Evidence map›Paper›PMID 42698112›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026

Machine learning based on preoperative CT for noninvasive prediction of recurrence-free survival in gastrointestinal stromal tumors.

Lian Zhao, Liming Zhao, Xiaonan Yin, Enyu Yuan, Yili Gu, Xiaohua Zheng, Bing Wu, Yuan Yin, Xijiao Liu

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Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Lian Zhao *Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan Province, 610041, China.
Liming Zhao *Department of Radiology, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, China.
Xiaonan YinDivision of Gastrointestinal Surgery, Department of General Surgery, West China Hospital of Sichuan University, Chengdu, China.
Enyu YuanDepartment of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan Province, 610041, China.
Yili GuDepartment of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan Province, 610041, China.
Xiaohua ZhengDepartment of Radiology, Chengdu Sixth People's Hospital, Chengdu, China.
Bing WuDepartment of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan Province, 610041, China.
Yuan YinDivision of Gastrointestinal Surgery, Department of General Surgery, West China Hospital of Sichuan University, Chengdu, China. yinyuan10@hotmail.com.
Xijiao LiuDepartment of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan Province, 610041, China. liuxijiao@wchscu.cn.

Funding

Chengdu City Science and Technology Project 2026-YF11-00019-HZSichuan Science and Technology Program 2025YFHZ0322the Postgraduate Education and Teaching Reform Research Project, West China Clinical College of Medicine, Sichuan University HXYJS202416
6 · The paper itself

Abstract

objectiveClinicians need a reliable, noninvasive tool that can predict the risk of gastrointestinal stromal tumor (GIST) recurrence preoperatively. We aimed to develop a machine learning model based on preoperative contrast-enhanced CT (CECT) to predict recurrence-free survival (RFS) in GIST patients who underwent radical resection. MATERIALS AND

methodsA total of 192 patients with intermediate- and high-risk GISTs who underwent radical resection and subsequently received adjuvant imatinib were included, with a minimum follow-up duration of 24 months. A machine learning model (Model

resultsThe C-index values of the Model

conclusionWe found the machine learning-based preoperative CECT performed better than the AFIP index in prediction of RFS of GIST patients, especially at the 5th year, predicting recurrence risk in patients who underwent radical resection and receiving adjuvant therapy. This model may serve as a non-invasive tool to identify high-risk individuals who require more intensive surveillance and personalized management following radical resection.

Indexed as

Gastrointestinal NeoplasmsGastrointestinal Stromal TumorsMachine LearningNeoplasm Recurrence, LocalTomography, X-Ray ComputedAgedContrast MediaDisease-Free SurvivalFemaleHumansImatinib MesylateMaleMiddle AgedPredictive Learning ModelsRetrospective StudiesContrast MediaImatinib MesylateCTGastrointestinal stromal tumorsMachine learningPrognosis

Identifiers

PMID42698112
PMCPMC13545761

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.