A Novel Data Analytics-derived Metric (Nearest Cluster Distance) Is Easily Implemented in Routine Practice and Correctly Identifies Breast Cancer Cases for Quality Review

BACKGROUND: Errors in breast cancer grading and predictive testing are clinically important and can be difficult to detect in routine practice. A quality metric able to identify a subset of breast cancer cases which are high yield on quality review would be of practical clinical benefit. METHODS: Data analytic techniques were used to generate consensus tumor signature centers from a dataset over 500 breast cancer cases from a single practice. Cases were assigned a novel metric, Nearest Cluster Distance, corresponding to their distances from the nearest tumor signature center. The subset of tumors exceeding a cutoff for this metric were flagged, and then reviewed and rescored in a blinded fashion together with matched controls. A simplified version of this metric was created using universally accessible methods. RESULTS: Flagged cases showed statistically significant movement toward consensus tumor signature centers compared with controls, consistent with identification of cases which could benefit from review and possible rescoring. The simplified metric performs identically. CONCLUSION: This method can be readily applied in routine practice and is promising as a real time quality check for breast cancer diagnosis and reporting.