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TRUVACE RECORD VERSION
record: TRV-2026-0402
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T10:29:50.047063Z
status: published
lens: p_space
sector: health
headline: Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study
dek: BACKGROUND: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine pract…
gain_title: (none)
problem_title: When compared to historical multidisciplinary routine practice readings in 1000 testing cases, the AI system did not demonstrate confirmed non-inferiority and showed slightly lower specificity at matched sensitivity.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: When compared to historical multidisciplinary routine practice readings in 1000 testing cases, the AI system did not demonstrate confirmed non-inferiority and showed slightly lower specificity at matched sensitivity.
problem_evidence: non-inferiority was not confirmed
quick_read: Researchers trained an AI system on 9207 prostate MRI examinations from the Netherlands and tested it on 1000 examinations from the Netherlands and Norway, with a 400-case subset read by 62 radiologists from 20 countries. By June 2024 publication, the AI achieved AUROC 0.91 versus 0.86 for radiologists using PI-RADS 2.1, and at matched operating points detected 6.8% more clinically significant cancers at same specificity or 50.4% fewer false positives at same sensitivity.

The result matters because prostate MRI workload is rising and depends on experienced readers; a supportive AI that reduces overdiagnosis of grade group 1 cancers while maintaining sensitivity could aid primary diagnostic settings. Uncertainty remains because the comparison to multidisciplinary routine practice showed slightly lower specificity and the authors state prospective validation is needed to test clinical applicability.
limitation: Retrospective design and need for prospective testing; authors note clinical applicability not yet established.
tag: Evidence-backed problem
key_points: International paired non-inferiority study trained on 9207 MRI examinations from 11 sites in the Netherlands and tested on 1000 examinations from 12 sites in Netherlands and Norway. | Multireader study included 62 radiologists from 45 centres in 20 countries with median 7 years prostate MRI experience reading 400 paired examinations. | Reference standard used histopathology and at least 3 years median 5 years follow-up; 2440 of 10,207 examinations had Gleason grade group 2 or greater cancer. | Against multidisciplinary routine practice in 1000 testing cases, AI showed 68.9% specificity versus 69.0% at 96.1% sensitivity.
rundown: The study used 10,207 MRI examinations from 9129 patients collected from Jan 1, 2012 through Dec 31, 2021, with 9207 cases for training and tuning and 1000 cases for testing, plus a 400-case multireader subset evaluated by 62 radiologists from 45 centres in 20 countries.

Primary endpoints were sensitivity, specificity and AUROC compared to PI-RADS 2.1 readers and to historical radiology readings made during multidisciplinary routine practice with patient history and peer consultation, using histopathology and at least 3 years follow-up as reference.
sources:
- peer_reviewed | The Lancet Oncology | https://doi.org/10.1016/s1470-2045(24)00220-1 | 2024-06-12
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