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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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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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Liu X, Han T, Wang Y, Liu H, Deng J, Xue C, Li S, Zhou J. Prediction of Ki-67 expression in gastric gastrointestinal stromal tumors using histogram analysis of monochromatic and iodine images derived from spectral CT. Cancer Imaging 2024; 24:173. [PMID: 39741326 DOI: 10.1186/s40644-024-00820-6] [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: 03/21/2024] [Accepted: 12/27/2024] [Indexed: 01/02/2025] Open
Abstract
PURPOSE To assess and compare the diagnostic efficiency of histogram analysis of monochromatic and iodine images derived from spectral CT in predicting Ki-67 expression in gastric gastrointestinal stromal tumors (gGIST). METHODS Sixty-five patients with gGIST who underwent spectral CT were divided into a low-level Ki-67 expression group (LEG, Ki-67 < 10%, n = 33) and a high-level Ki-67 expression group (HEG, Ki-67 ≥ 10%, n = 32). Conventional CT features were extracted and compared. Histogram parameters were extracted from monochromatic and iodine images, respectively. The diagnostic efficiency of the histogram parameters from monochromatic and iodine images was assessed and compared between the two groups. Spearman's correlation analysis was used to correlate histogram parameters with Ki-67 expression. RESULTS The HEG was more likely to present with an irregular shape and a larger size than the LEG (all p < 0.05). Regarding histogram parameters, the HEG showed higher maximum, mean, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.99, SD, variance, and CV of monochromatic images; higher maximum, Perc.99, and entropy of iodine images, compared with the LEG (all p < 0.003125). ROC analysis showed that significant histogram parameters of monochromatic and iodine images allowed for effective differentiation between LEG and HEG. DeLong's test showed that the diagnostic efficiency of histogram parameters in monochromatic images (Perc.90) was superior to that of iodine images (maximum) (p = 0.010). A positive correlation was observed between the significant histogram parameters and Ki-67 expression (all p < 0.05). CONCLUSION Both histogram analysis of monochromatic and iodine images derived from spectral CT can predict Ki-67 expression in gGIST, and the diagnostic efficacy of monochromatic images is superior to iodine images.
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Affiliation(s)
- Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Yuzhu Wang
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
| | - Hong Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
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Yang P, Wu J, Liu M, Zheng Y, Zhao X, Mao Y. Preoperative CT-based radiomics and deep learning model for predicting risk stratification of gastric gastrointestinal stromal tumors. Med Phys 2024; 51:7257-7268. [PMID: 38935330 DOI: 10.1002/mp.17276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/21/2024] [Accepted: 06/16/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively. PURPOSE To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively. METHODS The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed. RESULTS The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891). CONCLUSION In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.
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Affiliation(s)
- Ping Yang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiamei Wu
- Department of Radiology, Chongqing Dongnan Hospital, Chongqing, China
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaofang Zhao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Mao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Barat M, Pellat A, Terris B, Dohan A, Coriat R, Fishman EK, Rowe SP, Chu L, Soyer P. Cinematic Rendering of Gastrointestinal Stromal Tumours: A Review of Current Possibilities and Future Developments. Can Assoc Radiol J 2024; 75:359-368. [PMID: 37982314 DOI: 10.1177/08465371231211278] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023] Open
Abstract
Gastrointestinal stromal tumours (GISTs) are defined as CD117-positive primary, spindled or epithelioid, mesenchymal tumours of the gastrointestinal tract, omentum, or mesentery. While computed tomography (CT) is the recommended imaging modality for GISTs, overlap in imaging features between GISTs and other gastrointestinal tumours often make radiological diagnosis and subsequent selection of the optimal therapeutic approach challenging. Cinematic rendering is a novel CT post-processing technique that generates highly photorealistic anatomic images based on a unique lighting model. The global lighting model produces high degrees of surface detail and shadowing effects that generate depth in the final three-dimensional display. Early studies have shown that cinematic rendering produces high-quality images with enhanced detail by comparison with other three-dimensional visualization techniques. Cinematic rendering shows promise in improving the visualization of enhancement patterns and internal architecture of abdominal lesions, local tumour extension, and global disease burden, which may be helpful for lesion characterization and pretreatment planning. This article discusses and illustrates the application of cinematic rendering in the evaluation of GISTs and the unique benefit of using cinematic rendering in the workup of GIST with a specific emphasis on tumour characterization and preoperative planning.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 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, Assistance Publique-Hopitaux de Paris, Paris, France
