Automated MRI system for clinically significant prostate cancer detection development validation and real-world implementation

Prostate MRI enables detection of clinically significant prostate cancer (csPCa), yet variability in PI-RADS scoring limits reproducibility and throughput. Here, we report the development and validation of an automated MRI-based decision aid (ProAI) that estimates patient-level risk of csPCa from biparametric MRI and supports routine reporting. Training, internal validation, and external testing spanned 7849 examinations across six centres and two public datasets. On pooled external tests, the system achieved a patient-level AUC of 0.93 (95% CI, 0.91-0.95), comparable to PI-RADS while improving inter-case consistency. In a multi-reader, multi-case study involving nine clinicians, assistance increased accuracy from 0.80 to 0.86 and reduced reading time. Prospective implementation in 1978 consecutive examinations-maintained performance (AUC 0.92) and was associated with a 32% reduction in radiology workload. Performance generalised to the TCIA cohort (AUC 0.83). These findings indicate that an automated MRI-based decision aid can standardise reporting and enhance efficiency across prostate cancer care pathways. This study was registered at ClinicalTrials. Trial number: ChiCTR2400092863.

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Publication Details

Journal
Nature Communications
Published
2025-11-23
DOI
https://doi.org/10.1038/s41467-025-66593-z
Primary Topic
Prostate Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Automated MRI system for clinically significant prostate cancer detection development validation and real-world implementation

Fang Lei, Yan Wang, Chengwei Shao, Yun Bian et al.
Nature Communications
Prostate Cancer Diagnosis and Treatment
article

Automated MRI system for clinically significant prostate cancer detection development validation and real-world implementation

Fang Lei, Yan Wang, Chengwei Shao, Yun Bian, Jianping Lu, Xuedong Yang, Haihu Chen, Qingsong Yang, Hui Jiang, Jing Li, Jian Wang, Gang Li, Xu Fang, Jing Yang, Peng Xia, Xu Gao, Hanchang Wu, Fang Liu, Xiaoguang Yang, Jianying Liu, Yong Yi, Luo Yin-deng, Xiangfei Chai, Chengwei Chen
article en

Abstract

Prostate MRI enables detection of clinically significant prostate cancer (csPCa), yet variability in PI-RADS scoring limits reproducibility and throughput. Here, we report the development and validation of an automated MRI-based decision aid (ProAI) that estimates patient-level risk of csPCa from biparametric MRI and supports routine reporting. Training, internal validation, and external testing spanned 7849 examinations across six centres and two public datasets. On pooled external tests, the system achieved a patient-level AUC of 0.93 (95% CI, 0.91-0.95), comparable to PI-RADS while improving inter-case consistency. In a multi-reader, multi-case study involving nine clinicians, assistance increased accuracy from 0.80 to 0.86 and reduced reading time. Prospective implementation in 1978 consecutive examinations-maintained performance (AUC 0.92) and was associated with a 32% reduction in radiology workload. Performance generalised to the TCIA cohort (AUC 0.83). These findings indicate that an automated MRI-based decision aid can standardise reporting and enhance efficiency across prostate cancer care pathways. This study was registered at ClinicalTrials. Trial number: ChiCTR2400092863.

Nature CommunicationsVol. 16(1)
Second Military Medical University (CN), Dalian Medical University (CN), Shanghai Stomatological Hospital (CN), Second Affiliated Hospital of Chongqing Medical University (CN), Changhai Hospital (CN), Pudong Medical Center (CN), Shanghai Traditional Chinese Medicine Hospital (CN), Hohhot First Hospital (CN), Shandong Provincial QianFoShan Hospital (CN), Wuxi Taihu Hospital (CN), Chongqing Medical University (CN)
National Science Foundation, Shanghai Municipal Health Commission
Openalex Percentile: Top 49%
Prostate Cancer Diagnosis and Treatment
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