1
|
Yin XN, Wang ZH, Zou L, Yang CW, Shen CY, Liu BK, Yin Y, Liu XJ, Zhang B. Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor. World J Gastrointest Oncol 2024; 16:1296-1308. [PMID: 38660646 PMCID: PMC11037038 DOI: 10.4251/wjgo.v16.i4.1296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/23/2024] [Accepted: 02/25/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. AIM To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. METHODS A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. RESULTS The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. CONCLUSION Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.
Collapse
Affiliation(s)
- Xiao-Nan Yin
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zi-Hao Wang
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Li Zou
- Department of Paediatric Surgery, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Cai-Wei Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Chao-Yong Shen
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bai-Ke Liu
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yuan Yin
- Gastric Cancer Research Center, 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
| | - Bo Zhang
- Department of Gastrointestinal Surgery, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
| |
Collapse
|
2
|
Hu L, Jin P, Xu W, Wang C, Huang P. Clinical and radiomics integrated nomogram for preoperative prediction of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer. Front Oncol 2024; 14:1370466. [PMID: 38567151 PMCID: PMC10985173 DOI: 10.3389/fonc.2024.1370466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Objectives The present study aimed to develop a radiomics nomogram based on conventional ultrasound (CUS) to preoperatively distinguish high tumor-infiltrating lymphocytes (TILs) and low TILs in triple-negative breast cancer (TNBC) patients. Methods In the present study, 145 TNBC patients were retrospectively included. Pathological evaluation of TILs in the hematoxylin and eosin sections was set as the gold standard. The patients were randomly allocated into training dataset and validation dataset with a ratio of 7:3. Clinical features (age and CUS features) and radiomics features were collected. Then, the Rad-score model was constructed after the radiomics feature selection. The clinical features model and clinical features plus Rad-score (Clin+RS) model were built using logistic regression analysis. Furthermore, the performance of the models was evaluated by analyzing the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results Univariate analysis and LASSO regression were employed to identify a subset of 25 radiomics features from a pool of 837 radiomics features, followed by the calculation of Rad-score. The Clin+RS integrated model, which combined posterior echo and Rad-score, demonstrated better predictive performance compared to both the Rad-score model and clinical model, achieving AUC values of 0.848 in the training dataset and 0.847 in the validation dataset. Conclusion The Clin+RS integrated model, incorporating posterior echo and Rad-score, demonstrated an acceptable preoperative evaluation of the TIL level. The Clin+RS integrated nomogram holds tremendous potential for preoperative individualized prediction of the TIL level in TNBC.
Collapse
Affiliation(s)
- Ling Hu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Ultrasound in Medicine, Hangzhou Women’s Hospital, Hangzhou, Zhejiang, China
| | - Peile Jin
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China
| | - Wen Xu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China
| | - Chao Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China
| |
Collapse
|
3
|
Zhang C, Wang C, Mao G, Cheng G, Ji H, He L, Yang Y, Hu H, Wang J. Radiomics analysis of contrast-enhanced computerized tomography for differentiation of gastric schwannomas from gastric gastrointestinal stromal tumors. J Cancer Res Clin Oncol 2024; 150:87. [PMID: 38336926 PMCID: PMC10858083 DOI: 10.1007/s00432-023-05545-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/20/2023] [Indexed: 02/12/2024]
Abstract
PURPOSE To assess the performance of radiomics-based analysis of contrast-enhanced computerized tomography (CE-CT) images for distinguishing GS from gastric GIST. METHODS Forty-nine patients with GS and two hundred fifty-three with GIST were enrolled in this retrospective study. CT features were evaluated by two associate chief radiologists. Radiomics features were extracted from portal venous phase images using Pyradiomics software. A non-radiomics dataset (combination of clinical characteristics and radiologist-determined CT features) and a radiomics dataset were used to build stepwise logistic regression and least absolute shrinkage and selection operator (LASSO) logistic regression models, respectively. Model performance was evaluated according to sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve, and Delong's test was applied to compare the area under the curve (AUC) between different models. RESULTS A total of 1223 radiomics features were extracted from portal venous phase images. After reducing dimensions by calculating Pearson correlation coefficients (PCCs), 20 radiomics features, 20 clinical characteristics + CT features were used to build the models, respectively. The AUC values for the models using radiomics features and those using clinical features were more than 0.900 for both the training and validation groups. There were no significant differences in predictive performance between the radiomic and clinical data models according to Delong's test. CONCLUSION A radiomics-based model applied to CE-CT images showed comparable predictive performance to senior physicians in the differentiation of GS from GIST.
Collapse
Affiliation(s)
- Cui Zhang
- Department of Radiology, TongDe Hospital of ZheJiang Province, No. 234, Gucui Road, Hangzhou, 310013, Zhejiang, China
| | - Chongwei Wang
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guoqun Mao
- Department of Radiology, TongDe Hospital of ZheJiang Province, No. 234, Gucui Road, Hangzhou, 310013, Zhejiang, China
| | | | - Hongli Ji
- Jianpei Technology, Hangzhou, Zhejiang, China
| | - Linyang He
- Jianpei Technology, Hangzhou, Zhejiang, China
| | - Yang Yang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jian Wang
- Department of Radiology, TongDe Hospital of ZheJiang Province, No. 234, Gucui Road, Hangzhou, 310013, Zhejiang, China.
| |
Collapse
|
4
|
Guo C, Zhou H, Chen X, Feng Z. Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors. Heliyon 2023; 9:e20983. [PMID: 37876490 PMCID: PMC10590931 DOI: 10.1016/j.heliyon.2023.e20983] [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: 02/21/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/26/2023] Open
Abstract
Background KIT exon 11 mutation in gastrointestinal stromal tumors (GISTs) is associated with treatment strategies. However, few studies have shown the role of imaging-based texture analysis in KIT exon 11 mutation in GISTs. In this study, we aimed to show the association between computed tomography (CT)-based texture features and KIT exon 11 mutation. Methods Ninety-five GISTs confirmed by surgery and identified with mutational genotype of KIT were included in this study. By amplifying the samples using over-sampling technique, a total of 183 region of interest (ROI) segments were extracted from 63 patients as training cohort. The 63 new ROI segments were extracted from the 63 patients as internal validation cohort. Thirty-two patients who underwent KIT exon 11 mutation test during 2021-2023 was selected as external validation cohort. The textural parameters were evaluated both in training cohort and validation cohort. Least absolute shrinkage and selection operator (LASSO) algorithms and logistic regression analysis were used to select the discriminant features. Results Three of textural features were obtained using LASSO analysis. Logistic regression analysis showed that patients' age, tumor location and radiomics features were significantly associated with KIT exon 11 mutation (p < 0.05). A nomogram was developed based on the associated factors. The area under the curve (AUC) of clinical features, radiomics features and their combination in training cohort was 0.687 (95 % CI: 0.604-0.771), 0.829 (95 % CI: 0.768-0.890) and 0.874 (95 % CI: 0.822-0.926), respectively. The AUC of radiomics features in internal validation cohort and external cohort was 0.880 (95 % CI: 0.796-0.964) and 0.827 (95%CI: 0.667-0.987), respectively. Conclusion The CT texture-based model can be used to predict KIT exon 11 mutation in GISTs.
