Multiscale Pancancer Analysis Uncovers Intrinsic Imaging and Molecular Characteristics Prominent in Breast Cancer and Glioblastoma.

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