Villalba, Michael

Objectives: Women with a history of breast cancer are at increased risk of developing subsequent breast cancer, including ipsilateral recurrence and contralateral new primary breast cancer. This study evaluated the discriminatory performance of a mammogram-based artificial intelligence (AI) risk model for predicting subsequent breast cancer within one year after a negative screening mammogram. Methods: This enriched retrospective case–control study included women with a prior history of breast cancer who underwent screening digital breast tomosynthesis between January 2018 and December 2023 at three affiliated academic breast imaging centers. Digital breast tomosynthesis examinations classified as BI-RADS 1 or 2 were retrospectively analyzed using the ProFound AI® Risk model version 1.0 to estimate 1-year breast cancer risk. Patients were classified according to whether they developed subsequent breast cancer within one year of the index screening examination. Model discrimination was evaluated using receiver operating characteristic analysis. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated at an exploratory cutoff selected by maximizing the Youden index. Results: The study included 96 women (mean age, 65.3 ± 8.7 years), of whom 32 developed subsequent breast cancer within one year, and 64 did not. The mean AI risk score was significantly higher in the subsequent breast cancer group than in the control group (1.18 ± 0.59 vs. 0.49 ± 0.41; p < 0.001). The AI model demonstrated an AUC of 0.824 (95% CI: 0.728–0.921). At an exploratory cutoff of 0.39, sensitivity was 81.3%, specificity was 76.6%, PPV was 63.4%, and NPV was 89.1%. In separate exploratory analyses, the AUC was 0.790 (95% CI: 0.641–0.939) for ipsilateral recurrence and 0.860 (95% CI: 0.752–0.974) for contralateral new primary breast cancer. AI risk scores were not significantly correlated with tumor size or age at subsequent breast cancer diagnosis. Conclusions: In this enriched retrospective case–control study, higher mammogram-based AI risk scores were associated with subsequent breast cancer within one year after a negative screening examination. The model demonstrated discriminatory performance for both ipsilateral recurrence and contralateral new primary breast cancer; however, these analyses were exploratory. Because the cohort was enriched for subsequent breast cancer events, the reported predictive values are specific to the study sample and should not be extrapolated to routine surveillance populations. Larger prospective cohorts are needed to validate discrimination, calibration, and clinical utility.

e12615 Background: Breast MRI after completion of neoadjuvant chemotherapy (NAC) has demonstrated high sensitivity and positive predictive value in evaluating response to NAC. It is widely recognized as the most accurate imaging technique for assessing tumor response, outperforming mammography and ultrasound. However, there remains a knowledge gap in its potential as an early predictor of pathological response, when performed at the midpoint of NAC. Here, we investigated the correlation between MRI findings at the midpoint of NAC and subsequent pathological response at definitive surgery. Methods: A retrospective study was performed of patients with breast cancer who received NAC at Mayo Clinic, Florida and had at least 2 breast MRIs, one at midpoint and one either pre- or post-treatment. Patients with prior ipsilateral breast cancer were excluded. Participants were stratified into three molecular subtypes: hormone receptor-positive (HR+), HER2-positive (HER2+), and triple-negative breast cancer (TNBC). All MRIs were re-reviewed by dedicated breast radiologists, with tumor response classified as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD) based on RECIST criteria. Pathological outcomes, including pathologic complete response (pCR) and residual cancer burden (RCB), were extracted from pathology reports and confirmed by a single breast pathologist, as indicated. pCR was defined as the absence of residual invasive tumor in both the breast and axillary lymph nodes. Results: A total of 154 patients met inclusion criteria from 2012 to 2022. Similar to previous studies, the positive predictive value for the post-treatment MRI was high at 82.4%, with 14 of 17 patients with imaging complete responses (iCR) achieving pCR. For the midpoint MRI, the positive predictive value for patients with iCR was 100%, with 8 out of 8 patients achieving pCR. Patients with certain subtypes of breast cancer with at least PR in both breast and axillary lymph nodes at midpoint MRI had numerically higher pCR rate, specifically HER2+ (22/37, 59.5%) and TNBC (8/13, 61.5%) but not HR+ tumors (12/45, 26.7%). In contrast, patients with less than PR in either breast or axillary lymph nodes showed numerically lower rate pCR in TNBC (7/18, 38.9%) and HR+ tumors (3/18, 16.7%). However, 15/23 (65.2%) patients with HER2+ tumors achieved pCR despite having less than PR at midpoint MRI. Conclusions: Midpoint breast MRI may serve as an early predictor of pathological response, particularly in patients with iCR. Patients with less than PR, especially those with HR+ and TNBC, have lower chances of achieving pCR. These findings highlight the importance of considering additional therapies for these subgroups of patients to improve the treatment outcomes, as they are less likely to achieve pCR with the current standard neoadjuvant chemotherapy regimen.