ArticleiScience2021
Deep learning predicts chromosomal instability from histopathology images.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 42 citations in OpenAlex.
- Cell and Nuclear Size Is Associated with Chromosomal Instability and Tumorigenicity in Cancer Cells That Undergo Whole Genome Doubling.Cancer research · 2026Article
- Interferon Epsilon Loss Is Elusive 9p21 Link to Immune-Cold Tumors, Resistant to Immune Checkpoint Therapy, and Endogenous CXCL9/10 Induction.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2025Review
- Chromosomal Instability and Clonal Heterogeneity in Breast Cancer: From Mechanisms to Clinical Applications.Cancers · 2025Review
- Targeting chromosomally unstable tumors with a selective KIF18A inhibitor.Nature communications · 2025Article
- Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer.Biomolecules & biomedicine · 2024Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Integrating histopathology and transcriptomics for spatial tumor microenvironment profiling in a melanoma case study.NPJ precision oncology · 2024Article
- Targeting chromosomal instability in patients with cancer.Nature reviews. Clinical oncology · 2024Review
- Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine.Journal of personalized medicine · 2024Review
- The yin and yang of chromosomal instability in prostate cancer.Nature reviews. Urology · 2024Review
- Joint global and local interpretation method for CIN status classification in breast cancer.Heliyon · 2024Article
- Chromosomal instability in aneuploid acute lymphoblastic leukemia associates with disease progression.EMBO molecular medicine · 2024Article
- Deep learning-based histological predictions of chromosomal instability in colorectal cancer.American journal of cancer research · 2024Article
- One label is all you need: Interpretable AI-enhanced histopathology for oncology.Seminars in cancer biology · 2023Review
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Spatial cellular architecture predicts prognosis in glioblastoma.Nature communications · 2023Article
- A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images.iScience · 2023Article
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Single-cell Heterogeneity-aware Transformer-guided Multiple Instance Learning for Cancer Aneuploidy Prediction from Whole Slide Histopathology Images.IEEE journal of biomedical and health informatics · 2023Article
- Chromosomal Instability as Enabling Feature and Central Hallmark of Breast Cancer.Breast cancer (Dove Medical Press) · 2023Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Chromosomal instability (CIN) is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available hematoxylin and eosin histology through a deep learning-based model. When applied to a cohort of 1,010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of CIN status suggested intra-tumor heterogeneity within slides. Moreover, presence of patches with high predicted CIN score within an entire slide was more predictive of clinical outcome than the average CIN score of the slide, thus underscoring the clinical importance of intra-tumor heterogeneity.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.