Collapse
Affiliation(s)
- Chuangen Guo
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China
| | - Hao Zhou
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, 155 hanzhong Road, Nanjing, 210029, China
| | - Xiao Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, 155 hanzhong Road, Nanjing, 210029, China
| | - Zhan Feng
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China
| |
Collapse
|
5
|
Galluzzo A, Boccioli S, Danti G, De Muzio F, Gabelloni M, Fusco R, Borgheresi A, Granata V, Giovagnoni A, Gandolfo N, Miele V. Radiomics in gastrointestinal stromal tumours: an up-to-date review. Jpn J Radiol 2023; 41:1051-1061. [PMID: 37171755 DOI: 10.1007/s11604-023-01441-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: 03/02/2023] [Accepted: 04/29/2023] [Indexed: 05/13/2023]
Abstract
Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.
Collapse
Affiliation(s)
- Antonio Galluzzo
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Sofia Boccioli
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, Pisa, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126, Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria Delle Marche", Via Conca 71, 60126, Ancona, Italy
| | - Vincenza Granata
- Department of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione, Pascale-IRCCS di Napoli", 80131, Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126, Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria Delle Marche", Via Conca 71, 60126, Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149, Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| |
Collapse
|
6
|
Zhang Y, Yue X, Zhang P, Zhang Y, Wu L, Diao N, Ma G, Lu Y, Ma L, Tao K, Li Q, Han P. Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study. Front Oncol 2023; 13:1193010. [PMID: 37645430 PMCID: PMC10461453 DOI: 10.3389/fonc.2023.1193010] [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: 03/28/2023] [Accepted: 07/26/2023] [Indexed: 08/31/2023] Open
Abstract
Objective gastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs. Methods Overall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1. Results The Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621-0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore. Conclusion The radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance.
Collapse
Affiliation(s)
- Yu Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiaofei Yue
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Peng Zhang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuying Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Linxia Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Nan Diao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Guina Ma
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yuting Lu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ling Ma
- He Kang Corporate Management (SH) Co. Ltd., Shanghai, China
| | - Kaixiong Tao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| |
Collapse
|
7
|
Lin JX, Wang FH, Wang ZK, Wang JB, Zheng CH, Li P, Huang CM, Xie JW. Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01637-2. [PMID: 37148481 DOI: 10.1007/s11547-023-01637-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/21/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVE The objective is to develop a mitotic prediction model and preoperative risk stratification nomogram for gastrointestinal stromal tumor (GIST) based on computed tomography (CT) radiomic features. METHODS A total of 267 GIST patients from 2009.07 to 2015.09 were retrospectively collected and randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest was delineated from the portal-phase images on contrast-enhanced (CE)-CT, and radiomic features were extracted. Lasso regression method was used to select valuable features to establish a radiomic model for predicting mitotic index in GIST. Finally, the nomogram of preoperative risk stratification was constructed by combining the radiomic features and clinical risk factors. RESULTS Four radiomic features closely related to the level of mitosis were obtained, and a mitotic radiomic model was constructed. The area under the curve (AUC) of the radiomics signature model used to predict mitotic levels in training and validation cohorts (training cohort AUC = 0.752; 95% confidence interval [95%CI] 0.674-0.829; validation cohort AUC = 0.764; 95% CI 0.667-0.862). Finally, the preoperative risk stratification nomogram combining radiomic features was equivalent to the clinically recognized gold standard AUC (0.965 vs. 0.983) (p = 0.117). The Cox regression analysis found that the nomogram score was one of the independent risk factors for the long-term prognosis of the patients. CONCLUSION Preoperative CT radiomic features can effectively predict the level of mitosis in GIST, and combined with preoperative tumor size, accurate preoperative risk stratification can be performed to guide clinical decision-making and individualized treatment.
Collapse
Affiliation(s)
- Jian-Xian Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Fu-Hai Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zu-Kai Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Bin Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chao-Hui Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ping Li
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chang-Ming Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China.
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China.
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Jian-Wei Xie
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China.
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China.
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
Yang L, Du D, Zheng T, Liu L, Wang Z, Du J, Yi H, Cui Y, Liu D, Fang Y. Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI. Front Oncol 2022; 12:948557. [PMID: 36505814 PMCID: PMC9727176 DOI: 10.3389/fonc.2022.948557] [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: 05/20/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Preoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction. Methods A total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development (n = 81) and test (n = 31) sets based on the time of diagnosis. With the use of T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map, a convolutional neural network (CNN)-based classifier was developed for MI prediction, which used a hybrid approach based on 2D tumor images and radiomics features from 3D tumor shape. The trained model was tested on an internal test set. Then, the hybrid model was comprehensively tested and compared with the conventional ResNet, shape radiomics classifier, and age plus diameter classifier. Results The hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927-0.968), 0.964 (95% CI: 0.930-0.978), and 90.8 (95% CI: 88.0-93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828-1.000), 0.941 (95% CI: 0.792-1.000), and 93.6% (95% CI: 79.3-98.2) in the test set, respectively. Discussion The deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.
Collapse
Affiliation(s)
- Linsha Yang
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Dan Du
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Tao Zheng
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Lanxiang Liu
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Zhanqiu Wang
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Juan Du
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Huiling Yi
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Yujie Cui
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Defeng Liu
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China,*Correspondence: Defeng Liu, ; Yuan Fang,
| | - Yuan Fang
- Medical Imaging Center, Chongqing Yubei District People’s Hospital, Chongqing, China,*Correspondence: Defeng Liu, ; Yuan Fang,
| |
Collapse
|
10
|
Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features. Radiol Med 2022; 127:1085-1097. [DOI: 10.1007/s11547-022-01549-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/18/2022] [Indexed: 10/14/2022]
|
11
|
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: 1.0] [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.