| | - Benoit Terris
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Pathology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Steven P Rowe
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
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Zhao L, Cao G, Shi Z, Xu J, Yu H, Weng Z, Mao S, Chen Y. Preoperative differentiation of gastric schwannomas and gastrointestinal stromal tumors based on computed tomography: a retrospective multicenter observational study. Front Oncol 2024; 14:1344150. [PMID: 38505598 PMCID: PMC10948459 DOI: 10.3389/fonc.2024.1344150] [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: 11/25/2023] [Accepted: 02/19/2024] [Indexed: 03/21/2024] Open
Abstract
Introduction Gastric schwannoma is a rare benign tumor accounting for only 1-2% of alimentary tract mesenchymal tumors. Owing to their low incidence rate, most cases are misdiagnosed as gastrointestinal stromal tumors (GISTs), especially tumors with a diameter of less than 5 cm. Therefore, this study aimed to develop and validate a diagnostic nomogram based on computed tomography (CT) imaging features for the preoperative prediction of gastric schwannomas and GISTs (diameters = 2-5 cm). Methods Gastric schwannomas in 47 patients and GISTs in 230 patients were confirmed by surgical pathology. Thirty-four patients with gastric schwannomas and 167 with GISTs admitted between June 2009 and August 2022 at Hospital 1 were retrospectively analyzed as the test and training sets, respectively. Seventy-six patients (13 with gastric schwannomas and 63 with GISTs) were included in the external validation set (June 2017 to September 2022 at Hospital 2). The independent factors for differentiating gastric schwannomas from GISTs were obtained by multivariate logistic regression analysis, and a corresponding nomogram model was established. The accuracy of the nomogram was evaluated using receiver operating characteristic and calibration curves. Results Logistic regression analysis showed that the growth pattern (odds ratio [OR] 3.626; 95% confidence interval [CI] 1.105-11.900), absence of necrosis (OR 4.752; 95% CI 1.464-15.424), presence of tumor-associated lymph nodes (OR 23.978; 95% CI 6.499-88.466), the difference between CT values during the portal and arterial phases (OR 1.117; 95% CI 1.042-1.198), and the difference between CT values during the delayed and portal phases (OR 1.159; 95% CI 1.080-1.245) were independent factors in differentiating gastric schwannoma from GIST. The resulting individualized prediction nomogram showed good discrimination in the training (area under the curve [AUC], 0.937; 95% CI, 0.900-0.973) and validation (AUC, 0.921; 95% CI, 0.830-1.000) datasets. The calibration curve showed that the probability of gastric schwannomas predicted using the nomogram agreed well with the actual value. Conclusion The proposed nomogram model based on CT imaging features can be used to differentiate gastric schwannoma from GIST before surgery.
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Affiliation(s)
- Luping Zhao
- Department of Medical Imaging, The Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Guanjie Cao
- Department of Medical Imaging, The Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Zhitao Shi
- Department of Medical Imaging, The Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Jingjing Xu
- Department of Medical Imaging, The Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Hao Yu
- Department of Medical Imaging, The Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Zecan Weng
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Sen Mao
- Department of Ultrasound, The Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Yueqin Chen
- Department of Medical Imaging, The Affiliated Hospital of Jining Medical University, Jining, Shandong, 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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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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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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Wu J, Zhuang M, Zhou Y, Zhan X, Xie W. The value of contrast-enhanced harmonic endoscopic ultrasound in differential diagnosis and evaluation of malignant risk of gastrointestinal stromal tumors (<50mm). Scand J Gastroenterol 2022; 58:542-548. [PMID: 36369879 DOI: 10.1080/00365521.2022.2144437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Contrast-enhanced harmonic endoscopic ultrasound (CEH-EUS) has been used in the differential diagnosis of benign and malignant tumors by visualization of tumor microcirculation and perfusion. However, its diagnostic role in submucosal tumors (SMTs), especially leiomyomas