Journal Article

DKFZ-2025-01714

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer.

Loeffler, C. M. L. ; Bando, H. ; Sainath, S. ; Muti, H. S. ; Jiang, X. ; van Treeck, M. ; Reitsam, N. G. ; Carrero, Z. I. ; Meneghetti, A. R. ; Nishikawa, T. ; Misumi, T. ; Mishima, S. ; Kotani, D. ; Taniguchi, H. ; Takemasa, I. ; Kato, T. ; Oki, E. ; Tanwei, Y.DKFZ* ; Durgesh, W.DKFZ* ; Foersch, S. ; Brenner, H.DKFZ* ; Hoffmeister, M.DKFZ* ; Nakamura, Y. ; Yoshino, T. ; Kather, J. N.

2025
Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41467-025-62910-8

Abstract: 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.

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Contributing Institute(s):

1. C070 Klinische Epidemiologie der Krebsfrüherkennung (C070)

Research Program(s):

1. 313 - Krebsrisikofaktoren und Prävention (POF4-313) (POF4-313)

Appears in the scientific report 2025

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