Does deep learning software improve the consistency and performance of radiologists with various levels of experience in assessing bi-parametric prostate MRI?

Abstract

Objective To investigate whether commercially available deep learning (DL) software improves the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency on bi-parametric MRI among radiologists with various levels of experience; to assess whether the DL software improves the performance of the radiologists in identifying clinically significant prostate cancer (csPCa). Methods We retrospectively enrolled consecutive men who underwent bi-parametric prostate MRI at a 3 T scanner due to suspicion of PCa. Four radiologists with 2, 3, 5, and > 20 years of experience evaluated the bi-parametric prostate MRI scans with and without the DL software. Whole-mount pathology or MRI/ultrasound fusion-guided biopsy was the reference. The area under the receiver operating curve (AUROC) was calculated for each radiologist with and without the DL software and compared using De Long’s test. In addition, the inter-rater agreement was investigated using kappa statistics. Results In all, 153 men with a mean age of 63.59 ± 7.56 years (range 53–80) were enrolled in the study. In the study sample, 45 men (29.80%) had clinically significant PCa. During the reading with the DL software, the radiologists changed their initial scores in 1/153 (0.65%), 2/153 (1.3%), 0/153 (0%), and 3/153 (1.9%) of the patients, yielding no significant increase in the AUROC (p > 0.05). Fleiss’ kappa scores among the radiologists were 0.39 and 0.40 with and without the DL software (p = 0.56). Conclusions The commercially available DL software does not increase the consistency of the bi-parametric PI-RADS scoring or csPCa detection performance of radiologists with varying levels of experience.

Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol

“…However, in a study by Liu et al [57], which conducted an evaluation method based on textural classification, a sensitivity of 0.85 and a specificity of 0.73 were found in demanding PIRADS = 3. On the other hand, Arslan et al [58] reported no clear benefit from the use of deep learning software in studies carried out at different levels of experienced radiologists. Compared to similar works undertaken by Gianni [59,60], obtaining a correct classification F1-score greater than 76% is a good result for texture features attributed to different lesions assigned to PIRADS.…”