and gastric submucosal tumors (GISTs) was rarely studied. The aim of this study was to analyze the diagnostic role of CEH-EUS for SMTs (<50 mm) and the value of assessing the malignant potential of GISTs. MATERIALS AND METHODS We retrospectively included patients with tumors <50 mm in diameter who underwent preoperative EUS and CEH-EUS examination and had pathologically confirmed as leiomyomas and GISTs. To analyze the imaging features of CEH-EUS with pathological diagnosis as the gold standard and evaluate its diagnostic value. RESULTS This study included 10 cases of leiomyomas and 38 cases of GISTs. Under CEH-EUS detection, 86.9% of GISTs showed hyper-enhancement, 89.5% showed diffuse enhancement, 39.5% showed non-enhancing spots, and 97.4% showed obvious capsule enhancement. In contrast, the leiomyoma cases mostly showed hypo-enhancement (50.0%) or non-enhancement (30.0%) (p < 0.05). Then, the value of CEH-EUS in the differential diagnosis of benign and malignant tumors based on blood flow is significantly higher than that of B-EUS. Signal appearance time was significantly faster in the intermediate-high risk GISTs than in the very low-low risk group (5.1 s versus 15.5 s, p < 0.05), and the AUROC values predicted the risk at this time to be 0.903 (0.763-0.975). Heterogeneous perfusion and non-enhancing spots were also more common in the intermediate-high risk group. Univariate and multivariate analysis revealed that intratumoral irregularitie was an independent predictor of moderate to high risk (OR 3.99, 95%CI 1.04-90.95), with sensitivity, specificity and accuracy of 73.33%, 91.30% and 84.21%, respectively. CONCLUSIONS Through this study, CEH-EUS has a good differential diagnostic ability for leiomyomas and GISTs, and has a high value in predicting the risk of GISTs.
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Affiliation(s)
- Jiali Wu
- Department of Gastroenterology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Mengqi Zhuang
- Department of Gastroenterology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Yubao Zhou
- Department of Gastroenterology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Xiang Zhan
- Department of Gastroenterology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Weiwei Xie
- Department of Gastroenterology, The Second Hospital of Anhui Medical University, Hefei, China
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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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12
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Preoperative paraspinal and psoas major muscle atrophy and paraspinal muscle fatty degeneration as factors influencing the results of surgical treatment of lumbar disc disease. Arch Orthop Trauma Surg 2022; 142:1375-1384. [PMID: 33484312 DOI: 10.1007/s00402-021-03754-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 01/01/2021] [Indexed: 01/06/2023]
Abstract
INTRODUCTION There is a growing number of publications highlighting sarcopenia and myosteatosis as poor prognosic factors for treatment results in oncological patients. The decrease in the cross-sectional area (CSA) of the multifidus muscle and muscle steatosis is associated with lumbar disc herniation and low back/limb pain. Nevertheless, no studies have analyzed the influence of the above parameters on patient satisfaction, pain decrease and return to daily activities. The aim of the study was to verify whether decreased preoperative CSA of the paraspinal and psoas major muscles and their fatty degeneration (myosteatosis) may influence the outcome of surgical treatment of lumbar disc disease (LDD). MATERIALS AND METHODS One hundred and one patients with LDD undergoing open microdiscectomy were enrolled in the analysis. Relative cross-sectional areas (rCSA) of the paraspinal and psoas major muscles as well as their fatty degeneration were measured. Patients were assessed according to the validated Polish versions of the EURO EQ-5D, Core Outcome Measure Index (COMI), Oswestry Disability Index (ODI) and Visual Analog Scale (VAS) 1 and 6 months postoperatively. The association between the variables was calculated using Pearson r and Spearman rank correlation. The Kruskal-Wallis test was used to compare the results between the groups with different rCSA of paraspinal and psoas major muscles and a different degree of paraspinal muscle myosteatosis. RESULTS Fatty degeneration of the paraspinal muscles correlated with better outcomes 1 and 6 months postoperatively according to ODI (P = 0.003 and P = 0.027, respectively). Patients with higher rCSA of the paraspinal and psoas major muscles achieved better results on the EURO EQ-5D scale (P = 0.0289 and P = 0.0089, respectively). Higher rCSA of the paraspinal and psoas major muscles did not correlate with better outcomes measured using ODI, COMI and VAS scales (P ≥ 0.072). CONCLUSION The degree of fatty degeneration of the paraspinal muscles correlates with better outcomes 1 and 6 months after microdiscectomy.
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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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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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