Dimitriadis Avtantil, Kalliatakis Grigorios, Osuala Richard, Kessler Dimitri, Mazzetti Simone, Regge Daniele, Diaz Oliver, Lekadir Karim, Fotiadis Dimitrios, Tsiknakis Manolis, Papanikolaou Nikolaos, Marias Kostas
Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece.
Department of Mathematics and Computer Science, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, L'Eixample, 08007 Barcelona, Spain.
J Imaging. 2025 Mar 26;11(4):98. doi: 10.3390/jimaging11040098.
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.
前列腺癌（PCa）是目前男性中第二常见的癌症。准确诊断PCa可为患者提供有效治疗并降低死亡率。以往的研究仅专注于从磁共振成像（MRI）中对PCa进行病变检测或病变分类。在这项工作中，我们关注PCa临床工作流程中一个关键但尚未充分探索的任务：区分有癌症存在的病例（经病理证实的PCa患者）和无可疑PCa发现的情况（无癌症存在）。为此，我们首次通过采用和处理多中心前列腺网络成像存档进行了大规模实验，该存档包含来自11000多例PCa病例的600多万张PCa图像表示，是最大的PCa MR图像集合。4504名患者的双参数MR（bpMRI）图像及其临床变量用于训练，而在两个包含975例回顾性患者和435例前瞻性患者的验证测试集上对架构进行评估。我们提出的多编码器交叉注意力融合架构在接收器操作特征曲线（AUC）下达到了0.91的良好面积。这证明了我们的方法融合复杂双参数成像模态和增强模型鲁棒性的能力，为深度学习模型在临床中准确确定不同患者群体中PCa的存在铺平了道路。
