Peer-Reviewed Publication
Cancers (Basel)2023;15(22)November 17, 2023Journal Article
The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer.
Ingeborg van den Berg1,2,3, Timo F W Soeterik1,2, Erik J R J van der Hoeven4, Bart Claassen5, Wyger M Brink3, Diederik J H Baas6, J P Michiel Sedelaar7, Lizette Heine8, Jim Tol8, Jochem R N van der Voort van Zyp2, Cornelis A T van den Berg2, Roderick C N van den Bergh1, Jean-Paul A van Basten6,7, Harm H E van Melick1
1Department of Urology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands.
2Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
3Magnetic Detection and Imaging Group, Technical Medical Centre, University of Twente, 7522 NH Enschede, The Netherlands.
4Department of Radiology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands.
5Department of Radiology, Canisius Wilhelmina Hospital, 7522 NH Nijmegen, The Netherlands.
6Department of Urology, Canisius Wilhelmina Hospital, 7522 NH Nijmegen, The Netherlands.
7Department of Urology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands.
8Quantib B.V., RadNet's AI Division, 3012 KM Rotterdam, The Netherlands.
Abstract
Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. W…
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