ArticleInternational journal of cancer2020
Significant inter- and intra-laboratory variation in grading of invasive breast cancer: A nationwide study of 33,043 patients in the Netherlands.
Article in International journal of cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI.European radiology · 2026Article
- Global scoring method of Ki67 immunohistochemistry in breast cancer demonstrates improved concordance using real-world multi-institutional data.Breast cancer research : BCR · 2025Article
- Artificial Intelligence-Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer.Cancer medicine · 2025Review
- Clinicians must participate in the development of multimodal AI.EClinicalMedicine · 2025Review
- Machine learning-based single-sample molecular classifier for cancer grading.Frontiers in oncology · 2025Article
- Breast cancer survival prediction using an automated mitosis detection pipeline.The journal of pathology. Clinical research · 2024Article
- Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images.Breast cancer research : BCR · 2024Article
- Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.Breast cancer research : BCR · 2024Article
- Identification of Patients with Early HR+ HER2- Breast Cancer at High Risk of Recurrence.Geburtshilfe und Frauenheilkunde · 2024Article
- Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer.Breast cancer research : BCR · 2024Observational
- High-content stimulated Raman histology of human breast cancer.Theranostics · 2024Article
- Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists' Assessment Approach.Diagnostics (Basel, Switzerland) · 2023Article
- CONFIDENT-trial protocol: a pragmatic template for clinical implementation of artificial intelligence assistance in pathology.BMJ open · 2023Article
- Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer.NPJ breast cancer · 2023Article
- Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow.Journal of pathology informatics · 2023Article
- Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.Scientific reports · 2022Article
- Integrated multi-omics profiling of high-grade estrogen receptor-positive, HER2-negative breast cancer.Molecular oncology · 2022Article
- Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective.Diagnostics (Basel, Switzerland) · 2022Review
- Agreement between core needle biopsy and surgical excision product: the importance of the invasive breast carcinoma grading system.Virchows Archiv : an international journal of pathology · 2022Article
- Systematic reporting of medical kidney biopsies.Clinical kidney journal · 2022Article
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7 authors.
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Abstract
Accurate, consistent and reproducible grading by pathologists is of key-importance for identification of individual patients with invasive breast cancer (IBC) that will or will not benefit from adjuvant systemic treatment. We studied the laboratory-specific grading variation using nationwide real-life data to create insight and awareness in grading variation. Synoptic pathology reports of all IBC resection-specimens, obtained between 2013 and 2016, were retrieved from the nationwide Dutch Pathology Registry (PALGA). Absolute differences in laboratory-proportions of Grades I-III were compared to the national reference. Multivariable logistic regression provided laboratory-specific odds ratios (ORs) for high- vs. low-grade IBC. 33,792 IBC pathology reports of 33,043 patients from 39 laboratories were included, of which 28.1% were reported as Grade I (range between laboratories 16.3-43.3%), 47.6% as Grade II (38.4-57.8%), and 24.3% as Grade III (15.5-34.3%). Based on national guidelines, the indication for adjuvant chemotherapy was dependent on histologic grade in 29.9% of patients. After case-mix correction, 20 laboratories (51.3%) showed a significantly deviant OR. Significant grading differences were also observed among pathologists within laboratories. In this cohort of 33,043 breast cancer patients, we observed substantial inter- and intra-laboratory variation in histologic grading. It can be anticipated that this has influenced outcome including exposure to unnecessary toxicity, since choice of adjuvant chemotherapy was dependent on grade in nearly a third of patients. Better standardization and training seems warranted.
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