Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning
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Abstract
Objective: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design: Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results: Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion: This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.
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CMS4 tumors have stromal enrichment.
“A second outstanding and undisputed hallmark of CMS4 tumors is their stromal enrichment, which emerged both from transcriptomic signature (2) and histologic analyses (29).”
From Clinical Challenges of Consensus Molecular Subtype CMS4 Colon Cancer in the Era of Precision Medicine (2024) · cites this paper as [29]
The CMS4 subtype is associated with tumor budding.
“several studies have documented an association between the CMS4 subtype and tumor budding, defined as a single tumor cell or tumor cluster of up to four cells at the invasive margin (29–31)”
From Clinical Challenges of Consensus Molecular Subtype CMS4 Colon Cancer in the Era of Precision Medicine (2024) · cites this paper as [29]
Artificial intelligence opens new avenues for colorectal cancer classification with a high degree of robustness.
“artificial intelligence is now opening up new avenues for colorectal cancer classification, with a high degree of robustness (29).”
From Clinical Challenges of Consensus Molecular Subtype CMS4 Colon Cancer in the Era of Precision Medicine (2024) · cites this paper as [29]
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Self-Supervision Closes the Gap Between Weak and Strong Supervision in HistologyPreprint
Dehaene, O., Camara, A., Moindrot, O., de Lavergne, A., & Courtiol, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03583 · Cited by 47 · see citing works
“[31], expert knowledge is added to the model by annotating regions of interest (the tumor area), which may be seen as a weaker form of strong supervision.”
BackgroundArtificial intelligence in gastroenterology and hepatology: Status and challenges
Cao, J., Lu, Z., Chen, M., Zhang, B., Juengpanich, S., Hu, J., Li, S., Topatana, W., Zhou, X.-Y., Xu, F., et al. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i16.1664 · Cited by 49 · see citing works
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Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review
Couture, H. D. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12122022 · Cited by 44 · see citing works
Pai, R. K., Banerjee, I., Shivji, S., Jain, S., Hartman, D., Buchanan, D. D., Jenkins, M. A., Schaeffer, D. F., Rosty, C., Como, J., et al. (2022) · Gastroenterology · doi:10.1053/j.gastro.2022.08.025 · Cited by 43 · see citing works
Chou, T., Dacic, S., Wistuba, I. I., Beasley, M. B., Berezowska, S., Chang, Y., Chung, J.-H., Connolly, C., Han, Y., Hirsch, F. R., et al. (2024) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2024.11.016 · Cited by 43 · see citing works
AI in Computational Pathology of Cancer: Improving Diagnostic Workflows and Clinical Outcomes?
Çifçi, D., Veldhuizen, G. P., Foersch, S., & Kather, J. N. (2023) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-061521-092038 · Cited by 41 · see citing works
The therapeutic and prognostic implications of immunobiology in colorectal cancer: a review
Zaborowski, A. M., Winter, D. C., & Lynch, L. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01475-x · Cited by 39 · see citing works
“Furthermore, development of image-based molecular profiling using standard histology sections and deep learning may facilitate widespread implementation of precision therapy on the basis of gene expression [15].”
Multi-modality artificial intelligence in digital pathology
Qiao, Y., Zhao, L., Luo, C., Luo, Y., Wu, Y., Li, S., Bu, D., & Zhao, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac367 · Cited by 44 · see citing works
Guo, B., Li, X., Yang, M., Jonnagaddala, J., Zhang, H., & Xu, S. X. (2023) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.312 · Cited by 39 · see citing works
Peters, N. A., Constantinides, A., Ubink, I., van Kuik, J., Bloemendal, H. J., van Dodewaard, J. M., Brink, M. A., Schwartz, T. P., Lolkema, M. P. J. K., Lacle, M. M., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.969855 · Cited by 35 · see citing works
“Several tissue-based diagnostic tools have been developed for clinical CMS stratification (2, 9, 51, 52).”
