HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer
- Chiara M. L. Loeffler
- Hideaki Bando
- Srividhya Sainath
- Hannah Sophie Muti
- Xiaofeng Jiang
- Marko van Treeck
- Nic Gabriel Reitsam
- Zunamys I. Carrero
- Asier Rabasco Meneghetti
- Tomomi Nishikawa
- Toshihiro Misumi
- Saori Mishima
- Daisuke Kotani
- Hiroya Taniguchi
- Ichiro Takemasa
- Takeshi Kato
- Eiji Oki
- Yuan Tanwei
- Wankhede Durgesh
- Sebastian Foersch
- Hermann Brenner
- Michael Hoffmeister
- Yoshiaki Nakamura
- Takayuki Yoshino
- Jakob Nikolas Kather
2025-08-14
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.