Commercially Available Artificial Intelligence Score on Preoperative Mammography for Prediction of Future Breast Cancer After DCIS Treatment

Abstract

<b>Background:</b> Mammographic artificial intelligence (AI) systems have been explored for future breast cancer risk prediction. <b>Objective:</b> To investigate associations of scores from a commercial AI system for mammographic breast cancer detection and diagnosis with development of second breast cancers after DCIS treatment and to compare AI predictive performance with existing clinical risk models. <b>Methods:</b> This retrospective five-center study included 1740 patients (median age, 55.0 years) who underwent surgery for DCIS between January 2012 and December 2017 and had ≥1 year of postoperative follow-up. Medical records were reviewed to identify second breast cancers (ipsilateral recurrences after breast-conserving surgery [BCS] or mastectomy or contralateral breast cancers). A commercial AI system for breast cancer detection and diagnosis processed preoperative mammograms. AI scores were dichotomized using the Youden index for second breast cancer prediction. Univariable and multivariable cause-specific hazards models with competing-risk analysis assessed associations with second breast cancers. Cumulative incidence rates (CIRs) were compared between dichotomized AI scores using log-rank tests. Time-dependent AUCs were compared between AI scores and clinical risk models incorporating pathologic information (Van Nuys prognostic index [VNPI]; MSKCC nomograms) using bootstrapping. <b>Results:</b> Twenty-eight patients developed post-BCS ipsilateral recurrence; seven developed post-mastectomy ipsilateral recurrence; 25 developed contralateral breast cancer. AI scores were dichotomized at a threshold of ≥73.5%. Post-BCS ipsilateral recurrence showed a significant independent association with AI score ≥73.5% (HR=2.88). CIR for post-BCS ipsilateral recurrence was higher for AI score ≥73.5% than for AI score <73.5% at 5 years (4.13% vs 0.86%, p<.001) and 10 years (7.26% vs 3.72%, p<.001). AUC for predicting post-BCS ipsilateral recurrence was not significantly different between AI scores and VNPI or MSKCC nomogram at 5 years (0.70 vs 0.73 [p>.99] and 0.63 [p=.82], respectively) and 10 years (0.66 vs 0.75 [p=.66] and 0.68 [p>.99], respectively). AI scores were not associated with other second breast cancer events in hazard models and CIR analyses (p>.05). <b>Conclusion:</b> AI scores showed independent associations with ipsilateral recurrence after BCS for DCIS and had predictive performance not significantly different from existing clinical models. <b>Clinical Impact:</b> AI scores, readily obtained noninvasively on preoperative mammography, may help inform DCIS treatment and surveillance strategies.

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