Computer Vision Identifies Recurrent and Nonrecurrent Ductal Carcinoma in Situ Lesions with Special Emphasis on African-American Women

Abstract

Although nonrecurrent and recurrent forms of ductal carcinoma in situ (DCIS) of the breast are observed, no evidence-based test can make this distinction. The current retrospective case-control study used archival DCIS samples stained with anti-phospho-Ser226-glucose transporter type 1 and anti-phosphofructokinase type L antibodies. Immunofluorescence micrographs were used to create machine learning models of recurrent and nonrecurrent biomarker patterns, which were evaluated in cross-validation studies. Clinical performance was assessed by holdout studies using patients whose data were not used in training. Micrographs were stratified according to the recurrence probability of each image. Recurrent patients were defined by at least one image with a probability of recurrence ≥98%, whereas nonrecurrent patients had none. These studies found no false-negatives, identified true-positives, and uniquely identified true-negatives. Roughly 20% of the microscope fields of recurrent lesions were computationally recurrent. Strong prognostic results were obtained for both white and African-American women. This machine tool provides the first means to accurately predict recurrent and nonrecurrent patient outcomes. Data indicate that at least some false-positive findings were true-positive findings that benefited from surgical intervention. The intracellular locations of phospho-Ser226-glucose transporter type 1 and phosphofructokinase type L likely participate in cancer recurrences by accelerating glucose flux, a key feature of the Warburg effect.