MethodFu, X., Sahai, E. A., & Wilkins, A. C. (2023) · The Journal of Pathology · doi:10.1002/path.6153 · Cited by 36 · see citing works
Reitsam, N. G., Grozdanov, V., Loeffler, C. M. L., Muti, H. S., Grosser, B., Kather, J. N., & Märkl, B. (2023) · Cancer Gene Therapy · doi:10.1038/s41417-023-00695-y · Cited by 35 · see citing works
Improving feature extraction from histopathological images through a fine-tuning ImageNet model
Li, X., Cen, M., Xu, J., Zhang, H., & Xu, X. S. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100115 · Cited by 35 · see citing works
“convolutional neural networks (CNNs), which has also been successfully applied for tumor detection, prediction of histological subtypes, genomic mutations, transcriptomic expression, molecular subtyping, and patient prognosis [1, 3-15].”
MethodStrating, E., Wassenaar, E., Verhagen, M., Rauwerdink, P., van Schelven, S., de Hingh, I., Rinkes, I. B., Boerma, D., Witkamp, A., Lacle, M., et al. (2022) · British Journal of Cancer · doi:10.1038/s41416-022-01748-z · Cited by 36 · see citing works
“The currently available diagnostic tools for CMS4 identification involve the molecular, immunohistochemical or imaging-based analysis of one or more tumour biopsies and/or sections thereof [4, 11, 14, 15].”
MethodDeep learning in digital pathology for personalized treatment plans of cancer patients
Wen, Z., Wang, S., Yang, D. M., Xie, Y., Chen, M., Bishop, J., & Xiao, G. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.003 · Cited by 34 · see citing works
Lee, S. H., & Jang, H. (2022) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2021.0394 · Cited by 38 · see citing works
The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer
Ghosh, A., Sirinukunwattana, K., Alham, N. K., Browning, L., Colling, R. T., Protheroe, A. S., Protheroe, E. A., Jones, S., Aberdeen, A., Rittscher, J., et al. (2021) · Cancers · doi:10.3390/cancers13061325 · Cited by 33 · see citing works
“Annotated slides were then exported to the AIDA platform.”
BackgroundMethodInfluentialSelf-citationYoshida, H., & Kiyuna, T. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i21.2818 · Cited by 33 · see citing works
Krieg, C., Weber, L. M., Fosso, B., Marzano, M., Hardiman, G., Olcina, M. M., Domingo, E., Aidy, S. E., Mallah, K., Robinson, M. D., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-004717 · Cited by 31 · see citing works
Self-citationFlinner, N., Gretser, S., Quaas, A., Bankov, K., Stoll, A. K., Heckmann, L., Mayer, R. S., Döring, C., Demes, M. C., Buettner, R., et al. (2022) · The Journal of Pathology · doi:10.1002/path.5879 · Cited by 31 · see citing works
“Recently, molecular subclasses based on tissue morphology were also shown to be successfully predicted using convolutional neuronal networks (cNNs) in bladder [30] and colorectal [31] cancer.”
BackgroundLafarge, M. W., Domingo, E., Sirinukunwattana, K., Wood, R., Samuel, L. M., Murray, G. I., Richman, S. D., Blake, A., Sebag‐Montefiore, D., Gollins, S., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00580-3 · Cited by 30 · see citing works
“The prognostic value of CMS classes has been reported in several studies [12, 13], yet their association with treatment outcome is an open research topic [11].”
BackgroundMethodInfluentialSelf-citationNguyen, H.-G., Blank, A., Dawson, H., Lugli, A., & Zlobec, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-81352-y · Cited by 29 · see citing works
Ding, G., Tan, W., Lin, Y., Ling, Y., Huang, W. K., Zhang, S., Shi, J.-Y., Luo, R., Ji, Y., Wang, X., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03482-0 · Cited by 30 · see citing works
Recent advances and challenges in colorectal cancer: From molecular research to treatment
Qi, G., Zhao, R., Gao, C., Ma, Z., Wang, S., & Xu, J. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i21.106964 · Cited by 31 · see citing works
Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation
Lan, Y., Huang, N., Fu, Y., Liu, K., Zhang, H., Li, Y., & Yang, S. L. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.802794 · Cited by 28 · see citing works
“Second, when we focus on image-based deep learning in the biomedical area, many other deep learning methods like YOLO (Li et al., 2021), GANs (Rubin et al., 2019; Sirinukunwattana et al., 2021) were applied and achieved good results.”
