Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer

Abstract

Background: Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa.

Purpose: To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting.

Material and methods: Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves.

Results: In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171).

Conclusion: PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.

Keywords: PI-RADS; magnetic resonance imaging; multisite-multivendor; prostate cancer; radiomics.

Fig. 1.

Example of regions of interest for extraction of histogram and texture parameters.

Fig. 2.

Model development workflow. The baseline model (left) comprising clinical parameters, including PSA and PI-RADS; the extended model (right) comprising clinical parameters, including PSA, PI-RADS, and ROI-based parameters. PSA, prostate-specific antigen; ROI, region of interest.

Fig. 3.

Flow chart showing numbers and exclusions. *Images were reread in order to comply with PI-RADS v2.1 (released 2019) which implied that some lesions were downgraded to PI-RADS 1-2. csPCa, clinically significant prostate cancer (Gleason score ≥7); ncsPCa, non-clinically significant prostate cancer (Gleason score = 6); PZ, peripheral zone; TZ, transition zone.

Fig. 4.

ROC curves for predictive models for (a) the PZ and (b) the TZ, based on clinical parameters and PI-RADS (baseline), and clinical parameters, PI-RADS, and histogram/texture parameters (extended). PZ, peripheral zone; ROC, receiver operating characteristic; TZ, transition zone.