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      A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images

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          Abstract

          Background

          Endoscopic ultrasonography (EUS) is recommended as the best tool for evaluating gastric subepithelial lesions (SELs); nonetheless, it has difficulty distinguishing gastrointestinal stromal tumors (GISTs) from leiomyomas and schwannomas. GISTs have malignant potential, whereas leiomyomas and schwannomas are considered benign.

          Purpose

          This study aimed to establish a combined radiomic model based on EUS images for distinguishing GISTs from leiomyomas and schwannomas in the stomach.

          Methods

          EUS images of pathologically confirmed GISTs, leiomyomas, and schwannomas were collected from five centers. Gastric SELs were divided into training and testing datasets based on random split‐sample method (7:3). Radiomic features were extracted from the tumor and muscularis propria regions. Principal component analysis, least absolute shrinkage, and selection operator were used for feature selection. Support vector machine was used to construct radiomic models. Two radiomic models were built: the conventional radiomic model included tumor features alone, whereas the combined radiomic model incorporated features from the tumor and muscularis propria regions.

          Results

          A total of 3933 EUS images from 485 cases were included. For the differential diagnosis of GISTs from leiomyomas and schwannomas, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were 74.5%, 72.2%, 78.7%, and 0.754, respectively, for the EUS experts; 76.8%, 74.4%, 81.0%, and 0.830, respectively, for the conventional radiomic model; and 90.9%, 91.0%, 90.6%, and 0.953, respectively, for the combined radiomic model. For gastric SELs <20 mm, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the combined radiomic model were 91.4%, 91.6%, 91.1%, and 0.960, respectively.

          Conclusions

          We developed and validated a combined radiomic model to distinguish gastric GISTs from leiomyomas and schwannomas. The combined radiomic model showed better diagnostic performance than the conventional radiomic model and could assist EUS experts in non‐invasively diagnosing gastric SELs, particularly gastric SELs <20 mm.

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          Most cited references43

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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: the bridge between medical imaging and personalized medicine

            Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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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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                Author and article information

                Contributors
                zhou_chunh@163.com
                zdwrjxh66@sjtu.edu.cn
                Journal
                J Appl Clin Med Phys
                J Appl Clin Med Phys
                10.1002/(ISSN)1526-9914
                ACM2
                Journal of Applied Clinical Medical Physics
                John Wiley and Sons Inc. (Hoboken )
                1526-9914
                11 May 2023
                July 2023
                : 24
                : 7 ( doiID: 10.1002/acm2.v24.7 )
                : e14023
                Affiliations
                [ 1 ] Department of Gastroenterology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
                [ 2 ] Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China
                [ 3 ] Department of Gastroenterology The Second Affiliated Hospital of Soochow University Suzhou Jiangsu China
                [ 4 ] Department of Digestive Endoscopy Center Shanghai Tenth People's Hospital, Tongji University School of Medicine Shanghai China
                [ 5 ] Department of Gastroenterology, Jinhua Hospital Zhejiang University School of Medicine Jinhua Zhejiang China
                [ 6 ] Department of Gastroenterology First Hospital of Hanbin District Ankang Shaanxi China
                Author notes
                [*] [* ] Correspondence

                Dr. Chun‐Hua Zhou and Dr. Duo‐Wu Zou, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai 200025, China.

                Email: zhou_chunh@ 123456163.com , zdwrjxh66@ 123456sjtu.edu.cn

                Author information
                https://orcid.org/0000-0002-9881-4111
                https://orcid.org/0000-0003-2351-578X
                https://orcid.org/0000-0002-2461-5304
                Article
                ACM214023
                10.1002/acm2.14023
                10338752
                37166416
                58e8f0b7-0cf6-45ad-a1a4-fd9d78923821
                © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 April 2023
                : 10 February 2023
                : 25 April 2023
                Page count
                Figures: 2, Tables: 5, Pages: 10, Words: 5856
                Funding
                Funded by: Science and Technology Commission of Shanghai Municipality , doi 10.13039/501100003399;
                Award ID: 21Y11908100
                Award ID: 21S31903500
                Funded by: Science and Education Revitalize Health, Suzhou, Jiangsu Province, China
                Award ID: KJXW2019013
                Categories
                Medical Imaging
                Medical Imaging
                Custom metadata
                2.0
                July 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.1 mode:remove_FC converted:13.07.2023

                endoscopic ultrasonography,gastrointestinal stromal tumors,radiomics,stomach,subepithelial lesion

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