Study Background
Approximately 15-25% of patients with atypical ductal hyperplasia (ADH) on breast core needle biopsy (CNB) are upgraded to ductal carcinoma in situ (DCIS) or invasive carcinoma (IC) on surgical excision.
- We hypothesized that a machine learning approach could be utilized to train models to recognize ADH on digitized pathology images and to identify cases of ADH more likely to be upgraded to DCIS or IC at excision.
- Here we demonstrate the accuracy of the machine learning approach to identify ADH.
