Deep Learning-Derived Sarcopenia Marker Predicts Benefit from Anti-EGFR Therapy in Patients with RAS Wild-type Metastatic Colorectal Cancer.

Abstract

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 the muscle/bone ratio (MBR), a sarcopenia marker automatically derived from computed tomography (CT) images, in patients with mCRC from the prospective PanaMa study and a real-world validation cohort. 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 patients with mCRC treated with cetuximab was used for validation. Premaintenance 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% confidence interval (CI), 0.25-0.73; P = 0.002] and OS (HR, 0.41; 95% CI, 0.21-0.77; P = 0.006), whereas 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. The benefit of anti-EGFR therapy in mCRC is confined to patients with a high MBR. Automated sarcopenia assessment holds promise for personalized treatment intensification in mCRC.