Collapse
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.
| |
Collapse
|
12
|
Zhuo M, Guo J, Tang Y, Tang X, Qian Q, Chen Z. Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors. Front Oncol 2022; 12:905036. [PMID: 36091148 PMCID: PMC9459166 DOI: 10.3389/fonc.2022.905036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 08/09/2022] [Indexed: 11/24/2022] Open
Abstract
This study aimed to develop and evaluate a nomogram based on an ultrasound radiomics model to predict the risk grade of gastrointestinal stromal tumors (GISTs). 216 GIST patients pathologically diagnosed between December 2016 and December 2021 were reviewed and divided into a training cohort (n = 163) and a validation cohort (n = 53) in a ratio of 3:1. The tumor region of interest was depicted on each patient’s ultrasound image using ITK-SNAP, and the radiomics features were extracted. By filtering unstable features and using Spearman’s correlation analysis, and the least absolute shrinkage and selection operator algorithm, a radiomics score was derived to predict the malignant potential of GISTs. a radiomics nomogram that combines the radiomics score and clinical ultrasound predictors was constructed and assessed in terms of calibration, discrimination, and clinical usefulness. The radiomics score from ultrasound images was significantly associated with the malignant potential of GISTs. The radiomics nomogram was superior to the clinical ultrasound nomogram and the radiomics score, and it achieved an AUC of 0.90 in the validation cohort. Based on the decision curve analysis, the radiomics nomogram was found to be more clinically significant and useful. A nomogram consisting of radiomics score and the maximum tumor diameter demonstrated the highest accuracy in the prediction of risk grade in GISTs. The outcomes of our study provide vital insights for important preoperative clinical decisions.
Collapse
|
13
|
Wang Y, Wang Y, Ren J, Jia L, Ma L, Yin X, Yang F, Gao BL. Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center study. Front Oncol 2022; 12:966743. [PMID: 36052224 PMCID: PMC9425090 DOI: 10.3389/fonc.2022.966743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 07/25/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose This study was to investigate the diagnostic efficacy of radiomics models based on the enhanced CT images in differentiating the malignant risk of gastrointestinal stromal tumors (GIST) in comparison with the clinical indicators model and traditional CT diagnostic criteria. Materials and methods A total of 342 patients with GISTs confirmed histopathologically were enrolled from five medical centers. Data of patients wrom two centers comprised the training group (n=196), and data from the remaining three centers constituted the validation group (n=146). After CT image segmentation and feature extraction and selection, the arterial phase model and venous phase model were established. The maximum diameter of the tumor and internal necrosis were used to establish a clinical indicators model. The traditional CT diagnostic criteria were established for the classification of malignant potential of tumor. The performance of the four models was assessed using the receiver operating characteristics curve. Reuslts In the training group, the area under the curves(AUCs) of the arterial phase model, venous phase model, clinical indicators model, and traditional CT diagnostic criteria were 0.930 [95% confidence interval (CI): 0.895-0.965), 0.933 (95%CI 0.898-0.967), 0.917 (95%CI 0.872-0.961) and 0.782 (95%CI 0.717-0.848), respectively. In the validation group, the AUCs of the models were 0.960 (95%CI 0.930-0.990), 0.961 (95% CI 0.930-0.992), 0.922 (95%CI 0.884-0.960) and 0.768 (95%CI 0.692-0.844), respectively. No significant difference was detected in the AUC between the arterial phase model, venous phase model, and clinical indicators model by the DeLong test, whereas a significant difference was observed between the traditional CT diagnostic criteria and the other three models. Conclusion The radiomics model using the morphological features of GISTs play a significant role in tumor risk stratification and can provide a reference for clinical diagnosis and treatment plan.
Collapse
Affiliation(s)
- Yun Wang
- Affiliated Hospital of Hebei University/Hebei University (Clinical Medical College), Baoding, China
| | - Yurui Wang
- Tangshan Gongren Hospital, Tangshan, China
| | - Jialiang Ren
- General Electric Pharmaceutical Co., Ltd, Shanghai, China
| | - Linyi Jia
- Xingtai People’s Hospital, Xingtai, China
| | - Luyao Ma
- Affiliated Hospital of Hebei University/Hebei University (Clinical Medical College), Baoding, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University/Hebei University (Clinical Medical College), Baoding, China
- *Correspondence: Xiaoping Yin, ; Fei Yang,
| | - Fei Yang
- Medical Imaging Department, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
- *Correspondence: Xiaoping Yin, ; Fei Yang,
| | - Bu-Lang Gao
- Affiliated Hospital of Hebei University/Hebei University (Clinical Medical College), Baoding, China
| |
Collapse
|
14
|
Song Y, Li J, Wang H, Liu B, Yuan C, Liu H, Zheng Z, Min F, Li Y. Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study. Acad Radiol 2022; 29:806-816. [PMID: 34238656 DOI: 10.1016/j.acra.2021.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES Contrast-enhanced computed tomography (CE-CT) was used to establish radiomics nomogram to evaluate the malignant potential of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS A total of 500 GIST patients were enrolled in this study and divided into training cohort (n = 346, our center) and validation cohort (n = 154, another center). Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select the feature subset with the best discriminant features from the three phases image, and five classifiers were used to establish four radiomics signatures. Preoperative radiomics nomogram was constructed by adding the clinical features determined by multivariate logistic regression analysis. The performance of radiomics signatures and nomogram were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). The calibration of nomogram was appraised by calibration curve. RESULTS A total of 13 radiomic features were extracted from tri-phase combined CE-CT images. Tri-phase combined CE-CT features + Support Vector Machine (SVM) was the best combination at predicting the malignant potential of GIST, with an AUC of 0.895 (95% CI 0.858-0.931) in the training cohort and 0.847 (95% CI 0.778-0.917) in the validation cohort. The nomogram also had good calibration. In the training cohort and the validation cohort, preoperative radiomics nomogram reached AUCs of 0.927 and 0.905, respectively, which were higher than clinical. CONCLUSION The radiomics nomogram had a good predictive effect and generalization on the malignant potential of GIST, which could effectively help guide preoperative clinical decision.
Collapse
Affiliation(s)
- Yancheng Song
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Bo Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Chentong Yuan
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Hao Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Ziwen Zheng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Fanyi Min
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Yu Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong.
| |
Collapse
|
15
|
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: 0] [Impact Index Per Article: 0] [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.