MethodBrouwer, N. P. M., Webbink, L., Haddad, T. S., Rutgers, N., van Vliet, S., Wood, C. S., Jansen, P. W. T. C., Lafarge, M. W., de Wilt, J. H. W., Hugen, N., et al. (2023) · The Journal of Pathology · doi:10.1002/path.6196 · Cited by 28 · see citing works
“This ensemble model was trained to predict CMSs from input H&E-stained WSIs using a multicohort dataset of WSIs paired with transcriptional CMS calls, covering all cancer stages among resection and biopsy specimens [15].”
BackgroundMethodResultInfluentialOral epithelial dysplasia detection and grading in oral leukoplakia using deep learning
Peng, J., Xu, Z., Dan, H., Li, J., Wang, J., Luo, X., Xu, H. Y., Zeng, X. Y., & Chen, Q. (2024) · BMC Oral Health · doi:10.1186/s12903-024-04191-z · Cited by 27 · see citing works
Deep Learning–Powered Whole Slide Image Analysis in Cancer Pathology
Dang, C., Qi, Z., Xu, T., Gu, M., Chen, J., Wu, J. J., Lin, Y., & Qi, X. (2025) · Laboratory Investigation · doi:10.1016/j.labinv.2025.104186 · Cited by 29 · see citing works
Brown, L. C., Graham, J., Fisher, D. J., Adams, R., Seligmann, J. F., Seymour, M., Kaplan, R. S., Yates, E., Parmar, M., Richman, S. D., et al. (2022) · Clinical Trials · doi:10.1177/17407745211069879 · Cited by 27 · see citing works
“It may be that the revolution in digital pathology and artificial intelligence will enable some biological stratification to be achieved directly from routine images as shown recently with consensus molecular subtyping.(26) In undertaking FOCUS4 as a platform design, we have noted some important distinctions between a…”
BackgroundSelf-citationAlam, R., Seo, K. J., Abdul‐Ghafar, J., Yim, K., Lee, S. H., Jang, H., Jung, C. K., & Chong, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad151 · Cited by 26 · see citing works
Immune subtyping of melanoma whole slide images using multiple instance learning
Godson, L., Alemi, N., Nsengimana, J., Cook, G. P., Clarke, E. L., Treanor, D. E., Bishop, D. T., Newton‐Bishop, J., Gooya, A., & Magee, D. R. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103097 · Cited by 25 · see citing works
A review of current systems for annotation of cell and tissue images in digital pathology
Korzynska, A., Roszkowiak, L., Zak, J., & Siemion, K. (2021) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.04.012 · Cited by 26 · see citing works
Chen, Z., Li, X., Yang, M., Zhang, H., & Xu, S. X. (2022) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.302 · Cited by 26 · see citing works
Zhao, S., Yan, C., Lv, H., Yang, J., You, C., Li, Z., Ma, D., Xiao, Y., Hu, J., Yang, W., et al. (2022) · Fundamental Research · doi:10.1016/j.fmre.2022.06.008 · Cited by 25 · see citing works
“Future work may tackle this problem using the domain adaption approach [ 18 , 49 ].”
Patharia, P., Sethy, P. K., & Nanthaamornphong, A. (2024) · Cancer Informatics · doi:10.1177/11769351241290608 · Cited by 24 · see citing works
“56 Sirinukunwattana et al. 87 developed an image-based method, imCMS, to predict colorectal cancer (CRC) consensus molecular subtypes (CMS) from standard H&E-stained tissue sections using deep learning.”
