Differentiation and risk stratification for gastrointestinal stromal tumors with endoscopic images using deep learning.
Purpose: Deep learning may be helpful to differentiate gastrointestinal stromal tumors and risk stratification using endoscopic images. Methods: A total of 494 patients with gastrointestinal stromal tumors and 1010 patients with gastric cancer from author's hospital were trained and validated. Anoth...
| Publicado en: | Health & Technology Vol. 16; no. 5; pp. 845 - 856 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196994174&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196994174 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21907188 BEWM jtl: Health & Technology issn: 21907188 maglogo: N pubinfo: dt: Sep2026 vid: 16 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196994174 195028571 196994174 196994174 10.1007/s12553-026-01088-5 196994174 ppf: 845 ppct: 11 formats: tig: atl: Differentiation and risk stratification for gastrointestinal stromal tumors with endoscopic images using deep learning. aug: au: Zheng, Qiao Guo, Wenhao Li, Sunjun Zhang, Long Sun, Jiayue Cui, Jiaye Jin, Xiance Sun, Zhen affil: https://ror.org/00rd5t069 Department of Radiation and Medical Oncology, Wenzhou Medical University First Affiliated Hospital, 325000, Wenzhou, China sug: subj: Gastrointestinal Neoplasms Diagnosis Gastrointestinal Neoplasms Risk Factors Neoplasms, Connective Tissue Diagnosis Neoplasms, Connective Tissue Risk Factors Deep Learning Diagnosis, Computer Assisted Endoscopy, Gastrointestinal Image Processing, Computer Assisted Diagnosis, Differential Risk Assessment Prediction Models Human Funding Source Cancer Patients Surgical Patients Gastrointestinal Neoplasms Surgery Hospitals Retrospective Design Record Review Data Analysis Software Descriptive Statistics T-Tests Chi Square Test Mann-Whitney U Test Comparative Studies ROC Curve Random Sample Adenocarcinoma Validation Studies Preoperative Care Multicenter Studies Gastrointestinal Neoplasms Pathology Neoplasms, Connective Tissue Pathology Biopsy Sensitivity and Specificity Evaluation ab: Purpose: Deep learning may be helpful to differentiate gastrointestinal stromal tumors and risk stratification using endoscopic images. Methods: A total of 494 patients with gastrointestinal stromal tumors and 1010 patients with gastric cancer from author's hospital were trained and validated. Another 99 patients with gastrointestinal stromal tumors and 100 patients with gastric cancer from second hospital were enrolled as external validation. Two deep learning networks, Swin Transformer and ConvNeXt were adapted for the differentiation, tumor size, mitotic index and risk stratification prediction with image level, patient level and combined level. Results: In the internal validation dataset, ConvNeXt achieved area under curve of 0.985 in the differentiation gastrointestinal stromal tumors from gastric cancer. Swin Transformer achieved a best area under curve of 0.927, 0.806 and 0.765 in the prediction of tumor size, mitotic index and risk stratification for gastrointestinal stromal tumors, respectively. In the external validation dataset, ConvNeXt achieved a best area under curve of 0.999 in the differentiation gastrointestinal stromal tumors from gastric cancer. Swin Transformer achieved a best area under curve of 0.872, 0.856 and 0.693 in the prediction of tumor size, mitotic index and risk stratification for gastrointestinal stromal tumors. Conclusions: Endoscopic images-based deep learning models demonstrated excellent performance in the differentiation gastrointestinal stromal tumors from gastric cancer, and in the prediction the tumor size, mitotic index and risk stratification of gastrointestinal stromal tumors. They are promising in the differentiation and risk stratification to improve the management of gastrointestinal stromal tumors. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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