https://www.selleckchem.com/products/sr10221.html Background Higher categories of background parenchymal enhancement (BPE) increase breast cancer risk. However, current clinical BPE categorization is subjective. Objective Using a semi-automated segmentation algorithm, we calculated quantitative BPE measures and investigated the utility of individual features and feature pairs to significantly predict subsequent breast cancer risk, compared to radiologist-assigned BPE category. Methods In this retrospective case control study, we identified 95 high risk women without a personal history of breast cancer who underwent breast MRI. Nineteen subsequently developed breast cancer. Each case was age-matched to four controls (76 controls total). Sociodemographic characteristics were compared for the matched cases and controls using the Mann-Whitney U test. From each dynamic contrast-enhanced MRI, quantitative fibroglandular tissue (FGT) and BPE measures were computed by averaging enhancing voxels above enhancement ratio thresholds (0-100%), totaling the enhancing volu Compared to BPE category, the first post-gadolinium (phase 1) BPE% at the 30% and 40% enhancement ratio thresholds (Ph1BPE%30% and Ph1BPE%40%) yielded significantly higher AUC values of 0.85 (p=0.0007) and 0.84 (p=0.0004), respectively. Feature combinations demonstrated similar AUC with improved sensitivity. Conclusion Preliminary data indicate that quantitative BPE measures may outperform radiologist-assigned category in breast cancer risk prediction. Clinical impact Future risk prediction models that incorporate quantitative measures warrant additional investigation.BACKGROUND Based on expert consensus, PI-RADS version 2.1 introduced the transition zone (TZ) 'atypical' BPH nodule, defined as a TZ lesion with incomplete or absent capsule (T2 score 2), as well as a revised scoring pathway whereby such nodules, if showing marked restricted diffusion (DWI score 4-5) are upgraded from overall PI-RADS category 2 to