Collapse
|
16
|
Palatresi D, Fedeli F, Danti G, Pasqualini E, Castiglione F, Messerini L, Massi D, Bettarini S, Tortoli P, Busoni S, Pradella S, Miele V. Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience. Radiol Med 2022; 127:117-128. [PMID: 35022956 DOI: 10.1007/s11547-021-01446-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/30/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Our primary purpose was to search for computed tomography (CT) radiomic features of gastrointestinal stromal tumors (GISTs) that could potentially correlate with the risk class according to the Miettinen classification. Subsequently, assess the existence of features with possible predictive value in differentiating responder from non-responder patients to first-line therapy with Imatinib. METHODS A retrospective study design was carried out using data from June 2009 to December 2020. We analyzed all the preoperative CTs of patients undergoing surgery for GISTs. We segmented non-contrast-enhanced CT (NCECT) and contrast-enhanced venous CT (CECT) images obtained either on three different CT scans (heterogeneous cohort) or on a single CT scan (homogeneous cohort). We then divided the patients into two groups according to Miettinen classification criteria and based on the predictive value of response to first-line therapy with Imatinib. RESULTS We examined 54 patients with pathological confirmation of GISTs. For the heterogeneous cohort, we found a statistically significant relationship between 57 radiomic features for NCECT and 56 radiomic features for CECT using the Miettinen risk classification. In the homogeneous cohort, we found the same relationship between 8 features for the NCECT and 5 features for CECT, all included in the heterogeneous cohort. The various radiomic features are distributed with different values in the two risk stratification groups according to the Miettinen classification. We also found some features for groups predictive of response to first-line therapy with Imatinib. CONCLUSIONS We found radiomic features that correlate with statistical significance for both the Miettinen risk classification and the molecular subtypes of response. All features found in the homogeneous study cohort were also found in the heterogeneous cohort. CT radiomic features may be useful in assessing the risk class and prognosis of GISTs.
Collapse
Affiliation(s)
- Daniele Palatresi
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Filippo Fedeli
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Elisa Pasqualini
- Pathology Unit, Department of Health Sciences, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Francesca Castiglione
- Histopathology and Molecular Diagnostics Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Luca Messerini
- Department of Experimental and Clinical Medicine, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Daniela Massi
- Pathology Unit, Department of Health Sciences, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Bettarini
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Paolo Tortoli
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Simone Busoni
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| |
Collapse
|
17
|
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: 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: 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.
Collapse
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
| |
Collapse
|
18
|
Iancu RI, Zara AD, Mirestean CC, Iancu DPT. Radiomics in Head and Neck Cancers Radiotherapy. Promises and Challenges. MAEDICA 2021; 16:482-488. [PMID: 34925606 DOI: 10.26574/maedica.2020.16.3.482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Radiomics, a subdomain of artificial intelligence, consists in extracting a large volume of data from all medical imaging techniques and correlating them with clinical data in order to build predictive and prognostic models. Radiomics is related to radiogenomics that correlates genetic mutations and molecular and biological characteristics of tissues with information extracted from medical imaging. Both are state-of-the-art fields of translational biomedical research. The ability to predict early patient survival and response to treatment, but also the capacity to identify tumor subtypes non-invasively, could make radiomics a key player with an essential role in personalized oncology. In head and neck cancer radiotherapy, radiomic algorithms can predict not only the response to radiochemotherapy or induction chemotherapy but also the need for planning through adaptive radiotherapy (ART). Radiomics can also predict the risk of severe toxicities, especially that of xerostomia. Given the benefit that a de-escalation of treatment can bring in selected cases to improve the quality of life, radiomics is expected to be part of the therapeutic decision for head and neck cancers in the near future, and may help identify cases where de-escalation of multimodal therapy will not jeopardize the therapeutic benefit.
Collapse
Affiliation(s)
| | - A D Zara
- Regional Institute of Oncology, Iasi, Romania
| | - C C Mirestean
- University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - D P T Iancu
- "Gr. T. Popa" University of Medicine and Pharmacy, Iasi, Romania
| |
Collapse
|
19
|
Ao W, Cheng G, Lin B, Yang R, Liu X, Zhou S, Wang W, Fang Z, Tian F, Yang G, Wang J. A novel CT-based radiomic nomogram for predicting the recurrence and metastasis of gastric stromal tumors. Am J Cancer Res 2021; 11:3123-3134. [PMID: 34249449 PMCID: PMC8263673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/17/2021] [Indexed: 06/13/2023] Open
Abstract
Our study aimed to explore the value of applying the CT-based radiomic nomogram for predicting recurrence and/or metastasis (RM) of gastric stromal tumors (GSTs). During the past ten years, a total of 236 patients with GST were analyzed retrospectively. According to the postoperative follow-up classification, the patients were divided into two groups, namely non-recurrence/metastasis group (non-RM) and RM group. All the cases were randomly divided into primary cohort and validation cohort according to the ratio of 7:3. Standardized CT images were segmented by radiologists using ITK-SNAP software manually. Texture features were extracted from all segmented lesions, then radiomic features were selected and the radiomic nomogram was built using least absolute shrinkage and selection operator (LASSO) method. The clinical features with the greatest correlation with RM of GST were selected by univariate analysis, and used as parameters to build the clinical feature model. Eventually, model of radiomic and clinical features were fitted to construct the clinical + radiomic feature model. The performance of each model was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). A total of 1223 features were extracted from all the segmentation regions of each case, and features were selected via the least absolute shrinkage and LASSO binary logistic regression model. After deletion of redundant features, four key features were obtained, which were used as the parameters to build a radiomic signature. The AUCs of radiomic nomogram in primary cohort and validation cohort were 0.816 and 0.946, respectively. The AUCs of clinical + radiomic feature model in primary cohort and validation cohort were 0.833 and 0.937, respectively. Using DeLong test, the differences of AUC values between radiomic nomogram and clinical + radiomic feature model in primary cohort (P = 0.840) and validation cohort (P = 0.857) were not statistically significant. To sum up, CT-based radiomic nomogram is of great potential in predicting the RM of GST non-invasively before operation.
