CT-radiomics and Pathological Tumor Response to Systemic Therapy: A Predictive Analysis for Colorectal Liver Metastases. Development and Internal Validation of a Clinical-Radiomic Model
EJSO（2025）
Humanitas Univ
Abstract
Introduction The standard treatment of colorectal liver metastases (CRLM) is surgery with perioperative chemotherapy. A tumor response to systemic therapy confirmed at pathology examination is the strongest predictor of survival, but it cannot be adequately predicted in the preoperative setting. This bi-institutional retrospective study investigates whether CT-based radiomics of CRLM and peritumoral tissue provides a reliable non-invasive estimation of the pathological tumor response to chemotherapy. Methods All consecutive patients undergoing liver resection for CRLM at the two institutions were considered. Only patients with a radiological partial response or stable disease at chemotherapy and with a preoperative/post-chemotherapy CT performed <60 days before surgery were included. The pathological response was evaluated according to the tumor regression grade (TRG). The tumor (Tumor-VOI) was manually segmented on the portal phase of the CT and a 5-mm ring of peritumoral tissue was automatically generated (Margin-VOI). The predictive models underwent internal validation. Results Overall, 222 patients were included; 64 had a pathological response (29%, TRG1-3). Two-third of patients displaying a radiological response (111/170) did not have a pathological one (TRG4-5). For TRG1-3 prediction, the clinical model performed fairly (Accuracy=0.725, validation-AUC=0.717 95%CI=0.652-0.788). Radiomics improved the results: the model combining the clinical data and Tumor-VOI features had Accuracy=0.743 and validation-AUC=0.729 (95%CI=0.665-0.798); the full model (clinical/Tumor-VOI/Margin-VOI) achieved Accuracy=0.820 and validation-AUC=0.768 (95%CI=0.707-0.826). Conclusion CT-based radiomics of CRLM allows an insightful non-invasive assessment of TRG. The combined analysis of the tumor and peritumoral tissue improves the prediction. In association with clinical data, the radiomic indices outperform standard radiological and clinical evaluation.
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Key words
Radiomics,Computed tomography,Colorectal liver metastases,Neoadjuvant chemotherapy,Pathological response,Tumor regression grade
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