Lijing Zhang1, Liqing Kang2, Guoce Li2, Xin Zhang3, Jialiang Ren4, Zhongqiang Shi4, Jiayue Li5, Shujing Yu2. 1. Department of Radiology, Cangzhou Central Hospital, No. 16 Xinhua West Road, Cangzhou, 061000, China. 18031792007@163.com. 2. Department of Radiology, Cangzhou Central Hospital, No. 16 Xinhua West Road, Cangzhou, 061000, China. 3. Department of Pathology, Cangzhou Central Hospital, Cangzhou, 061000, China. 4. GE Healthcare, Shanghai, 210000, China. 5. Department of Radiology, Cangzhou Renmin Hospital, Cangzhou, 061000, China.
Abstract
PURPOSE: The pathological risk degree of gastrointestinal stromal tumors (GISTs) has become an issue of great concern. Computed tomography (CT) is beneficial for showing adjacent tissues in detail and determining metastasis or recurrence of GISTs, but its function is still limited. Radiomics has recently shown a great potential in aiding clinical decision-making. The purpose of our study is to develop and validate CT-based radiomics models for GIST risk stratification. METHODS: Three hundred and sixty-six patients clinically suspected of primary GISTs from January 2013 to February 2018 were retrospectively enrolled, among which data from 140 patients were eventually analyzed after exclusion. Data from patient CT images were partitioned based on the National Institutes of Health Consensus Classification, including tumor segmentation, radiomics feature extraction and selection. A radiomics model was then proposed and validated. RESULTS: The radiomics signature demonstrated discriminative performance for advanced and nonadvanced GISTs with an area under the curve (AUC) of 0.935 [95% confidence interval (CI) 0.870-1.000] and an accuracy of 90.2% for validation cohort. The radiomics signature demonstrated favorable performance for the risk stratification of GISTs with an AUC of 0.809 (95% CI 0.777-0.841) and an accuracy of 67.5% for the validation cohort. Radiomics analysis could capture features of the four risk categories of GISTs. Meanwhile, this CT-based radiomics signature showed good diagnostic accuracy to distinguish between nonadvanced and advanced GISTs, as well as the four risk stratifications of GISTs. CONCLUSION: Our findings highlight the potential of a quantitative radiomics analysis as a complementary tool to achieve an accurate diagnosis for GISTs.
PURPOSE: The pathological risk degree of gastrointestinal stromal tumors (GISTs) has become an issue of great concern. Computed tomography (CT) is beneficial for showing adjacent tissues in detail and determining metastasis or recurrence of GISTs, but its function is still limited. Radiomics has recently shown a great potential in aiding clinical decision-making. The purpose of our study is to develop and validate CT-based radiomics models for GIST risk stratification. METHODS: Three hundred and sixty-six patients clinically suspected of primary GISTs from January 2013 to February 2018 were retrospectively enrolled, among which data from 140 patients were eventually analyzed after exclusion. Data from patient CT images were partitioned based on the National Institutes of Health Consensus Classification, including tumor segmentation, radiomics feature extraction and selection. A radiomics model was then proposed and validated. RESULTS: The radiomics signature demonstrated discriminative performance for advanced and nonadvanced GISTs with an area under the curve (AUC) of 0.935 [95% confidence interval (CI) 0.870-1.000] and an accuracy of 90.2% for validation cohort. The radiomics signature demonstrated favorable performance for the risk stratification of GISTs with an AUC of 0.809 (95% CI 0.777-0.841) and an accuracy of 67.5% for the validation cohort. Radiomics analysis could capture features of the four risk categories of GISTs. Meanwhile, this CT-based radiomics signature showed good diagnostic accuracy to distinguish between nonadvanced and advanced GISTs, as well as the four risk stratifications of GISTs. CONCLUSION: Our findings highlight the potential of a quantitative radiomics analysis as a complementary tool to achieve an accurate diagnosis for GISTs.
Authors: Vincenza Granata; Roberta Grassi; Roberta Fusco; Andrea Belli; Carmen Cutolo; Silvia Pradella; Giulia Grazzini; Michelearcangelo La Porta; Maria Chiara Brunese; Federica De Muzio; Alessandro Ottaiano; Antonio Avallone; Francesco Izzo; Antonella Petrillo Journal: Infect Agent Cancer Date: 2021-07-19 Impact factor: 2.965
Authors: Francesca Iacobellis; Donatella Narese; Daniela Berritto; Antonio Brillantino; Marco Di Serafino; Susanna Guerrini; Roberta Grassi; Mariano Scaglione; Maria Antonietta Mazzei; Luigia Romano Journal: Diagnostics (Basel) Date: 2021-05-30