Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images
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Abstract
Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNN architectures to analyze 27,815 hematoxylin and eosin scanned images from The Cancer Genome Atlas for tumor/normal, cancer subtype, and mutation classification. Our CNNs are able to classify TCGA pathologist-annotated tumor/normal status of whole slide images (WSIs) in 19 cancer types with consistently high AUCs (0.995 ± 0.008), as well as subtypes with lower but significant accuracy (AUC 0.87 ± 0.1). Remarkably, tumor/normal CNNs trained on one tissue are effective in others (AUC 0.88 ± 0.11), with classifier relationships also recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45 ± 0.16 between classifier pairs. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. Patterns for TP53 mutations can also be detected, with WSI self- and cross-tissue AUCs ranging from 0.65-0.80. Finally, we comparatively evaluate CNNs on 170 breast and colon cancer images with pathologist-annotated nuclei, finding that both cellular and intercellular regions contribute to CNN accuracy. These results demonstrate the power of CNNs not only for histopathological classification, but also for cross-comparisons to reveal conserved spatial behaviors across tumors.
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Many morphological features are observed across cancers from different topographies.
“Indeed, it has been shown that training a model on multiple tissues or cancer variants can improve detection performance for each cancer53, as many morphological features are observed across cancers from different topographies54.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [54]
Increased diversity helps to learn robust and rare features.
“although we posit that increased diversity helps to learn robust and rare features. Indeed, it has been shown that training a model on multiple tissues or cancer variants can improve detection performance for each cancer53, as many morphological features are observed across cancers from different topographies54.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [54]
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- Authors
- Noorbakhsh, J., Farahmand, S., Foroughi pour, A., Namburi, S., Caruana, D., Rimm, D., Soltanieh-ha, M., Zarringhalam, K., & Chuang, J. H.
- Affiliations
- The Jackson Laboratory for Genomic Medicine (US)
- University of Massachusetts Boston (US)
- Yale University (US)
- Boston University (US)
- Corresponding author
- Chuang, Jeffrey H.
- ORCID
- Venue
- Nature Communications
- Published
- 2020-12-11
- Volume
- 11
- Issue
- 1
- Pages
- 6367-6367
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7733499
- OpenAlex
- W3113328935
- Cited by
- 206
- References
- 64
- Citations by year
- 2026: 16 · 2025: 32 · 2024: 44 · 2023: 47 · 2022: 41 · 2021: 20 · 2020: 5 · 2019: 1
- FWCI
- 11.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OpenBU (Boston University) (publishedVersion, cc-by)
- OpenBU (Boston University) (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion, cc-by)
- OpenBU (Boston University) (submittedVersion, cc-by-nc-nd)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (R01CA230031)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- histopathology image analysis, whole slide images, convolutional neural networks, tumor subtypes, H&E staining, TCGA, breast cancer, colon cancer, nuclei segmentation
- MeSH terms
- Deep Learning, Breast Neoplasms, Colonic Neoplasms, Female, Genotype, Humans, Image Processing, Computer-Assisted, Mutation, Neoplasms, Spatial Behavior, Genes, p53, Computational Biology, Area Under Curve
About these checks
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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