Improved breast cancer histological grading using deep learning
Open accessAbstract
BACKGROUND: The Nottingham histological grade (NHG) is a well-established prognostic factor for breast cancer that is broadly used in clinical decision making. However, ∼50% of patients are classified as grade 2, an intermediate risk group with low clinical value. To improve risk stratification of NHG 2 breast cancer patients, we developed and validated a novel histological grade model (DeepGrade) based on digital whole-slide histopathology images (WSIs) and deep learning. PATIENTS AND METHODS: In this observational retrospective study, routine WSIs stained with haematoxylin and eosin from 1567 patients were utilised for model optimisation and validation. Model generalisability was further evaluated in an external test set with 1262 patients. NHG 2 cases were stratified into two groups, DG2-high and DG2-low, and the prognostic value was assessed. The main outcome was recurrence-free survival. RESULTS: DeepGrade provides independent prognostic information for stratification of NHG 2 cases in the internal test set, where DG2-high showed an increased risk for recurrence (hazard ratio [HR] 2.94, 95% confidence interval [CI] 1.24-6.97, P = 0.015) compared with the DG2-low group after adjusting for established risk factors (independent test data). DG2-low also shared phenotypic similarities with NHG 1, and DG2-high with NHG 3, suggesting that the model identifies morphological patterns in NHG 2 that are associated with more aggressive tumours. The prognostic value of DeepGrade was further assessed in the external test set, confirming an increased risk for recurrence in DG2-high (HR 1.91, 95% CI 1.11-3.29, P = 0.019). CONCLUSIONS: The proposed model-based stratification of patients with NHG 2 tumours is prognostic and adds clinically relevant information over routine histological grading. The methodology offers a cost-effective alternative to molecular profiling to extract information relevant for clinical decisions.
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AI-driven whole slide pathology analysis has been applied in diagnosis, prognosis, and efficacy prediction.
“Artificial intelligence (AI)–driven whole slide pathology analysis has been applied in diagnosis, prognosis, and efficacy prediction, and it has become a research hotspot (14, 24, 25).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [24]
The Introduction (p. 2) describes AI analysis of whole-slide images for cancer detection and classification, and the Results (p. 1) report prognostic stratification using such images. The paper does not report prediction of treatment efficacy: its Conclusion (p. 9) describes chemotherapy decision-making as a potential application, not an evaluated outcome. (LLM, conf=0.87)
Experimental — accuracy not yet measuredWang and colleagues developed DeepGrade, a deep learning model aiming to improve on the Nottingham grading system.
“Wang and colleagues (58) have developed DeepGrade, a deep learning model that aims to improve on the Nottingham grading system.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [58]
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- Authors
- Wang, Y., Acs, B., Robertson, S., Liu, B., Solorzano, L., Wählby, C., Hartman, J., & Rantalainen, M.
- Affiliations
- Karolinska Institutet (SE)
- Karolinska University Hospital (SE)
- Uppsala University (SE)
- Corresponding author
- Rantalainen, M.
- Venue
- Annals of Oncology
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 33
- Issue
- 1
- Pages
- 89-98
- ISSN
- 0923-7534 (ISSN-L)
- OpenAlex
- W3203189213
- Cited by
- 211
- References
- 48
- Citations by year
- 2026: 26 · 2025: 62 · 2024: 59 · 2023: 52 · 2022: 12
- FWCI
- 10.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Annals of Oncology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- 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 › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- breast cancer, Nottingham histological grade, histological grading, deep learning, prognostic stratification, recurrence-free survival, risk stratification, H&E staining, tumor aggressiveness
- MeSH terms
- Deep Learning, Breast Neoplasms, Female, Humans, Prognosis, Retrospective Studies, Proportional Hazards Models, Neoplasm Grading
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