An evaluation of race-based representation among men participating in clinical trials for prostate cancer and erectile dysfunction

MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information.

METHODS

This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL2) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI-ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets.

RESULTS

Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78).

CONCLUSION

Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics' (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.

Exploring representation of underrepresented minority men in a restorative therapy clinical trial for erectile dysfunction: addressing barriers and promoting inclusion

This retrospective descriptive analysis explores underrepresented minority men in our clinical trial for restorative therapy for erectile dysfunction and to identify strategies to promote diversity and inclusion in the study population. Demographic data were collected from all participants and the prevalence in our population was compared to the source population. The proportion of individuals taking part in our clinical trial was compared to the overall disease population using the participation to prevalence ratio. Among the 61 participants enrolled in the Platelet-Rich Plasma for Erectile Dysfunction trial, 72.1% were Hispanic compared to 39.9% in the national source population. There were 27.9% non-Hispanic participants, and 41.2% non-Hispanic men in the local South Florida population. The racial composition of our study shows 80.3% of PRP participants identify as White, 16.4% as Black, and 3.4% as Asian. In the national source population, 61.8% of patients were White, 27.5% are black, and 1.5% are Asian. Through the implementation of strategies such as having Hispanic team members on the clinical trial staff and providing education and outreach materials both in Spanish and English, we were able to overcome barriers to participation in Hispanic men and potentially improve health outcomes for underrepresented minority men with erectile dysfunction.