HPB O06 - A computer tomography-based radiomics signature predicts overall survival following curative resection of colorectal liver metastasis: A multicentre, retrospective study

Abstract

Abstract Background Colorectal cancer liver metastasis (CRLM) has a heterogeneous outcome and improved prognostic biomarkers are needed to aid patient-tailored management. Here, we assess the value of Computer Tomography (CT)-guided radiomics in the prognostication of post-resection CRLM using data from two large tertiary referral centres in the UK. Method Patient data was extracted from prospectively maintained databases of patients undergoing CRLM resection at two centres. CRLM and regions of interest from background normal liver were segmented. Radiomics features were extracted from portal venous phase CT images using open-source software. Feature selection techniques were applied. Machine learning models incorporating radiomic or clinical features, and their combination, were developed and assessed for mortality prediction at fixed time points. Cox proportional hazard regression was utilised for risk score generation to classify patients as high or low-risk for overall survival estimation. The models were evaluated on unseen testing data. Results Radiomics features (269) were generated from 959 metastases in 399 patients across the two sites. Models with combined clinical and radiomic features performed similarly to those with radiomic features alone, and often outperformed models with clinical features alone demonstrating good discrimination and risk prediction at 2 and 3 years. The computed radiomic scores allowed us to generate Kaplan–Meier curves which showed CRLM patients in the high-risk group had a lower overall survival vs the low-risk group(p<0.05). This risk-score independently predicted mortality in our multivariate Cox proportional-hazards model. Conclusion We demonstrate a robust combination of CT-based radiomics features can predict CRLM outcomes across two different hospital sites. Our radiomic risk-scores predicts overall survival and can prognosticate patients into high and low risk groups. These findings support the prognostic utility of CT-based radiomics in patients undergoing resection of CRLM and provide rationale for further investigation in a prospective, multi-centre setting.