Collapse
Affiliation(s)
- Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang ProvinceHangzhou, Zhejiang, China
| | | | - Bin Lin
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of MedicineHangzhou, Zhejiang, China
| | - Rong Yang
- Department of Radiology, First Affiliated Hospital, Zhejiang University School of MedicineHangzhou, Zhejiang, China
| | - Xuebin Liu
- Department of Radiology, First Affiliated Hospital, Zhejiang University School of MedicineHangzhou, Zhejiang, China
| | - Sheng Zhou
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese MedicineLanzhou, Gansu, China
| | - Wenqi Wang
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese MedicineLanzhou, Gansu, China
| | | | - Fengjuan Tian
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineHangzhou, Zhejiang, China
| | - Guangzhao Yang
- Department of Radiology, Tongde Hospital of Zhejiang ProvinceHangzhou, Zhejiang, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang ProvinceHangzhou, Zhejiang, China
| |
Collapse
|
20
|
Chen Z, Xu L, Zhang C, Huang C, Wang M, Feng Z, Xiong Y. CT Radiomics Model for Discriminating the Risk Stratification of Gastrointestinal Stromal Tumors: A Multi-Class Classification and Multi-Center Study. Front Oncol 2021; 11:654114. [PMID: 34168985 PMCID: PMC8217748 DOI: 10.3389/fonc.2021.654114] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/11/2021] [Indexed: 12/16/2022] Open
Abstract
Objective To establish and verify a computed tomography (CT)-based multi-class prediction model for discriminating the risk stratification of gastrointestinal stromal tumors (GISTs). Materials and Methods A total of 381 patients with GISTs were confirmed by surgery and pathology. Information on 213 patients were obtained from one hospital and used as training cohort, whereas the details of 168 patients were collected from two other hospitals and used as independent validation cohort. Regions of interest on CT images of arterial and venous phases were drawn, radiomics features were extracted, and dimensionality reduction processing was performed. Using a one-vs-rest method, a Random Forest-based GISTs risk three-class prediction model was established, and the receiver operating characteristic curve (ROC) was used to evaluate the performance of the multi-class classification model, and the generalization ability was verified using external data. Results The training cohort included 96 very low-risk and low-risk, 60 intermediate-risk and 57 high-risk patients. External validation cohort included 82 very low-risk and low-risk, 48 intermediate-risk and 38 high-risk patients. The GISTs risk three-class radiomics model had a macro/micro average area under the curve (AUC) of 0.84 and an accuracy of 0.78 in the training cohort. It had a stable performance in the external validation cohort, with a macro/micro average AUC of 0.83 and an accuracy of 0.80. Conclusion CT radiomics can discriminate GISTs risk stratification. The performance of the three-class radiomics prediction model is good, and its generalization ability has also been verified in the external validation cohort, indicating its potential to assist stratified and accurate treatment of GISTs in the clinic.
Collapse
Affiliation(s)
- Zhonghua Chen
- Department of Radiology, Haining People's Hospital, Jiaxing, China
| | - Linyi Xu
- Department of Radiology, Haining People's Hospital, Jiaxing, China
| | - Chuanmin Zhang
- Department of Radiology, Haining People's Hospital, Jiaxing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, R&D Center, Beijing, China
| | - Minhong Wang
- Department of Radiology, First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Zhan Feng
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yue Xiong
- Department of Radiology, Haining People's Hospital, Jiaxing, China
| |
Collapse
|
21
|
Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors. Sci Rep 2021; 11:12009. [PMID: 34103619 PMCID: PMC8187426 DOI: 10.1038/s41598-021-91508-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 05/24/2021] [Indexed: 01/08/2023] Open
Abstract
To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013–2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient’s enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733–0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696–0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: − 3.133, P = 0.008), maximum tumor diameter (Z value: − 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659–0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.
Collapse
|
22
|
Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021; 13:cancers13112522. [PMID: 34063937 PMCID: PMC8196591 DOI: 10.3390/cancers13112522] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Part I is an overview aimed to investigate some technical principles and the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy in gastrointestinal cancers, describing mostly the results for each pre-eminent tumor. In particular, this paper provides a general description of the main radiomic drawbacks and future challenges, which limit radiomic application in clinical setting as routine. Further investigations need to standardize and validate the Radiomics as a helpful tool in management of oncologic patients. In that context, Radiomics has been playing a relevant role and could be considered as a future imaging landscape. Abstract Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.
Collapse
Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome-Umberto I University Hospital, Viale del Policlinico, 155, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-063-377-5285
| |
Collapse
|
23
|
Mao H, Zhang B, Zou M, Huang Y, Yang L, Wang C, Pang P, Zhao Z. MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors. Front Oncol 2021; 11:631927. [PMID: 34041017 PMCID: PMC8141866 DOI: 10.3389/fonc.2021.631927] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 04/13/2021] [Indexed: 01/04/2023] Open
Abstract
Background We conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs). Methods Forty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal–Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models. Results The high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences). Conclusions Radiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.
Collapse
Affiliation(s)
- Haijia Mao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Bingqian Zhang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Mingyue Zou
- Department of Radiology, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Yanan Huang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Liming Yang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Cheng Wang
- Department of Pathology, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - PeiPei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| |
Collapse
|
24
|
Wang M, Feng Z, Zhou L, Zhang L, Hao X, Zhai J. Computed-Tomography-Based Radiomics Model for Predicting the Malignant Potential of Gastrointestinal Stromal Tumors Preoperatively: A Multi-Classifier and Multicenter Study. Front Oncol 2021; 11:582847. [PMID: 33968714 PMCID: PMC8100324 DOI: 10.3389/fonc.2021.582847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 02/19/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Our goal was to establish and verify a radiomics risk grading model for gastrointestinal stromal tumors (GISTs) and to identify the optimal algorithm for risk stratification. Methods: We conducted a retrospective analysis of 324 patients with GISTs, the presence of which was confirmed by surgical pathology. Patients were treated at three different hospitals. A training cohort of 180 patients was collected from the largest center, while an external validation cohort of 144 patients was collected from the other two centers. To extract radiomics features, regions of interest (ROIs) were outlined layer by layer along the edge of the tumor contour on CT images of the arterial and portal venous phases. The dimensionality of radiomic features was reduced, and the top 10 features with importance value above 5 were selected before modeling. The training cohort used three classifiers [logistic regression, support vector machine (SVM), and random forest] to establish three GIST risk stratification prediction models. The receiver operating characteristic curve (ROC) was used to compare model performance, which was validated by external data. Results: In the training cohort, the average area under the curve (AUC) was 0.84 ± 0.07 of the logistic regression, 0.88 ± 0.06 of the random forest, and 0.81 ± 0.08 of the SVM. In the external validation cohort, the AUC was 0.85 of the logistic regression, 0.90 of the random forest, and 0.80 of the SVM. The random forest model performed the best in both the training and the external validation cohorts and could be generalized. Conclusion: Based on CT radiomics, there are multiple machine-learning models that can predict the risk of GISTs. Among them, the random forest algorithm had the highest prediction efficiency and could be readily generalizable. Through external validation data, we assume that the random forest model may be used as an effective tool to guide preoperative clinical decision-making.
