European urology open science · 2026
Deep Learning Artificial Intelligence and Restriction Spectrum Imaging for Patient-level Detection of Clinically Significant Prostate Cancer on Biparametric Magnetic Resonance Imaging.
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Song Yuze, Domingo Mariluz Rojo, Conlin Christopher C, Do Deondre D, Baxter Madison T, Dornisch Anna, Xu George, Bagrodia Aditya, Barrett Tristan, Harisinghani Mukesh, Hollenberg Gary, Kamran Sophia, Kane Christopher J, Kessler Dimitri A, Kuperman Joshua, Lee Kanglung, Liss Michael A, Margolis Daniel J A, Murphy Paul M, Nakrour Nabih, Ngyuen Truong, Osinski Thomas L, Rakow-Penner Rebecca, Roychowdhury Shoumik, Shabik Ahmed S, Trecarten Shaun, Wehrli Natasha, Weinberg Eric P, Woolen Sean A, Dale Anders M, Seibert Tyler M
Аннотация
BACKGROUND AND OBJECTIVE: Our aim was to evaluate whether combining the maximum restriction score derived from restriction spectrum imaging (RSIrsmax) with deep learning (DL) models can enhance patient-level detection of clinically significant prostate cancer (csPCa) in comparison to Prostate Imaging-Reporting and Data System (PI-RADS) or RSIrsmax alone.
METHODS: A total of 1892 patients from seven institutions who underwent imaging between January 2016 and March 2024 were included on the basis of magnetic resonance imaging (MRI) findings and biopsy-confirmed prostate cancer diagnosis. Two DL architectures, 3D-DenseNet and 3D-DenseNet+RSI (incorporating RSIrsmax), were developed and trained using biparametric MRI and RSI data using a leave-one-center-out validation approach. RSI is a rapid sequence that requires only 2-3 min to acquire. Model performance was evaluated in a biopsy-confirmed subset of 876 patients, with subgroup analyses stratified by site and scanner vendor. Receiver operating characteristic (ROC) and precision recall curves and forest plots (I2 for heterogeneity) were generated, and the area under the ROC curve (AUC) and sensitivity, were compared, as well as specificity at fixed sensitivity of 0.90. Calibration, decision-curve, and reclassification analyses (net reclassification improvement and integrated discrimination improvement) were performed. Codes used in developing the DL model are available on GitHub (https://github.com/ESONG1999/Deep-learning-AI-and-RSI-for-patient-level-detection-of-csPCa-on-MRI).
KEY FINDINGS AND LIMITATIONS: Neither RSIrsmax nor the best DL model combined with RSIrsmax significantly outperformed PI-RADS interpretation by expert radiologists. However, when combined with PI-RADS, both approaches significantly improved patient-level csPCa detection, with AUCs of 0.78 (95% confidence interval [CI] 0.75-0.81; p < 0.001) for RSIrsmax + PI-RADS and 0.80 (95% CI 0.77-0.82; p < 0.001) for the best DL model + PI-RADS, versus 0.75 (95% CI 0.71-0.78) for PI-RADS alone. The absolute gain in specificity at fixed sensitivity of 0.90 was 0.04 (95% CI 0.04-0.04) for RSIrsmax + PI-RADS, and 0.03 (95% CI 0.03-0.04) for DL + PI-RADS.
CONCLUSIONS AND CLINICAL IMPLICATIONS: Both RSIrsmax and the best DL model demonstrated comparable performance to PI-RADS alone. Addition of either model to PI-RADS significantly enhanced patient-level detection of csPCa in comparison to PI-RADS alone. Limitations include biopsy as an imperfect reference, the exclusion of hip implant cases, lack of external calibration, limited RSI availability, and missing case-level information for individual radiologists and their expertise.
PATIENT SUMMARY: We looked at whether adding advanced scan data (ASD) and artificial intelligence (AI) models to radiologist assessments of MRI (magnetic resonance imaging) scans was better in detecting aggressive prostate cancer (PCa). We found that adding AI models or ASD to standard scan scores improved cancer detection in comparison to standard scores alone. The results suggest that combining radiologist expertise with AI and ASD may help in earlier identification of more patients with csPCa.
METHODS: A total of 1892 patients from seven institutions who underwent imaging between January 2016 and March 2024 were included on the basis of magnetic resonance imaging (MRI) findings and biopsy-confirmed prostate cancer diagnosis. Two DL architectures, 3D-DenseNet and 3D-DenseNet+RSI (incorporating RSIrsmax), were developed and trained using biparametric MRI and RSI data using a leave-one-center-out validation approach. RSI is a rapid sequence that requires only 2-3 min to acquire. Model performance was evaluated in a biopsy-confirmed subset of 876 patients, with subgroup analyses stratified by site and scanner vendor. Receiver operating characteristic (ROC) and precision recall curves and forest plots (I2 for heterogeneity) were generated, and the area under the ROC curve (AUC) and sensitivity, were compared, as well as specificity at fixed sensitivity of 0.90. Calibration, decision-curve, and reclassification analyses (net reclassification improvement and integrated discrimination improvement) were performed. Codes used in developing the DL model are available on GitHub (https://github.com/ESONG1999/Deep-learning-AI-and-RSI-for-patient-level-detection-of-csPCa-on-MRI).
KEY FINDINGS AND LIMITATIONS: Neither RSIrsmax nor the best DL model combined with RSIrsmax significantly outperformed PI-RADS interpretation by expert radiologists. However, when combined with PI-RADS, both approaches significantly improved patient-level csPCa detection, with AUCs of 0.78 (95% confidence interval [CI] 0.75-0.81; p < 0.001) for RSIrsmax + PI-RADS and 0.80 (95% CI 0.77-0.82; p < 0.001) for the best DL model + PI-RADS, versus 0.75 (95% CI 0.71-0.78) for PI-RADS alone. The absolute gain in specificity at fixed sensitivity of 0.90 was 0.04 (95% CI 0.04-0.04) for RSIrsmax + PI-RADS, and 0.03 (95% CI 0.03-0.04) for DL + PI-RADS.
CONCLUSIONS AND CLINICAL IMPLICATIONS: Both RSIrsmax and the best DL model demonstrated comparable performance to PI-RADS alone. Addition of either model to PI-RADS significantly enhanced patient-level detection of csPCa in comparison to PI-RADS alone. Limitations include biopsy as an imperfect reference, the exclusion of hip implant cases, lack of external calibration, limited RSI availability, and missing case-level information for individual radiologists and their expertise.
PATIENT SUMMARY: We looked at whether adding advanced scan data (ASD) and artificial intelligence (AI) models to radiologist assessments of MRI (magnetic resonance imaging) scans was better in detecting aggressive prostate cancer (PCa). We found that adding AI models or ASD to standard scan scores improved cancer detection in comparison to standard scores alone. The results suggest that combining radiologist expertise with AI and ASD may help in earlier identification of more patients with csPCa.