ArticleScientific reports2022
Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
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- Beyond Color: Hybrid Vibrational-Electronic Broadband Coherent Anti-Stokes Raman Scattering for Molecularly Informed Digital Pathology.Analytical chemistry · 2026Article
- Towards deep-learning based detection and quantification of intestinal metaplasia on digitized gastric biopsies: a multi-expert comparative study.Scientific reports · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.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
- A deep learning framework to iDentify prOgnostically releVant cancEr Regions (DOVER) within whole slide histopathology images.Cancer letters · 2026Article
- Advancements in Cancer Survival Prediction: A Systematic Review of Classical and Modern Approaches.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2025Review
- Cross-slide augmentation for whole slide image classification based on class activation map.Scientific reports · 2025Article
- A Multimodal Diagnostic Model for Breast Cancer Invasiveness Based on Ultrasound Imaging and Serum Biomarkers.Medicina (Kaunas, Lithuania) · 2025Article
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- Laser speckle particle sizer (SPARSE) informs the size distribution of tissue granularities.Science advances · 2025Article
- Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.Genome medicine · 2025Article
- Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer.Journal of pathology informatics · 2025Article
- Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis.Science advances · 2025Article
- Estimation of time-to-total knee replacement surgery with multimodal modeling and artificial intelligence.Computers in biology and medicine · 2025Article
- A method for spatial interpretation of weakly supervised deep learning models in computational pathology.Scientific reports · 2025Article
- Enhanced nuclear information fusion and visual transformer for pathological breast cancer image classification.Scientific reports · 2025Article
- Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.Journal of imaging informatics in medicine · 2025Article
- Predicting Nottingham grade in breast cancer digital pathology using a foundation model.Breast cancer research : BCR · 2025Article
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8 authors.
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Abstract
Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in breast cancer grading. Computer-based breast cancer grading methods have been proposed but only work on specifically selected tissue areas and/or require labor-intensive annotations to be applied to new datasets. In this study, we trained and evaluated a deep learning-based breast cancer grading model that works on whole-slide histopathology images. The model was developed using whole-slide images from 706 young (< 40 years) invasive breast cancer patients with corresponding tumor grade (low/intermediate vs. high), and its constituents nuclear grade, tubule formation and mitotic rate. The performance of the model was evaluated using Cohen's kappa on an independent test set of 686 patients using annotations by expert pathologists as ground truth. The predicted low/intermediate (n = 327) and high (n = 359) grade groups were used to perform survival analysis. The deep learning system distinguished low/intermediate versus high tumor grade with a Cohen's Kappa of 0.59 (80% accuracy) compared to expert pathologists. In subsequent survival analysis the two groups predicted by the system were found to have a significantly different overall survival (OS) and disease/recurrence-free survival (DRFS/RFS) (p < 0.05). Univariate Cox hazard regression analysis showed statistically significant hazard ratios (p < 0.05). After adjusting for clinicopathologic features and stratifying for molecular subtype the hazard ratios showed a trend but lost statistical significance for all endpoints. In conclusion, we developed a deep learning-based model for automated grading of breast cancer on whole-slide images. The model distinguishes between low/intermediate and high grade tumors and finds a trend in the survival of the two predicted groups.
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