Evidence map›Paper›PMID 41860707›Full record

ArticleAbdominal radiology (New York)2026

CT-based radiomics signature for optimizing adjuvant chemotherapy decisions in stage II colorectal cancer with proficient mismatch repair.

Huali Tang, Yuxin Xie, Qingshu Li, Yin Zhou, Xiaofang Zhao, Yineng Zheng, Liu Liu, Yi Li, Yanqi Huang, Yun Mao

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Article in Abdominal radiology (New York), 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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5 · Who and what money

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10 authors.

Huali Tang *Department of Radiology, People's Hospital of Chongqing Hechua, Chongqing, China.
Yuxin Xie *The Affiliated Guangzhou Hospital of TCM of Guangzhou University of Chinese Medicine, Guangzhou, China.
Qingshu Li *Department of Pathology, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Yin ZhouDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xiaofang ZhaoDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yineng ZhengDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Liu LiuDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yi LiDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yanqi HuangDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. huangyanqi@gdph.org.cn.
Yun MaoDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. maoyun1979@163.com.

Funding

the National Natural Scientific Foundation of China No: 81901909the Program for Youth Innovation in Future Medicine, Chongqing Medical University No: W0096/03020202Y2021284
6 · The paper itself

Abstract

purposeTo develop a CT-based radiomics model for predicting prognosis and optimizing adjuvant chemotherapy (ACT) decisions in stage II proficient mismatch repair (pMMR) colorectal cancer (CRC).

methodsWe retrospectively enrolled 379 stage II pMMR CRC patients with distinctly good or poor outcomes, including 125 patients in the validation cohort. The radiomics model was developed using features extracted from segmented CT volumes by XGBoost. Clinical model A was constructed via multivariate logistic regression incorporating clinical factors recommended by the European Society of Medical Oncology (ESMO). Model performance was compared using the DeLong test. Survival analysis employed Kaplan-Meier curves and log-rank tests, and ACT subgroup analysis was performed according to risk stratification.

resultsIn the validation cohort, the radiomics model (AUC = 0.800) and combined model A (AUC = 0.811) outperformed clinical model A (AUC = 0.576; both p < 0.05). Patients stratified as low-risk by either the radiomics model or the combined model A exhibited significantly longer overall survival (OS) than high-risk patients in both training and validation cohorts (all p < 0.05). Within the high-risk groups identified by the radiomics model and the combined model A, patients receiving ACT showed significantly longer OS compared to non-recipients (p = 0.015 and p = 0.008, respectively), while no significant difference was observed in low-risk groups (both p > 0.05).

conclusionCompared to ESMO-recommended clinical high-risk factors, the CT-based radiomics model demonstrated superior prognostic prediction performance and could optimize ACT decisions based on risk stratification in stage II pMMR CRC.

Indexed as

Colorectal NeoplasmsDNA Mismatch RepairRadiomicsTomography, X-Ray ComputedAgedChemotherapy, AdjuvantContrast MediaFemaleHumansMaleMiddle AgedNeoplasm StagingPrognosisRetrospective StudiesContrast MediaAdjuvant chemotherapyColorectal cancerCT-based radiomicsEfficacy predictionProficient mismatch repair

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