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BACKGROUND: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. METHODS: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. RESULTS: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. CONCLUSION: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.

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BACKGROUND: To assess reproducibility and accuracy of overall Nottingham grade and component scores using digital whole slide images (WSIs) compared to glass slides. METHODS: Two hundred and eight pathologists were randomized to independently interpret 1 of 4 breast biopsy sets using either glass slides or digital WSI. Each set included 5 or 6 invasive carcinomas (22 total invasive cases). Participants interpreted the same biopsy set approximately 9 months later following a second randomization to WSI or glass slides. Nottingham grade, including component scores, was assessed on each interpretation, providing 2045 independent interpretations of grade. Overall grade and component scores were compared between pathologists (interobserver agreement) and for interpretations by the same pathologist (intraobserver agreement). Grade assessments were compared when the format (WSI vs. glass slides) changed or was the same for the two interpretations. RESULTS: Nottingham grade intraobserver agreement was highest using glass slides for both interpretations (73%, 95% confidence interval [CI]: 68%, 78%) and slightly lower but not statistically different using digital WSI for both interpretations (68%, 95% CI: 61%, 75%; P= 0.22). The agreement was lowest when the format changed between interpretations (63%, 95% CI: 59%, 68%). Interobserver agreement was significantly higher (P < 0.001) using glass slides versus digital WSI (68%, 95% CI: 66%, 70% versus 60%, 95% CI: 57%, 62%, respectively). Nuclear pleomorphism scores had the lowest inter- and intra-observer agreement. Mitotic scores were higher on glass slides in inter- and intra-observer comparisons. CONCLUSIONS: Pathologists' intraobserver agreement (reproducibility) is similar for Nottingham grade using glass slides or WSI. However, slightly lower agreement between pathologists suggests that verification of grade using digital WSI may be more challenging.

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Objective: We aimed to assess the impact of Nottingham grade (NG) on breast cancer specific survival (BCSS) and recurrence free survival (RFS) of operable breast cancer (BC) patients presenting at different TNM stages in view of assessing the value of NG in prognostication of breast cancer in the Sri Lankan setting. Method: This retro-prospective study included a consecutive series of TNM stage I to III BC patients presented to our unit from 2006 to 2012. Data were collected through follow up visits, clinic and laboratory records. Grading and scoring of oestrogen receptors (ER), progesterone receptors (PR) and human epidermal growth factor receptor 2 (Her2) expressions were done by a single investigator. Kaplan-Meier and Cox-regression models were used in the survival analysis. Results: A total of 742 (NG1-12%; NG2-45%; NG3-43%) patients with a median follow up of 39.5 (range: 12 - 138) months were included. Five-year BCSS was 94%-NG1, 80%-NG2 and 72%-NG3 (p < 0.001). Five-year RFS was 86%-NG1, 75%-NG2 and 67%-NG3 (p = 0.001). Only the lymph-node status (LNS) (p = 0.001) had an independent effect on the BCSS and RFS of NG3 patients. LNS (p = 0.001), PR (p = 0.004) and Her2 (p < 0.001) independently affected the BCSS of NG2 patients. None of the factors considered had an effect on the BCSS/RFS of NG1 patients. A significant decrease in BCSS and RFS was seen with an increase in NG in the sub-group of TNM stage III (p = 0.01 and 0.011). Conclusion: NG categorizes BC patients into prognostic groups with distinctly different survival outcomes. Sub-categorization of TNM stage III by NG is suggested.