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      Preoperative prediction of malignant potential of 2-5 cm gastric gastrointestinal stromal tumors by computerized tomography-based radiomics

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          Abstract

          BACKGROUND

          The use of endoscopic surgery for treating gastrointestinal stromal tumors (GISTs) between 2 and 5 cm remains controversial considering the potential risk of metastasis and recurrence. Also, surgeons are facing great difficulties and challenges in assessing the malignant potential of 2-5 cm gastric GISTs.

          AIM

          To develop and evaluate computerized tomography (CT)-based radiomics for predicting the malignant potential of primary 2-5 cm gastric GISTs.

          METHODS

          A total of 103 patients with pathologically confirmed gastric GISTs between 2 and 5 cm were enrolled. The malignant potential was categorized into low grade and high grade according to postoperative pathology results. Preoperative CT images were reviewed by two radiologists. A radiological model was constructed by CT findings and clinical characteristics using logistic regression. Radiomic features were extracted from preoperative contrast-enhanced CT images in the arterial phase. The XGboost method was used to construct a radiomics model for the prediction of malignant potential. Nomogram was established by combing the radiomics score with CT findings. All of the models were developed in a training group ( n = 69) and evaluated in a test group ( n = 34).

          RESULTS

          The area under the curve (AUC) value of the radiological, radiomics, and nomogram models was 0.753 (95% confidence interval [CI]: 0.597-0.909), 0.919 (95%CI: 0.828-1.000), and 0.916 (95%CI: 0.801-1.000) in the training group vs 0.642 (95%CI: 0.379-0.870), 0.881 (95%CI: 0.772-0.990), and 0.894 (95%CI: 0.773-1.000) in the test group, respectively. The AUC of the nomogram model was significantly larger than that of the radiological model in both the training group ( Z = 2.795, P = 0.0052) and test group ( Z = 2.785, P = 0.0054). The decision curve of analysis showed that the nomogram model produced increased benefit across the entire risk threshold range.

          CONCLUSION

          Radiomics may be an effective tool to predict the malignant potential of 2-5 cm gastric GISTs and assist preoperative clinical decision making.

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          Most cited references34

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          Radiomics: Images Are More than Pictures, They Are Data

          This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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            Radiomics: extracting more information from medical images using advanced feature analysis.

            Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

              To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).
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                Author and article information

                Contributors
                Journal
                World J Gastrointest Oncol
                WJGO
                World Journal of Gastrointestinal Oncology
                Baishideng Publishing Group Inc
                1948-5204
                15 May 2022
                15 May 2022
                : 14
                : 5
                : 1014-1026
                Affiliations
                Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
                Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
                Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
                Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
                Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
                Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
                Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China. sys27@ 123456163.com
                Author notes

                Author contributions: Sun XF designed the study and was responsible for the work; Sun XF, Tang L, and Ji WY conducted data collection; Sun XF and Zhang XY conducted image measurement; Zhu HT and Li XT conducted statistical analyses; Sun XF wrote the paper; all authors edited the paper.

                Supported by Beijing Hospitals Authority Ascent Plan, No. 20191103; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support , No. ZYLX201803; Beijing Natural Science Foundation, No. Z180001 and No. Z200015; and PKU-Baidu Fund, No. 2020BD027.

                Corresponding author: Ying-Shi Sun, MD, Chief Doctor, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52 Fucheng Road, Haidian District, Beijing 100142, China. sys27@ 123456163.com

                Article
                jWJGO.v14.i5.pg1014
                10.4251/wjgo.v14.i5.1014
                9124987
                35646280
                6b3365c5-c510-4aa8-9a60-3b2c3c985075
                ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.

                This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

                History
                : 1 December 2021
                : 29 December 2021
                : 21 April 2022
                Categories
                Retrospective Study

                gastrointestinal stromal tumors,gastric gastrointestinal stromal tumors,computed tomography,malignant potential,radiomics,nomogram

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