Explainable AI-based prognostication of patients with resectable colorectal liver metastases using preoperative clinical parameters: is it comparable to traditional clinical scoring systems?

Background: Prognostication for patients undergoing liver resection for colorectal liver metastases (CRLM) remains challenging. Artificial intelligence-based survival models may improve individualized risk estimation. Methods: We developed and compared classical machine learning and deep learning survival models using exclusively preoperative clinical variables from 350 patients undergoing first-time liver resection for CRLM. Models included Cox proportional hazards (CoxPH), random survival forest (RSF), support vector machine for survival (SVM), XGBoost survival, DeepSurv, and DeepHit. Model performance was evaluated using the concordance index (C-index) and the Integrated Brier Score (IBS) on a training set of 245 patients, testing set of 105 patients, and an independent internal validation set of 88 patients from the OSLO-COMET trial. Model interpretability was assessed using SHapley Additive explanation (SHAP) analysis. The results of the developed models were compared to well established Basingstoke Predictive Index (BPI). Results: On internal validation cohort, CoxPH demonstrated the most consistent performance on the internal validation set (C-index 0.59), and on the test set patients (C-index 0.65), comparable to more complex machine learning and deep learning models. Although RSF achieved the highest training performance, this did not translate into superior performance both on the test and the internal validation datasets. CoxPH and DeepSurv models showed comparable 1-, 3-, and 5-year individual prediction to BPI. SHAP analysis consistently identified ASA score, lobar distribution, primary tumor location and number of metastases as the most influential predictors across models. Conclusions: Using structured preoperative clinical data, we developed machine-and deep-learning models to predict overall survival in patients with CRLM. Given that the models were based exclusively on preoperative clinical variables, their performance is encouraging. Predictive accuracy may be further improved by incorporating additional data modalities, particularly radiological features as well as by training on larger datasets.