Multi-modal modeling of genomic, histopathologic, and lab data predicts survival after hepatectomy in oligometastatic colorectal cancer

Background: Oligometastasis is an intermediate stage of metastatic progression with limited spread and organ involvement, where curative treatment remains possible. In colorectal cancer (CRC), ~10% of patients present with liver-limited oligometastatic disease and undergo hepatectomy with curative intent. However, there is an unmet need for pre-operative predictive biomarkers that can distinguish long-term survivors from patients who relapse shortly after hepatectomy and thus did not benefit from surgery. To improve clinical decision-making for oligometastatic CRC, we aimed to develop a machine learning (ML) model to reliably predict post-hepatectomy outcomes using routinely collected clinical data. Methods: We analyzed 284 CRC patients treated at Memorial Sloan Kettering Cancer Center who had liver-confined oligometastatic disease and underwent metastatic resection via partial hepatectomy. Four data modalities were collected: clinical features; MSK-IMPACT targeted exon sequencing; histopathology features from whole slide images; and laboratory values collected within 30 days pre-hepatectomy. We trained an XGBoost ML model with nested cross-validation to predict which patients would achieve overall survival greater than three years after hepatectomy. Results: An ML model trained on genomics, histopathology and lab data achieved the highest predictive performance (AUROC = 0.75, SE = 0.03), with comparable performance using only genomics and lab data (AUROC = 0.73, SE = 0.04). Combining ML-predicted risk with the established Clinical Risk Score (CRS) for CRC recurrence showed a modest increase in prognostic discrimination (CRS C-index = 0.606, CRS + ML C-index = 0.635). Lab and histopathology variables contributed most to model predictions, and an elevated systemic inflammation index ((platelets × neutrophils) / lymphocytes) was associated with worse prognosis, consistent with prior work. We further found that deep deletion in dual-specificity phosphatase DUSP4, a regulator of MAPK signaling, was significantly associated with improved long-term survival (log-rank p = 0.016). Conclusions: A multi-modal ML model using pre-operative genomic, histopathologic, laboratory, and clinical data can predict long-term survival following hepatectomy in oligometastatic CRC. This study offers a strategy for integrating routinely collected data to improve clinical decision making and risk stratification for resection of oligometastatic disease.