Intratumoral and peritumoral delta radiomics of MRI predicts overall survival to targeted therapy in colorectal cancer with liver metastases

To develop and validate a hybrid prognostic model integrating clinical, MRI, and radiomics features for predicting overall survival (OS) in patients with colorectal cancer liver metastases (CRCLM) undergoing targeted therapy. This retrospective, multicenter study included 118 CRCLM patients who received targeted therapy at two tertiary hospitals. Patients were divided into training, internal test, and external validation cohorts. Clinical, MRI, and radiomics features were comprehensively collected. Multiple radiomics models—including intratumoral, peritumoral, combined, and delta models—were constructed. The optimal model was selected and combined with key clinical and imaging features to build a hybrid prognostic model. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration, and risk stratification by Kaplan–Meier analysis. Tumor related to adjacent vein (TRTAV), and the radiomics-derived Rad-score were identified as independent predictors of OS. The hybrid model demonstrated superior prognostic accuracy compared to single-feature models, with robust AUCs for 1-, 2-, and 3-year OS prediction across all cohorts. Risk stratification by the hybrid model revealed significant survival differences between low- and high-risk groups (all p < 0.05). The proposed hybrid model, integrating MRI and radiomics features, enables accurate, non-invasive prediction of OS in CRCLM patients undergoing targeted therapy and outperforms RECIST 1.1 in survival stratification, supporting individualized prognosis and clinical decision-making. This study received the approval of the two Institutional Review Boards ( No.LLHBCH2025YN-082 for Hubei Cancer Hospital; No.UHCT-IEC-SOP-016–03-01 for Wuhan Union Hospital).

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Journal
BMC Cancer
Published
2026-09-25
DOI
https://doi.org/10.1186/s12885-026-17014-x
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Intratumoral and peritumoral delta radiomics of MRI predicts overall survival to targeted therapy in colorectal cancer with liver metastases

Yulin Liu, Shuangquan Ai, renzhe xiao, Yi Li et al.
BMC Cancer
Radiomics and Machine Learning in Medical Imaging
article

Intratumoral and peritumoral delta radiomics of MRI predicts overall survival to targeted therapy in colorectal cancer with liver metastases

Yulin Liu, Shuangquan Ai, renzhe xiao, Yi Li, Wei Xiao, Xiaofang Guo, Zixuan Liu
article en

Abstract

To develop and validate a hybrid prognostic model integrating clinical, MRI, and radiomics features for predicting overall survival (OS) in patients with colorectal cancer liver metastases (CRCLM) undergoing targeted therapy. This retrospective, multicenter study included 118 CRCLM patients who received targeted therapy at two tertiary hospitals. Patients were divided into training, internal test, and external validation cohorts. Clinical, MRI, and radiomics features were comprehensively collected. Multiple radiomics models—including intratumoral, peritumoral, combined, and delta models—were constructed. The optimal model was selected and combined with key clinical and imaging features to build a hybrid prognostic model. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration, and risk stratification by Kaplan–Meier analysis. Tumor related to adjacent vein (TRTAV), and the radiomics-derived Rad-score were identified as independent predictors of OS. The hybrid model demonstrated superior prognostic accuracy compared to single-feature models, with robust AUCs for 1-, 2-, and 3-year OS prediction across all cohorts. Risk stratification by the hybrid model revealed significant survival differences between low- and high-risk groups (all p < 0.05). The proposed hybrid model, integrating MRI and radiomics features, enables accurate, non-invasive prediction of OS in CRCLM patients undergoing targeted therapy and outperforms RECIST 1.1 in survival stratification, supporting individualized prognosis and clinical decision-making. This study received the approval of the two Institutional Review Boards ( No.LLHBCH2025YN-082 for Hubei Cancer Hospital; No.UHCT-IEC-SOP-016–03-01 for Wuhan Union Hospital).

BMC Cancer
Union Hospital (HK), Huazhong University of Science and Technology (CN)
Good health and well-being
Openalex Percentile: Top 12%
Radiomics and Machine Learning in Medical Imaging
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