Subtraction of Temporally Sequential Digital Mammograms: Prediction and Localization of Near-Term Breast Cancer Occurrence.

Abstract

The objective is to predict a possible near-term occurrence of a breast mass after two consecutive screening rounds with normal mammograms. For the purposes of this study, conducted between 2020 and 2024, three consecutive rounds of mammograms were collected from 75 women, 46 to 79 years old. Successive screenings had an average interval of $\sim$ 2 years. In each case, two mammographic views of each breast were collected, resulting in a dataset with a total of 450 images (3 × 2 × 75). The most recent mammogram was considered the "future" screening round and provided the location of a biopsy-confirmed malignant mass, serving as the ground truth for the training. The two normal previous mammograms ("prior" and "current") were processed and a new subtracted image was created for the prediction. Region segmentation and post-processing were, then, applied, along with image feature extraction and selection. The selected features were incorporated into several classifiers and by applying leave-one-patient-out and k-fold cross-validation per patient, the regions of interest were characterized as benign or possible future malignancy. Study participants included 75 women (mean age, 62.5 ± 7.2; median age, 62 years). Feature selection from benign and possible future malignancy areas revealed that 14 features provided the best classification. The most accurate classification performance was achieved using ensemble voting, with 98.8% accuracy, 93.6% sensitivity, 98.8% specificity, and 0.96 AUC. Given the success of this algorithm, its clinical application could enable earlier diagnosis and improve prognosis for patients identified as at risk.