Inter-pathologist agreement for nuclear atypia scoring of breast cancer is poor. To address this problem, previous studies suggested some criteria for describing the variations appearance of tumor cells relative to normal cells. However, these criteria were still assessed subjectively by pathologists. Previous studies used quantitative computer-extracted features for scoring. However, application of these tools is limited as further improvement in their accuracy is required. This study proposes COMPASS (COMputer-assisted analysis combined with Pathologist’s ASSessment) for reproducible nuclear atypia scoring. COMPASS relies on both cytological criteria assessed subjectively by pathologists as well as computer-extracted textural features. Using machine learning, COMPASS combines these two sets of features and output nuclear atypia score. COMPASS’s performance was evaluated using 300 images for which expert-consensus derived reference nuclear pleomorphism scores were available, and they were scanned by two scanners from different vendors. A personalized model was built for three pathologists who gave scores to six atypia-related criteria for each image. Leave-one-out cross validation (LOOCV) was used. COMPASS was trained and tested for each pathologist separately. Percentage agreement between COMPASS and the reference nuclear scores was 93.8%, 92.9%, and 93.1% for three pathologists. COMPASS’s performance in nuclear grading was almost identical for both scanners, with Cohen’s kappa ranging from 0.80 to 0.86 for different pathologists and different scanners. Independently, the images were also assessed by two experienced senior pathologists. Cohen’s kappa of COMPASS was comparable to the Cohen’s kappa for two senior pathologists (0.79 and 0.68).
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We would like to thank Mitosis-Atypia challenge organizers who collected the data utilized in the study and kindly provided us the access to their dataset after the challenge time. We also acknowledge the University of Sydney HPC Service at the University of Sydney for providing the high-performance computing resources that have contributed to the research results reported within this paper.
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Gandomkar, Z., Brennan, P.C. & Mello-Thoms, C. Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study. J Digit Imaging 32, 702–712 (2019). https://doi.org/10.1007/s10278-019-00181-8
- Breast cancer
- Nuclear atypia grading
- Nuclear pleomorphism grading
- Pattern recognition