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Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study.

Twilt, Jasper J
; 
Saha, Anindo
; 
Bosma, Joeran S
; 
Giannarini, Gianluca
; 
Padhani, Anwar R
; 
Yakar, Derya
; 
Elschot, Mattijs
; 
Veltman, Jeroen
; 
Fütterer, Jurgen
; 
Huisman, Henkjan
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Author
Twilt, Jasper J
Saha, Anindo
Bosma, Joeran S
Giannarini, Gianluca
Padhani, Anwar R
Yakar, Derya
Elschot, Mattijs
Veltman, Jeroen
Fütterer, Jurgen
Huisman, Henkjan
de Rooij, Maarten
Saha, Anindo
Bosma, Joeran S
Twilt, Jasper J
van Ginneken, Bram
Noordman, Constant R
Slootweg, Ivan
Roest, Christian
Fransen, Stefan J
Sunoqrot, Mohammed RS
Bathen, Tone F
Rouw, Dennis
Immerzeel, Jos
Geerdink, Jeroen
van Run, Chris
Groeneveld, Miriam
Meakin, James
Yakar, Derya
Elschot, Mattijs
Veltman, Jeroen
Fütterer, Jurgen J
de Rooij, Maarten
Huisman, Henkjan
Bjartell, Anders
Padhani, Anwar R
Bonekamp, David
Villeirs, Geert
Salomon, Georg
Giannarini, Gianluca
Huisman, Henkjan
Kalpathy-Cramer, Jayashree
Barentsz, Jelle
Maier-Hein, Klaus H
Elschot, Mattijs
Rusu, Mirabela
Obuchowski, Nancy A
Rouviere, Olivier
van den Bergh, Roderick
Panebianco, Valeria
Kasivisvanathan, Veeru
Karagöz, Ahmet
Bône, Alexandre
Routier, Alexandre
Marcoux, Arnaud
Abi-Nader, Clément
Li, Cynthia Xinran
Feng, Dagan
Alis, Deniz
Karaarslan, Ercan
Ahn, Euijoon
Nicolas, François
Sonn, Geoffrey A
Bhattacharya, Indrani
Kim, Jinman
Shi, Jun
Jahanandish, Hassan
An, Hong
Kan, Hongyu
Oksuz, Ilkay
Qiao, Liang
Rohé, Marc-Michel
Yergin, Mert
Rusu, Mirabela
Khadra, Mohamed
Şeker, Mustafa Ege
Kartal, Mustafa Said
Debs, Noëlie
Fan, Richard E
Saunders, Sara
Soerensen, Simon John Christoph
Moroianu, Stefania
Vesal, Sulaiman
Yuan, Yuan
Malakoti-Fard, Afsoun
Mačiūnien, Agnė
Kawashima, Akira
Machadov, Ana MM de MG de Sousa
Moreira, Ana Sofia L
Ponsiglione, Andrea
Rappaport, Annelies
Stanzione, Arnaldo
Ciuvasovas, Arturas
Turkbey, Baris
De Keyzer, Bart
Pedersen, Bodil G
Eijlers, Bram
Chen, Christine
Riccardo, Ciabattoni
Alis, Deniz
Courrech Staal, Ewout FW
Jäderling, Fredrik
Langkilde, Fredrik
Aringhieri, Giacomo
Brembilla, Giorgio
Son, Hannah
Vanderlelij, Hans
Raat, Henricus PJ Frank
Pikūnienė, Ingrida
Macova, Iva
Schoots, Ivo
Caglic, Iztok
Zawaideh, Jeries P
Wallström, Jonas
Bittencourt, Leonardo K
Khurram, Misbah
Choi, Moon Hyung
Takahashi, Naoki
Tan, Nelly
Rouvière, Olivier
Franco, Paolo NiccolÒ
Gutierrez, Patricia A
Thimansson, Per Erik
Hanus, Petr
Puech, Philippe
Rau, Philipp R
De Visschere, Pieter
Guillaume, Ramette
Cuocolo, Renato
Falcão, Ricardo O
van Stiphout, Rogier SA
Girometti, Rossano
Briediene, Ruta
Grigienė, Rūta
Gitau, Samuel
Withey, Samuel
Ghai, Sangeet
Penzkofer, Tobias
Barrett, Tristan
Panebianco, Valeria
Tammisetti, Varaha Sai
Løgager, Vibeke B
Černý, Vladimír
Venderink, Wulphert
Law, Yan Mee
Lee, Young Joon
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Computer Vision
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Journal article
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English
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Abstract
Purpose To simulate an artificial intelligence (AI)-driven triaging workflow in which an AI system, using high-confidence thresholds, assesses a subset of prostate MRI examinations for clinically significant prostate cancer (csPCa), compare the assessment with stand-alone radiologists, and evaluate the number of examinations triaged by the AI to estimate potential workload reduction. Materials and Methods Data from an international AI confirmatory study (February 2022-November 2023) were used in this retrospective study. MRI examinations of 500 men with suspected csPCa from four European centers were included. Exclusion criteria were prior prostate treatment, prior csPCa, or considerable imaging artifacts. AI-triaging thresholds were calibrated on 100 examinations. The AI system assessed examinations exceeding high-specificity or high-sensitivity thresholds, with the remaining examinations deferred to radiologists. The workflow was simulated on 400 examinations, including examinations from an external site, incorporating assessments from 62 radiologists. Reference standards were histopathology and/or 3 or more years of follow-up. Sensitivity and specificity of the triaging workflow were compared with the conventional workflow using multireader, multicase analysis of variance. Results Among the 400 patients (median age, 66 years; IQR, 60-69 years) included for testing, radiologists achieved a sensitivity of 89.4% (95% CI: 85.8, 93.1) and specificity of 57.7% (95% CI: 52.3, 63.1). The AI-driven pathway maintained comparable sensitivity (89.0%; 95% CI: 85.0, 93.0; <i>P</i> = .36) but improved specificity by 11.5%, reaching 69.2% (95% CI: 64.4, 74.0; <i>P</i> &lt; .001). The AI system triaged and diagnosed 195 of 400 (49%; 95% CI: 173, 216) examinations with sensitivity of 94.7% (95% CI: 89.5, 99.9) and specificity of 94.7% (95% CI: 90.5, 98.9). Conclusion Triaging by this AI system improved simulated diagnostic workflow efficiency without compromising diagnostic accuracy for csPCa. <b>Keywords:</b> Prostate, MRI, Localization, Oncology, Comparative Studies, Diagnosis <i>Supplemental material is available for this article.</i> ClinicalTrials.gov registration no. NCT05489341 © RSNA, 2026.
Citation
J.J. Twilt, A. Saha, J.S. Bosma, G. Giannarini, A.R. Padhani, D. Yakar , et al., "Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study.," Radiology Imaging Cancer, vol. 8, no. 3, pp. e250461-e250461, 2026, https://doi.org/10.1148/rycan.250461.
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Radiology Imaging Cancer
Conference
Keywords
32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, 3211 Oncology and Carcinogenesis, Aged, Artificial Intelligence, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Prostatic Neoplasms, Retrospective Studies, Sensitivity and Specificity, Triage, Workflow
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Radiological Society of North America (RSNA)
DOI
10.1148/rycan.250461
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