Abstract 4217: Multi-modal modeling of genomic, histopathologic, and lab data predicts survival after hepatectomy in oligometastatic colorectal cancer

Abstract                                        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.                                                           Citation Format:                     Divya Koyyalagunta, Stefanie Gerstberger, Marion Liu, Chenlian Fu, Simran Chhabria, Madison Darmofal, Kevin Michael Boehm, Justin Jee, Michele Waters, Vinod P. Balachandran, Kevin Soares, Alice C. Wei, Jeffrey A. Drebin, T. Peter Kingham, William R. Jarnagin, Jinru Shia, Francisco Sanchez-Vega, Michael I. D’Angelica, Karuna Ganesh, Quaid Morris, . Multi-modal modeling of genomic, histopathologic, and lab data predicts survival after hepatectomy in oligometastatic colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4217.