Deep learning-accelerated T2WI of the prostate for transition zone lesion evaluation and extraprostatic extension assessment

Abstract

This bicenter retrospective analysis included 162 patients who had undergone prostate biopsy following prebiopsy MRI, excluding those with PCa identified only in the peripheral zone (PZ). DLR T2WI achieved a 69% reduction in scan time relative to TSE T2WI. The intermethod agreement between the two T2WI sets in terms of the Prostate Imaging Reporting and Data System (PI-RADS) classification and extraprostatic extension (EPE) grade was measured using the intraclass correlation coefficient (ICC) and diagnostic performance was assessed with the area under the receiver operating characteristic curve (AUC). Clinically significant PCa (csPCa) was found in 74 (45.7%) patients. Both T2WI methods showed high intermethod agreement for the overall PI-RADS classification (ICC: 0.907-0.949), EPE assessment (ICC: 0.925-0.957) and lesion size measurement (ICC: 0.980-0.996). DLR T2WI and TSE T2WI showed similar AUCs (0.666-0.814 versus 0.684-0.832) for predicting EPE. The AUCs for detecting csPCa with DLR T2WI (0.834-0.935) and TSE T2WI (0.891-0.935) were comparable in 139 patients with TZ lesions with no significant differences (P > 0.05). The findings suggest that DLR T2WI is an efficient alternative for TZ lesion assessment, offering reduced scan times without compromising diagnostic accuracy.

Keywords: Acceleration; Deep learning-based reconstruction; Magnetic resonance imaging; Prostate cancer; Staging; T2-weighted imaging.