Genomic traits are commonly observed across cancer types, yet current pan-cancer analyses primarily focus on shared molecular features, often overlooking potential imaging characteristics across cancers.

This retrospective study included 793 patients from the I-SPY1 breast cancer cohort (n = 145), Duke-UPenn glioblastoma (GBM) cohort (n = 452), and an external validation cohort (n = 196). We developed and validated multiparametric MRI-based radiomic and deep learning models to extract both cancer-type common (CTC) and cancer type-specific (CTS) features associated with the prognosis of both cancers. The biological relevance of the identified CTC features was investigated through pathway analysis.

Seven CTC radiomic features were identified, demonstrating superior survival prediction compared to cancer type-specific (CTS) features, with AUCs of 0.876 for breast cancer and 0.732 for GBM. The deep feature model stratified patients into distinct survival groups (p = 0.00029 for breast cancer; p = 0.0019 for GBM), with CTC features contributing more than CTS features. Independent validation confirmed their robustness (AUC: 0.784). CTC-associated genes were enriched in key pathways, including focal adhesion, suggesting a role in breast cancer brain metastasis.

Our study reveals pan-cancer imaging phenotypes that predict survival and provide biological insights, highlighting their potential in precision oncology.