A novel model incorporating quantitative contrast-enhanced ultrasound into PI-RADSv2-based nomogram detecting clinically significant prostate cancer

TL;DR

This study improves the detection of significant prostate cancer by combining ultrasound data with existing imaging scores and clinical factors.

Contribution

A new predictive model integrating quantitative contrast-enhanced ultrasound with PI-RADSv2 and clinical parameters for better prostate cancer detection.

Findings

The new model achieved higher AUCs (0.910 and 0.879) compared to the PI-RADSv2-based model (0.865 and 0.821).

Quantitative ultrasound parameters like rush time and wash-out AUC were independent predictors of clinically significant prostate cancer.

The model improved classification accuracy and clinical utility as shown by Net Reclassification Index and Decision Curve Analysis.

Abstract

The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

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