Collapse
Affiliation(s)
- Minhong Wang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Zhan Feng
- Department of Radiology, College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Lixiang Zhou
- Department of Pharmacy, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Liang Zhang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Xiaojun Hao
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Jian Zhai
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, China
| |
Collapse
|
25
|
Wu X, Dong D, Zhang L, Fang M, Zhu Y, He B, Ye Z, Zhang M, Zhang S, Tian J. Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study. Med Phys 2021; 48:2374-2385. [PMID: 33580497 DOI: 10.1002/mp.14767] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 01/06/2021] [Accepted: 02/04/2021] [Indexed: 12/18/2022] Open
Abstract
PURPOSE The present study assessed the predictive value of peritumoral regions on three tumor tasks, and further explored the influence of peritumors with different sizes. METHODS We retrospectively collected 333 samples of gastrointestinal stromal tumors from the Second Affiliated Hospital of Zhejiang University School of Medicine, and 183 samples of gastrointestinal stromal tumors from Tianjin Medical University Cancer Hospital. We also collected 211 samples of laryngeal carcinoma and 233 samples of nasopharyngeal carcinoma from the First Affiliated Hospital of Jinan University. The tasks of three tumor datasets were risk assessment (gastrointestinal stromal tumor), T3/T4 staging prediction (laryngeal carcinoma), and distant metastasis prediction (nasopharyngeal carcinoma), respectively. First, deep learning and radiomics were respectively used to construct peritumoral models, to study whether the peritumor had predictive value on three tumor datasets. Furthermore, we defined different sizes peritumors including fixed size (not considering tumor size) and adaptive size (according to average tumor radius) to explore the influence of peritumor of different sizes and types of tumors. Finally, we visualized the deep learning and radiomic models to observe the influence of the peritumor in three datasets. RESULTS The performance of intra-peritumors are better than intratumors alone in three datasets. Specifically, the comparisons of area under receiver operating characteristic curve in the testing set between intra-peritumoral and intratumoral models are: 0.908 vs 0.873 (P value: 0.037) in gastrointestinal stromal tumor datasets, 0.796 vs 0.756 (P value: 0.188) in laryngeal carcinoma datasets and 0.660 vs 0.579 (P value: 0.431) in nasopharyngeal carcinoma datasets. Furthermore, for gastrointestinal stromal tumor datasets, deep learning is more stable to learn peritumors with both fixed and adaptive size than radiomics. For laryngeal carcinoma datasets, the intra-peritumoral radiomic model could make model performance more balanced. For nasopharyngeal carcinoma datasets, radiomics is also more suitable for modeling peritumors than deep learning. The size of the peritumor is critical in this task, and only the performance of 1.5 mm-4.5 mm peritumors is stable. CONCLUSIONS Our results indicate that peritumors have additional predictive value in three tumor datasets through deep learning or radiomics. The definitions of the peritumoral region and artificial intelligence method also have great influence on the performance of the peritumor.
Collapse
Affiliation(s)
- Xiangjun Wu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Di Dong
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Mengjie Fang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yongbei Zhu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| |
Collapse
|
26
|
Li ZZ, Liu PF, An TT, Yang HC, Zhang W, Wang JX. Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients. Transl Oncol 2021; 14:101065. [PMID: 33761371 PMCID: PMC8020484 DOI: 10.1016/j.tranon.2021.101065] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 02/25/2021] [Accepted: 03/02/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND This study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints. METHOD LGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature. RESULTS Immunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes. CONCLUSIONS The IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.
Collapse
Affiliation(s)
- Zi-Zhuo Li
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| | - Peng-Fei Liu
- Department of Magnetic Resonance, The First Affiliated Hospital of Harbin Medical University China.
| | - Ting-Ting An
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| | - Hai-Chao Yang
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| | - Wei Zhang
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| | - Jia-Xu Wang
- Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China
| |
Collapse
|
27
|
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: 1.5] [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.
Collapse
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.
| |
Collapse
|
28
|
Yang L, Zheng T, Dong Y, Wang Z, Liu D, Du J, Wu S, Shi Q, Liu L. MRI Texture-Based Models for Predicting Mitotic Index and Risk Classification of Gastrointestinal Stromal Tumors. J Magn Reson Imaging 2020; 53:1054-1065. [PMID: 33037745 DOI: 10.1002/jmri.27390] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/23/2020] [Accepted: 09/25/2020] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Treatment regimens and prognoses of gastrointestinal stromal tumors (GIST) are quite different for tumors in different risk categories. Accurate preoperative grading of tumors is important for avoiding under- or overtreatment. PURPOSE To develop and validate an MRI texture-based model to predict the mitotic index and its risk classification. STUDY TYPE Retrospective. POPULATION Ninety-one patients with histologically-confirmed GIST; 64 patients in a training cohort, and 27 patients in a test cohort. FIELD STRENGTH/SEQUENCE T2 -weighted imaging (T2 WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) at 1.5T. ASSESSMENT GIST images were manually segmented by two independent radiologists using ITK-SNAP software and MRI features were extracted using Pyradiomics. Two pathologists reviewed the tissue specimens of the tumors to identify the mitotic index and risk classification in consensus. STATISTICAL TESTS The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. A logistic regression model was established based on the radiomic score (radscore), tumor location, and maximum diameter to predict tumor classification and develop a nomogram. Receiver operator characteristic (ROC) curves were used to evaluate the ability of the nomogram to distinguish between two tumors with different risk classifications, and a calibration curve was used to evaluate the consistency between the predicted risk and the actual risk. RESULTS The texture signature achieved high efficacy in predicting the mitotic index area under the curve ([AUC], 0.906; 95% confidence interval [CI]: 0.813, 0.961). A nomogram for prediction of the risk classification of GIST, which incorporated this texture signature together with maximum tumor diameter and location, allowed good discrimination in the training cohort (AUC, 0.878; 95% CI: 0.769, 0.960) and the validation cohort (AUC, 0.903; 95% CI: 0.732, 0.922). DATA CONCLUSION The texture-based model can be used to predict GIST mitotic index and risk classification preoperatively. LEVEL OF EVIDENCE 2. TECHNICAL EFFICACY STAGE 3.
Collapse
Affiliation(s)
- Linsha Yang
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China
| | - Tao Zheng
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China
| | - Yanchao Dong
- Department of Intervention, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China
| | - Zhanqiu Wang
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China
| | - Defeng Liu
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China
| | - Juan Du
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China
| | - Shuo Wu
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China
| | - Qinglei Shi
- Scientific Clinical Specialist, Siemens Ltd., Beijing, China
| | - Lanxiang Liu
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, China
| |
Collapse
|
29
|
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: 16] [Impact Index Per Article: 4.0] [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.
