HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer.
Loeffler CML., Bando H., Sainath S., Muti HS., Jiang X., van Treeck M., Reitsam NG., Carrero ZI., Meneghetti AR., Nishikawa T., Misumi T., Mishima S., Kotani D., Taniguchi H., Takemasa I., Kato T., Oki E., Tanwei Y., Durgesh W., Foersch S., Brenner H., Hoffmeister M., Nakamura Y., Yoshino T., Kather JN.
Although surgical resection is the standard therapy for stage II/III colorectal cancer, recurrence rates exceed 30%. Circulating tumor DNA (ctNDA) detects molecular residual disease (MRD), but lacks spatial and tumor microenvironment information. Here, we develop a deep learning (DL) model to predict disease-free survival from hematoxylin & eosin stained whole slide images in stage II-IV colorectal cancer. The model is trained on the DACHS cohort (n = 1766) and validated on the GALAXY cohort (n = 1404). In GALAXY, the DL model categorizes 304 patients as DL high-risk and 1100 as low-risk (HR 2.31; p < 0.005). Combining DL scores with MRD status improves prognostic stratification in both MRD-positive (HR 1.58; p < 0.005) and MRD-negative groups (HR 2.1; p < 0.005). Notably, MRD-negative patients predicted as DL high-risk benefit from adjuvant chemotherapy (HR 0.49; p = 0.01) vs. DL low-risk (HR = 0.92; p = 0.64). Combining ctDNA with DL-based histology analysis significantly improves risk stratification, with the potential to improve follow-up and personalized adjuvant therapy decisions.