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Xie Z, Suo S, Zhang W, Zhang Q, Dai Y, Song Y, Li X, Zhou Y. Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases. Eur Radiol 2024; 34:2223-2232. [PMID: 37773213 PMCID: PMC10957607 DOI: 10.1007/s00330-023-10249-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/12/2023] [Accepted: 07/23/2023] [Indexed: 10/01/2023]
Abstract
OBJECTIVES To evaluate and analyze radiomics models based on non-contrast-enhanced computed tomography (CT) and different phases of contrast-enhanced CT in predicting Ki-67 proliferation index (PI) among patients with pathologically confirmed gastrointestinal stromal tumors (GISTs). METHODS A total of 383 patients with pathologically proven GIST were divided into a training set (n = 218, vendor 1) and 2 validation sets (n = 96, vendor 2; n = 69, vendors 3-5). Radiomics features extracted from the most recent non-contrast-enhanced and three contrast-enhanced CT scan prior to pathological examination. Random forest models were trained for each phase to predict tumors with high Ki-67 proliferation index (Ki-67>10%) and were evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics on the validation sets. RESULTS Out of 107 radiomics features extracted from each phase of CT images, four were selected for analysis. The model trained using the non-contrast-enhanced phase achieved an AUC of 0.792 in the training set and 0.822 and 0.711 in the two validation sets, similar to models trained on different contrast-enhanced phases (p > 0.05). Several relevant features, including NGTDM Busyness and tumor size, remained predictive in non-contrast-enhanced and different contrast-enhanced images. CONCLUSION The results of this study indicate that a radiomics model based on non-contrast-enhanced CT matches that of models based on different phases of contrast-enhanced CT in predicting the Ki-67 PI of GIST. GIST may exhibit similar radiological patterns irrespective of the use of contrast agent, and such radiomics features may help quantify these patterns to predict Ki-67 PI of GISTs. CLINICAL RELEVANCE STATEMENT GIST may exhibit similar radiomics patterns irrespective of contrast agent; thus, radiomics models based on non-contrast-enhanced CT could be an alternative for risk stratification in GIST patients with contraindication to contrast agent. KEY POINTS • Performance of radiomics models in predicting Ki-67 proliferation based on different CT phases is evaluated. • Non-contrast-enhanced CT-based radiomics models performed similarly to contrast-enhanced CT in risk stratification in GIST patients. • NGTDM Busyness remains stable to contrast agents in GISTs in radiomics models.
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Affiliation(s)
- Zhenhui Xie
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wang Zhang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qingwei Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Xiaobo Li
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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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.
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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.
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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.
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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
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Yang Y, Zhang L, Wang T, Jiang Z, Li Q, Wu Y, Cai Z, Chen X. MRI Fat‐Saturated T2‐Weighted
Radiomics Model for Identifying the Ki‐67 Index of Soft Tissue Sarcomas. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Affiliation(s)
- Yang Yang
- Department of Radiology Hospital of Chengdu University of Traditional Chinese Medicine Chengdu People's Republic of China
| | - Liyuan Zhang
- Department of Plastic Surgery Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China Chengdu People's Republic of China
| | - Ting Wang
- Department of Plastic Surgery The First People's Hospital of Yibin Yibin People's Republic of China
| | - Zhiyuan Jiang
- Department of Plastic Surgery Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China Chengdu People's Republic of China
| | - Qingqing Li
- Department of Plastic Surgery Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China Chengdu People's Republic of China
| | - Yinghua Wu
- Department of Radiology Hospital of Chengdu University of Traditional Chinese Medicine Chengdu People's Republic of China
| | - Zhen Cai
- Department of Plastic Surgery Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China Chengdu People's Republic of China
| | - Xi Chen
- Sichuan College of Traditional Chinese Medicine Mianyang People's Republic of China
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