Collapse
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
| |
Collapse
|
30
|
Zhang QW, Zhou XX, Zhang RY, Chen SL, Liu Q, Wang J, Zhang Y, Lin J, Xu JR, Gao YJ, Ge ZZ. Comparison of malignancy-prediction efficiency between contrast and non-contract CT-based radiomics features in gastrointestinal stromal tumors: A multicenter study. Clin Transl Med 2020; 10:e291. [PMID: 32634272 PMCID: PMC7418807 DOI: 10.1002/ctm2.91] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 05/17/2020] [Accepted: 05/17/2020] [Indexed: 12/12/2022] Open
Abstract
This work seeks the development and validation of radiomics signatures from nonenhanced computed tomography (CT, NE-RS) to preoperatively predict the malignancy degree of gastrointestinal stromal tumors (GISTs) and the comparison of these signatures with those from contrast-enhanced CT. A dataset for 370 GIST patients was collected from four centers. This dataset was divided into cohorts for training, as well as internal and external validation. The minimum-redundancy maximum-relevance algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm were used to filter unstable features. (a) NE-RS and radiomics signature from contrast-enhanced CT (CE-RS) were built and compared for the prediction of malignancy potential of GIST based on the area under the receiver operating characteristic curve (AUC). (b) The radiomics model was also developed with both the tumor size and NE-RS. The AUC values were comparable between NE-RS and CE-RS in the training (.965 vs .936; P = .251), internal validation (.967 vs .960; P = .801), and external validation (.941 vs .899; P = .173) cohorts in diagnosis of high malignancy potential of GISTs. We next focused on the NE-RS. With 0.185 selected as the cutoff of NE-RS for diagnosis of the malignancy potential of GISTs, accuracy, sensitivity, and specificity for diagnosis high-malignancy potential GIST was 90.0%, 88.2%, and 92.3%, respectively, in the training cohort. For the internal validation set, the corresponding metrics are 89.1%, 94.9%, and 80.0%, respectively. The corresponding metrics for the external cohort are 84.6%, 76.1%, and 91.0%, respectively. Compared with only NE-RS, the radiomics model increased the sensitivity in the diagnosis of GIST with high-malignancy potential by 5.9% (P = .025), 2.5% (P = .317), 10.5% (P = .008) for the training set, internal validation set, and external validation set, respectively. The NE-RS had comparable prediction efficiency in the diagnosis of high-risk GISTs to CE-RS. The NE-RS and radiomics model both had excellent accuracy in predicting malignancy potential of GISTs.
Collapse
Affiliation(s)
- Qing-Wei 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
| | - Xiao-Xuan Zhou
- Department of Radiology, Sir Run Run Shaw Hospital (SRRSH), School of Medicine, Zhejiang University, Hangzhou, China
| | - Ran-Ying Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Fenglin Road 180, Shanghai, 200032, China
| | - Shuang-Li Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiang Liu
- Department of Pathology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Wang
- Department of radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Yan 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
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Fenglin Road 180, Shanghai, 200032, China
| | - Jian-Rong Xu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yun-Jie Gao
- 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
| | - Zhi-Zheng Ge
- 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
| |
Collapse
|
31
|
Zhang Q, Xu K, Chen M, Miao Y, Wang N, Xu Z, Xu H. Circulating tumor cells in whole process management of gastrointestinal stromal tumor in a real-life setting. Saudi J Gastroenterol 2020; 26:160-167. [PMID: 32386192 PMCID: PMC7392290 DOI: 10.4103/sjg.sjg_24_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND/AIM Liquid biopsy is changing the diagnosis and treatment strategies of various neoplasms. However, the circulating tumor cells (CTCs) of gastrointestinal stromal tumor (GIST) patients with different disease process are not clear. To better understand the dynamic change of CTCs in GIST patients, we conducted a real-life setting study. PATIENTS AND METHODS One-hundred fifty GIST patients were included. The isolation by size of tumor cell (ISET) method was employed to detect the CTCs/circulating tumor microemboli (CTM). Imatinib (IM) plasma concentration was detected by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). Multivariate and univariate analysis were used to analyze the effects of clinical characteristics on the positive rate of CTC and the number of CTCs/CTM. RESULTS The positive rate of CTCs was 72%. The median number of CTCs and CTM was 4 and 0. Logistic multivariate regression analysis showed that tumor diameter was the only independent factor of the positive rate of CTCs (P < 0.05). The numbers of CTCs and CTM had intensive linear correlation (P < 0.001). Tumor diameter, Ki 67 expression and mitotic were related to the number of CTCs (P < 0.05). Patients with higher Ki 67 expression tend to have more CTM (P < 0.05). IM plasma concentration showed no influence to the CTCs/CTM (P > 0.05). CONCLUSIONS : In the current study, we assessed the CTCs and CTM of GIST patients in various disease progressions and identified clinicopathological factors influencing the detection of CTCs and CTM. These results are instructive for clinicians to understand CTCs/CTM in GIST patients.
Collapse
Affiliation(s)
- Qiang Zhang
- Department of Gastrointestinal Surgery, The Second People's Hospital of Lianyungang, Lianyungang, Jiangsu, China
| | - Kangjing Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medical University, Nanjing, China
| | - Ming Chen
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medical University, Nanjing, China
| | - Yongchang Miao
- Department of Gastrointestinal Surgery, The Second People's Hospital of Lianyungang, Lianyungang, Jiangsu, China,Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Nuofan Wang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medical University, Nanjing, China
| | - Zekuan Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medical University, Nanjing, China
| | - Hao Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medical University, Nanjing, China,Address for correspondence: Dr. Hao Xu, Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, Jiangsu Province, 210029, China. E-mail:
| |
Collapse
|
32
|
Alsohaibani F, Alquaiz M, Alkahtani K, Alashgar H, Peedikayil M, AlFadda A, Almadi M. Efficacy of a bismuth-based quadruple therapy regimen for Helicobacter pylori eradication in Saudi Arabia. Saudi J Gastroenterol 2020; 26:84-88. [PMID: 32295933 PMCID: PMC7279072 DOI: 10.4103/sjg.sjg_626_19] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND/AIM The treatment efficacy of Helicobacter pylori (H. pylori) has been decreasing over time due to resistance to multiple antimicrobial therapies. The most effective treatment regimen for Saudi Arabian patients infected with H. pylori is still unknown. We aimed to study the eradication rate of 10 days of quadruple therapy; bismuth subcitrate potassium 140 mg, metronidazole 125 mg, and tetracycline 125 mg for H. pylori infection in a Saudi population. PATIENTS AND METHODS This was a prospective, open-label, non-randomized controlled trial. Patients with H. pylori infection were diagnosed by upper gastrointestinal (GI) endoscopy and rapid urease test (RUT) or histology. Patients who tested positive were recruited. Eligible patients were prescribed a 10-day course of quadruple therapy and received three capsules 4 times daily for 10 days along with omeprazole 20 mg twice daily. H. pylori was considered eradicated if the urea breath test (UBT) was negative after 6 weeks of completing the treatment. RESULTS Ninety-two patients with H. pylori infection were recruited. Three patients withdrew from the trial and another seven patients lost follow-up. We analyzed 82 patient's data as per-protocol analysis, of whom 66 (80%) were naive to H. pylori treatment. Four patients had failed previous treatment with the sequential regimen and 12 patients had treatment with clarithromycin-based triple therapy. The post-treatment UBT for H. pylori infection was negative by per-protocol analysis in 72/82 patients (87.8%), and 72/92 (78.3%) by intention-to-treat analysis. There was no correlation between previous treatment failure and treatment response to the bismuth-based quadruple therapy (P value = 0.28). CONCLUSIONS Treatment with a bismuth-based quadruple therapy was effective in eradicating H. pylori infection in 78.3% of Saudi patients with an ITT analysis and in 87.8% as per-protocol analysis.
