: Background: Variability in prostate biparametric MRI (bpMRI) interpretation limits diagnostic reliability for prostate cancer (PCa). Artificial intelligence (AI) has potential to reduce this variability and improve diagnostic accuracy. Objective: The objective of this study was to evaluate impact of a deep learning AI model on lesion- and patient-level clinically significant PCa (csPCa) and PCa detection rates and interreader agreement in bpMRI interpretations. Methods: This retrospective, multireader, multicenter study used a balanced incomplete block design for MRI randomization. Six radiologists of varying experience interpreted bpMRI scans with and without AI assistance in alternating sessions. The reference standard for lesion-level detection for cases was whole-mount pathology after radical prostatectomy; for control patients, negative 12-core systematic biopsies. In all, 180 patients (120 in the case group, 60 in the control group) who underwent mpMRI and prostate biopsy or radical prostatectomy between January 2013 and December 2022 were included. Lesion-level sensitivity, PPV, patient-level AUC for csPCa and PCa detection, and interreader agreement in lesion-level PI-RADS scores and size measurements were assessed. Results: AI assistance improved lesion-level PPV (PI-RADS ≥ 3: 77.2% [95% CI, 71.0-83.1%] vs 67.2% [61.1-72.2%] for csPCa; 80.9% [75.2-85.7%] vs 69.4% [63.4-74.1%] for PCa; both p < .001), reduced lesion-level sensitivity (PIRADS ≥ 3: 44.4% [38.6-50.5%] vs 48.0% [42.0-54.2%] for csPCa, p = .01; 41.7% [37.0-47.4%] vs 44.9% [40.5-50.2%] for PCa, p = .01), and no difference in patient-level AUC (0.822 [95% CI, 0.768-0.866] vs 0.832 [0.787-0.868] for csPCa, p = .61; 0.833 [0.782-0.874] vs 0.835 [0.792-0.871] for PCa, p = .91). AI assistance improved interreader agreement for lesion-level PI-RADS scores (κ = 0.748 [95% CI, 0.701-0.796] vs 0.336 [0.288-0.381], p < .001), lesion size measurements (coverage probability of 0.397 [0.376-0.419] vs 0.367 [0.349-0.383], p < .001), and patient-level PI-RADS scores (κ = 0.704 [0.627-0.767] versus 0.507 [0.421-0.584], p < .001). Conclusion: AI improved lesion-level PPV and interreader agreement with slightly lower lesion-level sensitivity. Clinical Impact: AI may enhance consistency and reduce false-positives in bpMRI interpretations. Further optimization is required to improve sensitivity without compromising specificity.
Evaluating Artificial Intelligence–Assisted Prostate Biparametric MRI Interpretation: An International Multireader Study / Gelikman, David G.; Yilmaz, Enis C.; Harmon, Stephanie A.; Huang, Erich P.; An, Julie Y.; Azamat, Sena; Law, Yan Mee; Margolis, Daniel J. A.; Marko, Jamie; Panebianco, Valeria; Esengur, Omer Tarik; Lin, Yue; Belue, Mason J.; Gaur, Sonia; Bicchetti, Marco; Xu, Ziyue; Tetreault, Jesse; Yang, Dong; Xu, Daguang; Lay, Nathan S.; Gurram, Sandeep; Shih, Joanna H.; Merino, Maria J.; Lis, Rosina; Choyke, Peter L.; Wood, Bradford J.; Pinto, Peter A.; Turkbey, Baris. - In: AMERICAN JOURNAL OF ROENTGENOLOGY. - ISSN 0361-803X. - (2025). [10.2214/ajr.24.32399]
Evaluating Artificial Intelligence–Assisted Prostate Biparametric MRI Interpretation: An International Multireader Study
Panebianco, Valeria;Bicchetti, Marco;
2025
Abstract
: Background: Variability in prostate biparametric MRI (bpMRI) interpretation limits diagnostic reliability for prostate cancer (PCa). Artificial intelligence (AI) has potential to reduce this variability and improve diagnostic accuracy. Objective: The objective of this study was to evaluate impact of a deep learning AI model on lesion- and patient-level clinically significant PCa (csPCa) and PCa detection rates and interreader agreement in bpMRI interpretations. Methods: This retrospective, multireader, multicenter study used a balanced incomplete block design for MRI randomization. Six radiologists of varying experience interpreted bpMRI scans with and without AI assistance in alternating sessions. The reference standard for lesion-level detection for cases was whole-mount pathology after radical prostatectomy; for control patients, negative 12-core systematic biopsies. In all, 180 patients (120 in the case group, 60 in the control group) who underwent mpMRI and prostate biopsy or radical prostatectomy between January 2013 and December 2022 were included. Lesion-level sensitivity, PPV, patient-level AUC for csPCa and PCa detection, and interreader agreement in lesion-level PI-RADS scores and size measurements were assessed. Results: AI assistance improved lesion-level PPV (PI-RADS ≥ 3: 77.2% [95% CI, 71.0-83.1%] vs 67.2% [61.1-72.2%] for csPCa; 80.9% [75.2-85.7%] vs 69.4% [63.4-74.1%] for PCa; both p < .001), reduced lesion-level sensitivity (PIRADS ≥ 3: 44.4% [38.6-50.5%] vs 48.0% [42.0-54.2%] for csPCa, p = .01; 41.7% [37.0-47.4%] vs 44.9% [40.5-50.2%] for PCa, p = .01), and no difference in patient-level AUC (0.822 [95% CI, 0.768-0.866] vs 0.832 [0.787-0.868] for csPCa, p = .61; 0.833 [0.782-0.874] vs 0.835 [0.792-0.871] for PCa, p = .91). AI assistance improved interreader agreement for lesion-level PI-RADS scores (κ = 0.748 [95% CI, 0.701-0.796] vs 0.336 [0.288-0.381], p < .001), lesion size measurements (coverage probability of 0.397 [0.376-0.419] vs 0.367 [0.349-0.383], p < .001), and patient-level PI-RADS scores (κ = 0.704 [0.627-0.767] versus 0.507 [0.421-0.584], p < .001). Conclusion: AI improved lesion-level PPV and interreader agreement with slightly lower lesion-level sensitivity. Clinical Impact: AI may enhance consistency and reduce false-positives in bpMRI interpretations. Further optimization is required to improve sensitivity without compromising specificity.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