MethodThe impact of AI on modern oncology from early detection to personalized cancer treatment
Li, J., Zhang, L., Yu, Z., Bao, Z., Li, D., & Wang, L. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01276-6 · Cited by 24 · see citing works
Brochet, T., Lapuyade‐Lahorgue, J., Huat, A., et al. (2022) · Entropy · doi:10.3390/e24040436 · Cited by 24 · see citing works
“Classification can be used to identify automatically the kind of cancer from which the patient is suffering [28,29] or the relevant outcomes after treatment, such as survival expectation [30] or relation to the treatment [31].”
Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview
Feng, X., Shu, W., Li, M., Li, J., Xu, J., & He, M. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04915-3 · Cited by 27 · see citing works
“Similarly, deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations [24, 33].”
BackgroundLangerud, J., Eilertsen, I. A., Moosavi, S. H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48706-2 · Cited by 23 · see citing works
Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review
Yang, J., Huang, J., Han, D., & Ma, X. (2024) · Clinical Medicine Insights Oncology · doi:10.1177/11795549231220320 · Cited by 24 · see citing works
Prezja, F., Annala, L., Kiiskinen, S., Lahtinen, S., Ojala, T., Ruusuvuori, P., & Kuopio, T. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37561 · Cited by 23 · see citing works
Leonard, N. A., Corry, S. M., Reidy, E., Egan, H., O’Malley, G., Thompson, K., McDermott, E. A., O’Neill, A., Zakaria, N., Egan, L. J., et al. (2024) · iScience · doi:10.1016/j.isci.2024.110701 · Cited by 22 · see citing works
Self-citationApplications of Digital Pathology in Cancer: A Comprehensive Review
Omar, M., Alexanderani, M. K., Valencia, I., Loda, M. F., & Marchionni, L. (2024) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-062822-010523 · Cited by 21 · see citing works
Decoding pathology: the role of computational pathology in research and diagnostics
Hölscher, D. L., & Bülow, R. D. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-03002-2 · Cited by 21 · see citing works
Cao, B., Zhang, K., Wei, B., & Chen, L. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i21.2681 · Cited by 21 · see citing works
Volinsky-Fremond, S., Koelzer, V. H., Horeweg, N., & Bosse, T. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.928977 · Cited by 22 · see citing works
“Among these DL-based studies, one research angle that has got some particular interests is explaining the DL predictions, frequently referred to as a “black box” and deriving human-interpretable features (31, 36, 37, 44).”
BackgroundSelf-citationPrezja, F., Äyrämö, S., Pölönen, I., Ojala, T., Lahtinen, S., Ruusuvuori, P., & Kuopio, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-42357-x · Cited by 21 · see citing works
Han, X. G., Weng, Z., Sun, K., Shen, J., Lin, J., Chen, S., Ли, Б., Shi, Y., Kuang, M., Song, X., et al. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02573-2 · Cited by 21 · see citing works
“…have been some studies con fi rmed the feasibility of further in-depth analysis of histological images through deep-learning algorithm, such as classifying consensus molecular subtypes [24], predicting microsatellite instability [25, 26], and developing a biomarker of cancer-speci fi c survival [27].”
Artificial intelligence in tumor drug resistance: Mechanisms and treatment prospects
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Deep learning model to predict Epstein–Barr virus associated gastric cancer in histology
Jeong, Y., Cho, C. E., Kim, J.-E., Lee, J., Kim, N., Jung, W. Y., Sung, J., Kim, J. H., Lee, Y. J., Jung, J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-22731-x · Cited by 22 · see citing works
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Kantha, A., Das, D., Pai, E., Kumar, T., & Pandey, M. (2025) · World Journal of Surgical Oncology · doi:10.1186/s12957-025-04117-1 · Cited by 19 · see citing works
“Histological grading and other traditional techniques are not good predictors [4].”
