SyMRI histogram analysis for diagnosing clinically significant prostate cancer.

Abstract

BackgroundQuantitative parameters derived from synthetic magnetic resonance imaging (SyMRI) have shown potential in diagnosing clinically significant prostate cancer (csPCa). Histogram analysis enhances diagnostic accuracy by evaluating spatial heterogeneity.PurposeTo assess the performance of histogram analysis models utilizing relaxation maps from SyMRI in diagnosing csPCa.Material and MethodsA total of 124 men with a clinical suspicion of csPCa were enrolled prospectively between April 2018 and December 2019. From 124 patients, 224 ROIs were analyzed, including 97 csPCa lesions, 11 insignificant PCa, 59 non-cancerous peripheral zone (PZ) lesions, and 57 benign prostatic hyperplasia. The lesions were randomly divided into a training group and a validation group, in a ratio of 7:3. Histogram analysis models were constructed using SyMRI relaxation maps, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and their combination. We compared these with mean-value-based models using the same modalities. The diagnostic accuracy of these models in distinguishing csPCa from clinically insignificant disease (CIS) was evaluated.ResultsHistogram analysis models outperformed mean-value-based models in both training and validation groups. SyMRI-based histogram analysis models demonstrated diagnostic effectiveness comparable to DWI and ADC models. The combined model achieved the highest area under the curve values in the PZ (0.898; 95% confidence interval [CI]=0.763-0.999) and transition zone (TZ) (0.944; 95% CI=0.874-0.999). In the TZ, the combined model significantly outperformed the Prostate Imaging Reporting and Data System (P = 0.019).ConclusionHistogram analysis of SyMRI relaxation maps is a valuable tool for differentiating csPCa from CIS. Combining SyMRI with DWI and ADC further improved diagnostic accuracy.