Deep Learning-derived Sarcopenia Marker Predicts Benefit from Anti-EGFR Therapy in Patients with RAS Wild-Type Metastatic Colorectal Cancer

Purpose: The benefit of treatment intensification in metastatic colorectal cancer (mCRC) may be influenced by host-related factors that are not accounted for in clinical trials or standard care. We investigated the prognostic and predictive value of muscle-bone ratio (MBR), a sarcopenia marker automatically derived from computed tomography (CT) images, in mCRC patients from the prospective PanaMa study and a real-world validation cohort. Experimental Design: PanaMa (AIO KRK 0212; NCT01991873) randomized patients with RAS wild-type mCRC, following induction therapy, to maintenance therapy with fluorouracil and folinic acid (FU/FA) with or without panitumumab (Pmab). MBR was automatically calculated from baseline CT images using a validated deep learning model, and patients were stratified by MBR tertiles. Associations with progression-free survival (PFS) and overall survival (OS) were studied using Kaplan-Meier and Cox regression analyses. A retrospective real-world cohort of mCRC patients treated with cetuximab was used for validation. Results: Pre-maintenance CT images were available for 189 of 248 randomized patients (76.2%) from PanaMa. In patients receiving FU/FA+Pmab, high MBR was associated with longer PFS (HR 0.43, 95% CI: 0.25-0.73, P=0.002) and OS (HR 0.41, 95% CI: 0.21-0.77, P=0.006), while no association was observed in patients receiving FU/FA alone. Pmab provided a PFS benefit only in patients with high MBR (HR 0.42, 95% CI: 0.24-0.73, P=0.002). The association of high MBR with superior PFS (P=0.002) and OS (P<0.001) was confirmed in the real-world cohort. Conclusions: The benefit of anti-EGFR therapy in mCRC is confined to patients with high MBR. Automated sarcopenia assessment holds promise for personalized treatment intensification in mCRC.