Bekou Eleni, Seimenis Ioannis, Tsochatzis Athanasios, Tziagkana Karafyllia, Kelekis Nikolaos, Deftereos Savas, Courcoutsakis Nikolaos, Koukourakis Michael I, Karavasilis Efstratios
Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
Medical Physics Laboratory, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece.
J Imaging. 2025 Jul 23;11(8):250. doi: 10.3390/jimaging11080250.
Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis. This study investigated the efficiency of seven ML models to diagnose the different PCa grades, changing the input variables. Our studied sample comprised 214 men who underwent bpMRI in different imaging centers. Seven ML algorithms were compared using radiomic features extracted from T2-weighted (T2W) and diffusion-weighted (DWI) MRI, with and without the inclusion of Prostate-Specific Antigen (PSA) values. The performance of the models was evaluated using the receiver operating characteristic curve analysis. The models' performance was strongly dependent on the input parameters. Radiomic features derived from T2WI and DWI, whether used independently or in combination, demonstrated limited clinical utility, with AUC values ranging from 0.703 to 0.807. However, incorporating the PSA index significantly improved the models' efficiency, regardless of lesion location or degree of malignancy, resulting in AUC values ranging from 0.784 to 1.00. There is evidence that ML methods, in combination with radiomic analysis, can contribute to solving differential diagnostic problems of prostate cancers. Also, optimization of the analysis method is critical, according to the results of our study.
前列腺癌（PCa）是男性中最常见的恶性肿瘤。精确分级对于PCa的有效治疗方法至关重要。应用于双参数磁共振成像（bpMRI）影像组学的机器学习（ML）有望改善PCa的诊断和预后。本研究调查了七种ML模型在改变输入变量的情况下诊断不同PCa分级的效率。我们的研究样本包括214名在不同影像中心接受bpMRI检查的男性。使用从T2加权（T2W）和扩散加权（DWI）MRI提取的影像组学特征，比较了七种ML算法，包括是否纳入前列腺特异性抗原（PSA）值。使用受试者工作特征曲线分析评估模型的性能。模型的性能强烈依赖于输入参数。从T2WI和DWI衍生的影像组学特征，无论单独使用还是联合使用，临床效用都有限，AUC值范围为0.703至0.807。然而，纳入PSA指数显著提高了模型的效率，无论病变位置或恶性程度如何，AUC值范围为0.784至1.00。有证据表明，ML方法与影像组学分析相结合有助于解决前列腺癌的鉴别诊断问题。此外，根据我们的研究结果，优化分析方法至关重要。
