Improved prostate cancer diagnosis: upgraded prostate imaging reporting and data system (PI-RADS) scores by zoomed diffusion-weighted imaging enhance deep-learning-based computer-aided diagnosis accuracy.

Abstract

Background: A new diffusion-weighted imaging (DWI) technique, known as zoomed-field-of-view echo-planar DWI (z-DWI), has been developed to reduce geometric distortions and susceptibility artifacts and to achieve higher spatial resolution. However, it remains unclear whether z-DWI, compared with the traditional DWI technique, can enhance the diagnostic performance of deep-learning-based computer-aided diagnosis (DL-CAD) and radiologists using DL-CAD in detecting prostate cancer (PCa). This study aims to evaluate and compare the diagnostic performance and PI-RADS scores of DL-CAD in detecting PCa using conventional full-field-of-view single-shot echo-planar DWI (f-DWI) and advanced z-DWI and to extend this comparison to clinical practice, in which radiologists use DL-CAD.

Methods: This study retrospectively included magnetic resonance imaging from 359 patients for suspected PCa. There were 496 prostate lesions included, with 253 (51%) being malignant. Using a DL-CAD system, images of f-DWI and z-DWI were uploaded separately to obtain the localizations and the prostate imaging reporting and data system (PI-RADS) scores of suspected malignant lesions. The results were compared to histopathologic results. The diagnostic performance of f-DWI and z-DWI were evaluated using the free-response receiver operating characteristics and the alternative free-response receiver operating characteristics curves. Discrepancies in PI-RADS scores were analyzed. Additionally, two radiologists participated in consensus reading images by using DL-CAD with different DWI techniques, and their performance and PI-RADS scores were compared. Lastly, the relationship between PI-RADS discrepancies and clinically significant prostate cancer (csPCa) risk was analyzed.

Results: z-DWI enabled DL-CAD to exhibit better diagnostic performance [area under the curve (AUC), 0.857 vs. 0.841; P=0.02], with a higher mean PI-RADS score for PCa lesions (4.26 vs. 3.92; P<0.001), and improved scores for 66 PCa lesions compared to f-DWI. When radiologists used DL-CAD, z-DWI also enabled radiologists to exhibit a higher mean PI-RADS score for PCa lesions (4.31 vs. 4.02; P<0.001) and improved scores for 56 PCa lesions compared to f-DWI, however, no statistically significant difference was found in diagnostic performance (AUC, 0.887 vs. 0.881; P=0.16). In multivariable logistic regression analyses, upgraded PI-RADS scores by z-DWI were significantly associated with csPCa risk.

Conclusions: z-DWI, in comparison to f-DWI, enhances the diagnostic performance of DL-CAD for PCa, assigning higher PI-RADS scores to malignant lesions. Despite offering limited improvement for radiologists using DL-CAD, z-DWI shows promise in enhancing the detection of csPCa.