Collapse
Affiliation(s)
- Fahad Alsohaibani
- Department of Medicine, Section of Gastroenterology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia,Address for correspondence: Dr. Fahad Alsohaibani, Department of Medicine MBC # 46, King Faisal Specialist Hospital and Research Center, P.O Box 3354, Riyadh - 11211, Saudi Arabia. E-mail:
| | - Mohammed Alquaiz
- Department of Medicine, Section of Gastroenterology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Khalid Alkahtani
- Department of Medicine, Section of Gastroenterology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Hamad Alashgar
- Department of Medicine, Section of Gastroenterology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Musthafa Peedikayil
- Department of Medicine, Section of Gastroenterology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Abdulrahman AlFadda
- Department of Medicine, Section of Gastroenterology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Majid Almadi
- Department of Medicine, Division of Gastroenterology, King Saud University Medical City, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| |
Collapse
|
33
|
Zhang QW, Gao YJ, Zhang RY, Zhou XX, Chen SL, Zhang Y, Liu Q, Xu JR, Ge ZZ. Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort. Clin Transl Med 2020; 9:12. [PMID: 32006200 PMCID: PMC6994569 DOI: 10.1186/s40169-020-0263-4] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 01/20/2020] [Indexed: 12/13/2022] Open
Abstract
Background and Aim To develop and validate radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). Method A total of 339 GIST patients from four centers were categorized into the training, internal validation, and external validation cohort. By filtering unstable features, minimum redundancy, maximum relevance, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, a radiomic signature was built to predict the malignant potential of GISTs. Individual nomograms of Ki-67 expression incorporating the radiomic signature or clinical factors were developed using the multivariate logistic model and evaluated regarding its calibration, discrimination, and clinical usefulness. Results The radiomic signature, consisting of 6 radiomic features had AUC of 0.787 [95% confidence interval (CI) 0.632–0.801], 0.765 (95% CI 0.683–0.847), and 0.754 (95% CI 0.666–0.842) in the prediction of high Ki-67 expression in the training, internal validation and external validation cohort, respectively. The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with AUC of 0.801 (95% CI 0.726–0.876), 0.828 (95% CI 0.681–0.974), and 0.784 (95% CI 0.701–0.868) in the training, internal validation and external validation cohort respectively. Based on the Decision curve analysis, the radiomics nomogram was found to be clinically significant and useful. Conclusions The radiomic signature from CE-CT was significantly associated with Ki-67 expression in GISTs. A nomogram consisted of radiomic signature, and tumor size had maximum accuracy in the prediction of Ki-67 expression in GISTs. Results from our study provide vital insight to make important preoperative clinical decisions.
Collapse
Affiliation(s)
- Qing-Wei 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
| | - Yun-Jie Gao
- 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
| | - Ran-Ying Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China
| | - Xiao-Xuan Zhou
- Department of Radiology, Sir Run Run Shaw Hospital (SRRSH) of School of Medicine, Zhejiang University, Hangzhou, China
| | - Shuang-Li Chen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Yan 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
| | - Qiang Liu
- Department of Pathology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200025, China
| | - Jian-Rong Xu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 1630, Dongfang Road, Pudong, Shanghai, 200120, China.
| | - Zhi-Zheng Ge
- 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.
| |
Collapse
|
34
|
Ren C, Wang S, Zhang S. Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors. Cancer Imaging 2020; 20:5. [PMID: 31931874 PMCID: PMC6958787 DOI: 10.1186/s40644-019-0284-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 12/29/2019] [Indexed: 12/15/2022] Open
Abstract
Background Gastrointestinal stromal tumors (GISTs), which are the most common mesenchymal tumors of the digestive system, are treated varyingly according to the malignancy. The purpose of this study is to develop and validate a nomogram for preoperative prediction of the malignant potential in patients with GIST. Methods A total of 440 patients with pathologically confirmed GIST after surgery in our hospital from January 2011 to July 2019 were retrospectively analyzed. They were randomly divided into the training set (n = 308) and validation set (n = 132). CT signs and texture features of each patient were analyzed and predictive model were developed using the least absolute shrinkage and selection operator (lasso) regression. Then a nomogram based on selected parameters was developed. The predictive effectiveness of nomogram was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). Concordance index (C-index) and calibration plots were formulated to evaluate the reliability and accuracy of the nomogram by bootstrapping based on internal (training set) and external (validation set) validity. The clinical application value of the nomogram was determined through the decision curve analysis (DCA). Results Totally 156 GIST patients with low-malignant (very low and low risk) and 284 ones with high-malignant potential (intermediate and high risk) are enrolled in this study. The prediction nomogram consisting of size, cystoid variation and meanValue had an excellent discrimination both in training and validation sets (AUCs (95% confidence interval(CI)): 0.935 (0.908, 0.961), 0.933 (0.892, 0.974); C-indices (95% CI): 0.941 (0.912, 0.956), 0.935 (0.901, 0.982); sensitivity: 81.4, 90.6%; specificity: 75.0, 75.7%; accuracy: 88.0, 88.6%, respectively). The calibration curves indicated a good consistency between the actual observation and nomogram prediction for differentiating GIST malignancy. Decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion This study presents a prediction nomogram that incorporates the CT signs and texture parameter, which can be conveniently used to facilitate the preoperative individualized prediction of malignancy in GIST patients.
Collapse
Affiliation(s)
- Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.,Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dong' an Road, Shanghai, 200032, China
| | - Shengping Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dong' an Road, Shanghai, 200032, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dong' an Road, Shanghai, 200032, China.
| |
Collapse
|
35
|
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.
Collapse
Affiliation(s)
- Cai-Wei Yang
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xi-Jiao Liu
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Si-Yun Liu
- 2GE Healthcare (China), Beijing 100176, China
| | - Shang Wan
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| |
Collapse
|