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| Home > Publications database > HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer. > print |
| 001 | 303650 | ||
| 005 | 20250824022537.0 | ||
| 024 | 7 | _ | |a 10.1038/s41467-025-62910-8 |2 doi |
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| 082 | _ | _ | |a 500 |
| 100 | 1 | _ | |a Loeffler, Chiara M L |b 0 |
| 245 | _ | _ | |a HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer. |
| 260 | _ | _ | |a [London] |c 2025 |b Springer Nature |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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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| 700 | 1 | _ | |a Bando, Hideaki |0 0000-0001-5041-2765 |b 1 |
| 700 | 1 | _ | |a Sainath, Srividhya |b 2 |
| 700 | 1 | _ | |a Muti, Hannah Sophie |b 3 |
| 700 | 1 | _ | |a Jiang, Xiaofeng |0 0000-0002-3716-8232 |b 4 |
| 700 | 1 | _ | |a van Treeck, Marko |b 5 |
| 700 | 1 | _ | |a Reitsam, Nic Gabriel |0 0000-0002-0070-3158 |b 6 |
| 700 | 1 | _ | |a Carrero, Zunamys I |b 7 |
| 700 | 1 | _ | |a Meneghetti, Asier Rabasco |0 0000-0002-1508-9410 |b 8 |
| 700 | 1 | _ | |a Nishikawa, Tomomi |b 9 |
| 700 | 1 | _ | |a Misumi, Toshihiro |b 10 |
| 700 | 1 | _ | |a Mishima, Saori |b 11 |
| 700 | 1 | _ | |a Kotani, Daisuke |0 0000-0002-4196-555X |b 12 |
| 700 | 1 | _ | |a Taniguchi, Hiroya |0 0000-0003-1407-6682 |b 13 |
| 700 | 1 | _ | |a Takemasa, Ichiro |b 14 |
| 700 | 1 | _ | |a Kato, Takeshi |b 15 |
| 700 | 1 | _ | |a Oki, Eiji |0 0000-0002-9763-9366 |b 16 |
| 700 | 1 | _ | |a Tanwei, Yuan |0 0000-0001-7387-6327 |b 17 |
| 700 | 1 | _ | |a Durgesh, Wankhede |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Foersch, Sebastian |0 0000-0002-4740-6900 |b 19 |
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| 700 | 1 | _ | |a Hoffmeister, Michael |0 P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f |b 21 |u dkfz |
| 700 | 1 | _ | |a Nakamura, Yoshiaki |0 0000-0002-5241-6855 |b 22 |
| 700 | 1 | _ | |a Yoshino, Takayuki |0 0000-0002-0489-4756 |b 23 |
| 700 | 1 | _ | |a Kather, Jakob Nikolas |b 24 |
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