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Extraction of high-dimensional quantitative data from medical images has become necessary in disease risk assessment,
diagnostics and prognostics. Radiomic workflows for mammography typically involve a single medical image for each
patient although medical images may exist for multiple imaging exams, especially in screening protocols. Our study
takes advantage of the availability of mammograms acquired over multiple years for the prediction of cancer onset. This
study included 841 images from 328 patients who developed subsequent mammographic abnormalities, which were
confirmed as either cancer (n=173) or non-cancer (n=155) through diagnostic core needle biopsy. Quantitative radiomic
analysis was conducted on antecedent FFDMs acquired a year or more prior to diagnostic biopsy. Analysis was limited
to the breast contralateral to that in which the abnormality arose. Novel metrics were used to identify robust radiomic
features. The most robust features were evaluated in the task of predicting future malignancies on a subset of 72 subjects
(23 cancer cases and 49 non-cancer controls) with mammograms over multiple years. Using linear discriminant analysis,
the robust radiomic features were merged into predictive signatures by: (i) using features from only the most recent
contralateral mammogram, (ii) change in feature values between mammograms, and (iii) ratio of feature values over
time, yielding AUCs of 0.57 (SE=0.07), 0.63 (SE=0.06), and 0.66 (SE=0.06), respectively. The AUCs for temporal
radiomics (ratio) statistically differed from chance, suggesting that changes in radiomics over time may be critical for
risk assessment. Overall, we found that our two-stage process of robustness assessment followed by performance
evaluation served well in our investigation on the role of temporal radiomics in risk assessment.
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