Deep learning based tissue analysis predicts outcome in colorectal cancer
Open accessAbstract
Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.
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Prognostic AI models for colorectal cancer have been developed for MRI, histopathology, and multiplex imaging.
“Prognostic AI models for colorectal cancer have been developed for various data modalities, including MRI (88), histopathology (89-91), and multiplex imaging (92,93).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [91]
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- Authors
- Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J.
- Affiliations
- University of Helsinki (FI)
- Uppsala University (SE)
- University of Oxford (GB)
- Corresponding author
- Bychkov, Dmitrii
- ORCID
- Venue
- Scientific Reports
- Published
- 2018-02-21
- Volume
- 8
- Issue
- 1
- Pages
- 3395-3395
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5821847
- OpenAlex
- W2794803511
- Cited by
- 644
- References
- 56
- Citations by year
- 2026: 25 · 2025: 56 · 2024: 76 · 2023: 86 · 2022: 101 · 2021: 120 · 2020: 101 · 2019: 57 · 2018: 22
- FWCI
- 41.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- Funders
- Finska Läkaresällskapet
- National Institute for Health and Care Research
- Sigrid Juséliuksen Säätiö
- Biocenter Finland
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Keywords
- colorectal cancer, outcome prediction, tissue microarray, H&E staining, convolutional recurrent neural network, tumor morphology, risk stratification, whole slide images
- MeSH terms
- Machine Learning, Deep Learning, Aged, Algorithms, Eosine Yellowish-(YS), Female, Hematoxylin, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Prognosis, Retrospective Studies, Colorectal Neoplasms
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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