InfluentialComprehensive Imaging Characterization of Colorectal Liver Metastases
Maclean, D., Tsakok, M., Gleeson, F. V., Breen, D. J., Goldin, R. D., Primrose, J. M., Harris, A. L., & Franklin, J. M. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.730854 · Cited by 19 · see citing works
“As yet, there have been no studies linking radiological imaging to CMS subtypes, although deep learning technologies have been applied to histological images to predict CMS subtypes and advanced imaging processing techniques (56), which are discussed in more detail below, have been used to classify other molecular fea…”
BackgroundState of machine and deep learning in histopathological applications in digestive diseases
Kobayashi, S., Saltz, J. H., & Yang, V. W. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i20.2545 · Cited by 20 · see citing works
- Authors
- Sirinukunwattana, K., Domingo, E., Richman, S. D., Redmond, K. L., Blake, A., Verrill, C., Leedham, S. J., Chatzipli, A., Hardy, C., Whalley, C. M., Wu, C.-H., Beggs, A. D., McDermott, U., Dunne, P. D., Meade, A., Walker, S. M., Murray, G. I., Samuel, L., Seymour, M., Tomlinson, I., Quirke, P., Maughan, T., Rittscher, J., & Koelzer, V. H.
- Affiliations
- University of Oxford (GB)
- University of Leeds (GB)
- Queen's University Belfast (GB)
- Centre for Human Genetics (GB)
- Wellcome Sanger Institute (GB)
- University of Birmingham (GB)
- MRC Clinical Trials Unit at UCL (GB)
- Almac (United Kingdom) (GB)
- University of Aberdeen (GB)
- Aberdeen Royal Infirmary (GB)
- Department of Oncology, Leeds Institute of Cancer and Pathology, Leeds, UK
- Edinburgh Cancer Research (GB)
- Department of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, Leeds, UK
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
- University of Zurich (CH)
- Corresponding author
- Koelzer, Viktor H
- ORCID
- Sirinukunwattana, Korsuk
- Domingo, Enric
- Richman, Susan D
- Redmond, Keara L
- Blake, Andrew
- Verrill, Clare
- Leedham, Simon J
- Chatzipli, Aikaterini
- Whalley, Celina M
- Wu, Chieh-hsi
- Beggs, Andrew D
- McDermott, Ultan
- Dunne, Philip D
- Meade, Angela
- Walker, Steven M
- Murray, Graeme I
- Samuel, Leslie
- Tomlinson, Ian
- Maughan, Timothy
- Rittscher, Jens
- Koelzer, Viktor H
- Venue
- Gut
- Publisher
- BMJ
- Published
- 2020-07-20
- Volume
- 70
- Issue
- 3
- Pages
- 544-554
- ISSN
- 0017-5749 (ISSN-L)
- 1468-3288
- PMC
- PMC7873419
- OpenAlex
- W3043602140
- Semantic Scholar
- 9531fb91f6876c57e3cf9bcb77ec79cc6f2a84d3
- Cited by
- 264
- References
- 30
- Citations by year
- 2026: 35 · 2025: 40 · 2024: 63 · 2023: 37 · 2022: 57 · 2021: 28 · 2020: 4
- FWCI
- 12.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Gut (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (Queen's University Belfast) (publishedVersion, cc-by)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Greater South Information System
- Funders
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre
- Engineering and Physical Sciences Research Council (EP/M013774/1)
- Medical Research Council UK
- Cancer Research UK
- Promedica Foundation (F-87701-41-01)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (P2SKP3_168322/1)
- 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 › Pathology and Forensic Medicine › Genetic factors in colorectal cancer
- Keywords
- colorectal cancer, consensus molecular subtypes, image classification, H&E histology, deep learning, intratumoral heterogeneity, rectal cancer, histopathology, molecular classification, prognostic stratification, epigenetic modifications
- MeSH terms
- Deep Learning, Biopsy, Humans, Phenotype, Predictive Value of Tests, Prognosis, RNA, Biomarkers, Tumor, Colorectal Neoplasms, Gene Expression Regulation, Neoplastic, Disease Progression, Gene Expression Profiling, Consensus, Neoplasm Grading, Datasets as Topic
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