Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks

Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks

Prostate cancer (PCa) is currently the second most prevalent cancer among men. Accurate diagnosis of PCa can provide effective treatment for patients and reduce mortality. Previous works have merely focused on either lesion detection or lesion classification of PCa from magnetic resonance imaging (M...

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Main Authors: Avtantil Dimitriadis, Grigorios Kalliatakis, Richard Osuala, Dimitri Kessler, Simone Mazzetti, Daniele Regge, Oliver Diaz, Karim Lekadir, Dimitrios Fotiadis, Manolis Tsiknakis, Nikolaos Papanikolaou, ProCAncer-I Consortium, Kostas Marias
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Journal of Imaging
Subjects:
prostate cancer
cross-attention
deep learning
Online Access:https://www.mdpi.com/2313-433X/11/4/98
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Summary:Prostate cancer (PCa) is currently the second most prevalent cancer among men. Accurate diagnosis of PCa can provide effective treatment for patients and reduce mortality. Previous works have merely focused on either lesion detection or lesion classification of PCa from magnetic resonance imaging (MRI). In this work we focus on a critical, yet underexplored task of the PCa clinical workflow: distinguishing cases with cancer presence (pathologically confirmed PCa patients) from conditions with no suspicious PCa findings (no cancer presence). To this end, we conduct large-scale experiments for this task for the first time by adopting and processing the multi-centric ProstateNET Imaging Archive which contains more than 6 million image representations of PCa from more than 11,000 PCa cases, representing the largest collection of PCa MR images. Bi-parametric MR (bpMRI) images of 4504 patients alongside their clinical variables are used for training, while the architectures are evaluated on two hold-out test sets of 975 retrospective and 435 prospective patients. Our proposed multi-encoder-cross-attention-fusion architecture achieved a promising area under the receiver operating characteristic curve (AUC) of 0.91. This demonstrates our method’s capability of fusing complex bi-parametric imaging modalities and enhancing model robustness, paving the way towards the clinical adoption of deep learning models for accurately determining the presence of PCa across patient populations.
ISSN:2313-433X

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