Evaluating the association between radiomic features on breast MRI and dormant tumor cell presence in the SURMOUNT trial.

Abstract

561 Background: While breast cancer treatment is effective at eradicating primary disease, approximately 20% of patients will ultimately recur at a distant site. Recurrence has been linked to the presence of minimal residual disease (MRD) after treatment, which presents as circulating micrometastases and dormant tumor cells (DTCs) located primarily in the bone marrow. Radiomic features on MRI are imaging biomarkers that have been linked with lesion heterogeneity, which in turn has been associated with disease aggressiveness. Here, we examine the association between radiomic features from the primary lesion on MRI and the presence of post-treatment DTCs. Noninvasively identifying patients at high risk of MRD would facilitate optimized treatment strategies to reduce recurrence risk. Methods: Dynamic contrast-enhanced MRI data from breast cancer survivors enrolled in the SURMOUNT trial (N=104) were retrospectively analyzed. Participants with MRIs collected within 90 days of diagnosis who consented to provide a bone marrow aspirate (BMA) sample were eligible. Working with a board-certified radiologist, we successfully identified and segmented the primary lesion for N=70 patients with BMA outcomes. Using the publicly available CaPTk software, we computed 33 radiomic features for each segmented lesion based on pixel intensity, morphology, and grey level textures. To reduce the dimensionality of the feature space, we used these feature values to group the participants into clusters—termed radiomic phenotypes — via agglomerative hierarchical clustering with significance testing. Fisher’s exact test was applied to test the statistical significance of the association between radiomic phenotype and BMA outcome (± DTC). For all statistical tests, p<0.05 was considered significant. Results: Of the N=70 participants analyzed, 58 were negative and 12 were positive for DTCs present in the bone marrow. Two statistically significant radiomic phenotypes ( p<0.05) were identified that were strongly associated with BMA outcome ( p=0.0251). 11 out of 12 positive outcomes were grouped into the second radiomic phenotype (Table). Conclusions: We found a statistically significant association between DTC presence and radiomic phenotypes derived from MRI data. Given the large percentage of positive outcomes assigned to the second radiomic phenotype, the radiomic phenotypes may indicate low and high risk of MRD, respectively. Importantly, this analysis shows promise for the ability to predict MRD from noninvasive imaging features. These results motivate continued data collection to increase sample size and identify a robust set of radiomic features, which may ultimately be used as predictors in potential models of MRD risk. [Table: see text]