tumors preferably occur at the small intestine, which is difficult target for diagnostic procedures. We also show that the NF1 GISTs may lead to fatal outcome despite of benign histopathological findings at the time of the diagnosis.
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Affiliation(s)
- Heli Ylä-Outinen
- Department of Cell Biology and Anatomy, University of Turku, Turku, Finland.,Department of Pulmonary Diseases, Turku University Hospital, Turku, Finland
| | - Niina Loponen
- Department of Cell Biology and Anatomy, University of Turku, Turku, Finland
| | - Roope A Kallionpää
- Department of Cell Biology and Anatomy, University of Turku, Turku, Finland
| | - Sirkku Peltonen
- Department of Dermatology, Turku University Hospital, Turku, Finland
| | - Juha Peltonen
- Department of Cell Biology and Anatomy, University of Turku, Turku, Finland
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Sato N, Masuda N, Morimoto T, Ueno T, Kanbayashi C, Kaneko K, Yasojima H, Saji S, Sasano H, Morita S, Ohno S, Toi M. Neoadjuvant exemestane or exemestane plus docetaxel and cyclophosphamide tailored by clinicopathological response to 12 weeks' exemestane exposure in patients with estrogen receptor-positive breast cancer: A multicenter, open-label, phase II study. Cancer Med 2019; 8:5468-5481. [PMID: 31361400 PMCID: PMC6745863 DOI: 10.1002/cam4.2423] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 06/28/2019] [Accepted: 07/03/2019] [Indexed: 12/21/2022] Open
Abstract
Our aim was to investigate the efficacy and safety of initial neoadjuvant endocrine therapy with exemestane alone followed by tailored treatment, either continued exemestane monotherapy or exemestane plus docetaxel–cyclophosphamide (TC) combination therapy, in postmenopausal patients with primary invasive estrogen receptor–positive, human epidermal growth factor receptor 2–negative, stage I‐IIIA breast cancer and Ki67 labeling index ≤30%. In this open‐label phase II study, patients initially received exemestane 25 mg/d for 12 weeks. Responders were defined as patients who achieved complete response (CR), partial response (PR) with Ki67 labeling index ≤5% after treatment, or stable disease with Ki67 labeling index ≤5% both before and after treatment. For the subsequent 12 weeks, exemestane monotherapy was continued for responders (group A), whereas nonresponders received exemestane plus four cycles of TC (docetaxel 75 mg/m2 and cyclophosphamide 600 mg/m2 every 3 weeks) (group B). Clinical response rate (ie the proportion of patients with CR or PR) at 24 weeks was the primary endpoint. Of 64 patients provisionally enrolled between December 2010 and May 2016, 58 (median age 60 years) started the study treatment. Five patients discontinued treatment in the initial exemestane monotherapy period, and 39 completed the study treatment. Clinical response rates at 8‐12 and 24 weeks were 71% (10/14, 95% confidence interval [CI] 41.9%‐91.6%) and 57% (8/14, 95% CI 28.9%‐82.3%), respectively, in group A, and 16% (4/25, 95% CI 4.5%‐36.1%) and 56% (14/25, 95% CI 34.9%‐75.6%), respectively, in group B. Grade ≥3 adverse events were reported in 8% (1/15) and 53% (20/38) in group A and group B, respectively. The tailored treatment maintained the favorable clinical response to exemestane alone in responders and improved clinical response in nonresponders. Trial number UMIN000004752 (UMIN Clinical Trials Registry).
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Affiliation(s)
- Nobuaki Sato
- Department of Breast Oncology, Niigata Cancer Center Hospital, Niigata, Japan
| | - Norikazu Masuda
- Department of Surgery, Breast Oncology, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Takashi Morimoto
- Department of Breast Surgery, Yao Municipal Hospital, Osaka, Japan
| | - Takayuki Ueno
- Breast Surgical Oncology, Breast Oncology Center, Cancer Institute Hospital, Tokyo, Japan
| | - Chizuko Kanbayashi
- Department of Breast Oncology, Niigata Cancer Center Hospital, Niigata, Japan
| | - Koji Kaneko
- Department of Breast Oncology, Niigata Cancer Center Hospital, Niigata, Japan
| | - Hiroyuki Yasojima
- Department of Surgery, Breast Oncology, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Shigehira Saji
- Department of Medical Oncology, Fukushima Medical University, Fukushima, Japan
| | | | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shinji Ohno
- Breast Oncology Center, Cancer Institute Hospital, Tokyo, Japan
| | - Masakazu Toi
- Department of Surgery (Breast Surgery), Graduate School of Medicine, Kyoto University, Kyoto, Japan
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