ArticleLaboratory investigation; a journal of technical methods and pathology2018
Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers.
Article in Laboratory investigation; a journal of technical methods and pathology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 2 of them syntheses that pooled it.
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Who cites it
78 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The impact of intratumor heterogeneity on outcome and resistance to therapy in primary breast cancer: a systematic review.Medical oncology (Northwood, London, England) · 2026Pooled it
- Application of artificial intelligence and radiomics in the prediction of lymph node metastasis and tumour grading of oral cancer - a systematic review and meta analysis.BMC oral health · 2026Pooled it
- Beyond Color: Hybrid Vibrational-Electronic Broadband Coherent Anti-Stokes Raman Scattering for Molecularly Informed Digital Pathology.Analytical chemistry · 2026Article
- Clinical Implications of Polyploid Giant Cancer Cells in Solid Tumors: Biology, Diagnosis, and Therapeutic Considerations.Cancers · 2026Review
- Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Quantitative Histomorphometric Analysis of Collagen Bundles in Masson's Trichrome Stained Rat (Health science reports · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Enhancing deep learning interpretability for hand-crafted feature-guided histologic image classification via weak-to-strong generalization.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Prognostic value of nuclear features based on tumor-associated collagen signatures in breast cancer.NPJ breast cancer · 2025Article
- BoxCell: Leveraging SAM for Cell Segmentation with Box Supervision.Scientific reports · 2025Article
- Breast cancer detection based on histological images using fusion of diffusion model outputs.Scientific reports · 2025Article
- Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer.NPJ precision oncology · 2025Article
- Development of model for identifying homologous recombination deficiency (HRD) status of ovarian cancer with deep learning on whole slide images.Journal of translational medicine · 2025Article
- Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry.Veterinary pathology · 2025Article
- Actin from within - how nuclear myosins and actin regulate nuclear architecture and mechanics.Journal of cell science · 2025Review
- Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling.Bioengineering (Basel, Switzerland) · 2025Article
- The tumour histopathology "glossary" for AI developers.PLoS computational biology · 2025Review
- Artificial intelligence in breast cancer diagnosis: A systematic literature review.Cambridge prisms. Precision medicine · 2025Review
- Histomorphometric Analysis of Oral Squamous Cell Carcinoma, Apparently Normal Adjacent Mucosa and Epithelial Dysplasia.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Keep it accurate and robust: An enhanced nuclei analysis framework.Computational and structural biotechnology journal · 2024Article
18 more citing papers are in PubMed but not listed here.
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Abstract
Early-stage estrogen receptor-positive (ER+) breast cancer (BCa) is the most common type of BCa in the United States. One critical question with these tumors is identifying which patients will receive added benefit from adjuvant chemotherapy. Nuclear pleomorphism (variance in nuclear shape and morphology) is an important constituent of breast grading schemes, and in ER+ cases, the grade is highly correlated with disease outcome. This study aimed to investigate whether quantitative computer-extracted image features of nuclear shape and orientation on digitized images of hematoxylin-stained and eosin-stained tissue of lymph node-negative (LN-), ER+ BCa could help stratify patients into discrete (<10 years short-term vs. >10 years long-term survival) outcome groups independent of standard clinical and pathological parameters. We considered a tissue microarray (TMA) cohort of 276 ER+, LN- patients comprising 150 patients with long-term and 126 patients with short-term overall survival, wherein 177 randomly chosen cases formed the modeling set, and 99 remaining cases the test set. Segmentation of individual nuclei was performed using multiresolution watershed; subsequently, 615 features relating to nuclear shape/texture and orientation disorder were extracted from each TMA spot. The Wilcoxon's rank-sum test identified the 15 most prognostic quantitative histomorphometric features within the modeling set. These features were then subsequently combined via a linear discriminant analysis classifier and evaluated on the test set to assign a probability of long-term vs. short-term disease-specific survival. In univariate survival analysis, patients identified by the image classifier as high risk had significantly poorer survival outcome: hazard ratio (95% confident interval) = 2.91(1.23-6.92), p = 0.02786. Multivariate analysis controlling for T-stage, histology grade, and nuclear grade showed the classifier to be independently predictive of poorer survival: hazard ratio (95% confident interval) = 3.17(0.33-30.46), p = 0.01039. Our results suggest that quantitative histomorphometric features of nuclear shape and orientation are strongly and independently predictive of patient survival in ER+, LN- BCa.
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