Artificial Intelligence-Assisted Biparametric MRI for Detecting Prostate Cancer—A Comparative Multireader Multicase Accuracy Study

• Published in: Journal of clinical medicine Vol. 14; no. 17; p. 6111
• Main Authors: Nißler, Daniel, Reimers-Kipping, Sabrina, Ingwersen, Maja, Berger, Frank, Niekrenz, Felix, Theis, Bernhard, Hielscher, Fabian, Franken, Philipp, Gaßler, Nikolaus, Grimm, Marc-Oliver, Teichgräber, Ulf, Franiel, Tobias
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.08.2025
• Subjects: Access to information; Accuracy; Algorithms; Artificial intelligence; Biopsy; Clinical significance; Diagnosis; Evaluation; Health aspects; Magnetic resonance imaging; Medical research; Methods; Prostate cancer; Technology application; Germany; deep learning; computer-assisted diagnosis; biparametric MRI; prostate cancer; multiparametric magnetic resonance imaging; artificial intelligence
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm14176111

Objectives: To evaluate the diagnostic accuracy of AI-assisted biparametric MRI (AI-bpMRI) in detecting prostate cancer (PCa) as a possible replacement for multiparametric MRI (mpMRI) depending on readers’ experience. Methods: This fully crossed, multireader multicase, single-centre, consecutive study retrospectively included men with suspected PCa. Three radiologists with different levels of experience independently scored each participant’s biparametric (bp) MRI, mpMRI, and AI-bpMRI according to the PI-RADS V2.1 classification. The AI-assisted image processing was based on a sequential deep learning network. Histopathological findings were used as a reference. The study evaluated the mean areas under the receiver operating characteristic curves (AUCs) using the jackknife method for covariance. AUCs were tested for non-inferiority of AI-bpMRI to mpMRI (non-inferiority margin: −0.05). Results: A total of 105 men (mean age 66 ± 7 years) were evaluated. AI-bpMRI was non-inferior to mpMRI in detecting both Gleason score (GS) ≥ 3 + 4 PCa (AUC difference: 0.03 [95% CI: −0.03, 0.08], p = 0.37) and GS ≥ 3 + 3 PCa (AUC difference: 0.04 [95% CI: −0.01, 0.09], p = 0.14) and was superior to bpMRI in detecting GS ≥ 3 + 3 PCa (AUC difference: 0.07 [95% CI: 0.02, 0.12], p = 0.004). The benefit of AI-bpMRI was greatest for the readers with low or medium experience (AUC difference in detecting GS ≥ 3 + 4 compared to mpMRI: 0.06 [95% CI: −0.03, 0.14], p = 0.19 and 0.06 [95% CI: −0.03, 0.14], p = 0.19, respectively). Conclusions: This study indicates that AI-bpMRI detects PCa with a diagnostic accuracy comparable to that of mpMRI.