A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study

Abstract

Background

Traditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylation of the PRKY promoter were found to be associated with prostate cancer.

Methods

Fifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023 were selected for this study, and their clinical data, blood samples and MRI images were obtained before the operation. Methylation level of two PRKY promoter sites, cg05618150 and cg05163709, were tested through bisulfite sequencing PCR (BSP). The PI-RADS score of each patient was estimated and the region of interest (ROI) was delineated by 2 experienced radiologists. After being extracted by a plug-in of 3D-slicer, radiomic features were selected through LASSCO regression and t-test. Selected radiomic features, methylation levels and clinical data were used for model construction through the random forest (RF) algorithm, and the predictive efficiency was analyzed by the area under the receiver operation characteristic (ROC) curve (AUC).

Results

Methylation level of the site, cg05618150, was observed to be associated with prostate cancer, for which the AUC was 0.74. The AUC of T2WI in csPCA prediction was 0.84, which was higher than that of the apparent diffusion coefficient ADC (AUC = 0.81). The model combined with T2WI and clinical data reached an AUC of 0.94. The AUC of the T2WI-clinic-methylation-combined model was 0.97, which was greater than that of the model combined with the PI-RADS score, clinical data and PRKY promoter methylation levels (AUC = 0.86).

Conclusions

The model combining with radiomic features, clinical data and PRKY promoter methylation levels based on machine learning had high predictive efficiency in csPCA diagnosis.