ArticleNPJ precision oncology2024
AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A Boveri perspective on cancer biomarker testing using artificial intelligence.NPJ precision oncology · 2026Review
- Toward comprehensive cellular characterization of H&E slides.Journal of pathology informatics · 2026Article
- Multiscale Architecture and Mechanics of the Cell Nucleus: Implications for Disease, Bioengineering and Nanomedicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Paclitaxel compromises nuclear integrity in interphase through SUN2-mediated cytoskeletal coupling.Journal of cell science · 2026Article
- NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities.Scientific reports · 2026Article
- Nuclear Mechanotransduction Across the Metastatic Cascade: Decoding Spatiotemporal Heterogeneity in Cancer Dissemination.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Correlation Between Molecular Genetic Analysis and Nuclear Pleomorphism in Long-Term Recurrent and Metastatic Chordoma.Cancers · 2026Article
- Image Feature Fusion of Hyperspectral Imaging and MRI for Automated Subtype Classification and Grading of Adult Diffuse Gliomas According to the 2021 WHO Criteria.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-based quantitative histopathology of endoscopic biopsies in Crohn's disease: a retrospective cross-sectional validation study.Frontiers in immunology · 2026Article
- Aging reimagined: Bridging clinical modulation and scientific breakthroughs.Biology of sport · 2026Review
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps.Analytical cellular pathology (Amsterdam) · 2026Review
- Nuclear destabilisation - a possible genesis of cancer?Biological reviews of the Cambridge Philosophical Society · 2025Review
- Catalytic activity of FAHD1 correlates with nuclear morphology and proteomic states in human osteosarcoma cells.Scientific reports · 2025Article
- Establishment of an imaging-based screening pipeline for the identification of human ribosome biogenesis inhibitors.BMC biology · 2025Article
- Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features.bioRxiv : the preprint server for biology · 2025Article
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma.NPJ precision oncology · 2025Article
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.
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Registered trials
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