Castaldo Rossana, Brancato Valentina, Cavaliere Carlo, Pecchia Leandro, Illiano Ester, Costantini Elisabetta, Ragozzino Alfonso, Salvatore Marco, Nicolai Emanuele, Franzese Monica
Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy.
School of Engineering, University of Warwick, Coventry, United Kingdom.
Front Oncol. 2024 May 15;14:1323247. doi: 10.3389/fonc.2024.1323247. eCollection 2024.
Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment.
Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age.
The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2.
This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.
前列腺癌（PCa）是男性中常见的癌症形式之一。目前，多参数磁共振成像（MRI）是用于肿瘤定位和癌症分期的成像方法。影像组学在前列腺癌检测中发挥着关键作用，并具有潜在价值，可减少不必要的活检需求，对肿瘤侵袭性进行特征描述，并监测前列腺癌治疗后的复发情况。
此外，将影像组学数据与临床和组织病理学数据相结合，可以进一步加深对前列腺癌的理解和管理，并减少因昂贵且侵入性的活检而向专科护理的不必要转诊。因此，本研究的目的是通过整合非侵入性诊断参数（影像组学和前列腺特异性抗原水平）以及患者年龄，开发一种风险模型评分，以自动检测前列腺癌患者。
使用来自两个中心的189例接受双参数MRI检查的前列腺癌患者数据集对所提出的方法进行了评估。弹性网络正则化广义线性模型在自动检测前列腺癌患者方面达到了91%的曲线下面积（AUC）。该模型风险评分还用于评估活检时前列腺癌的可疑病例，然后与双参数前列腺影像报告和数据系统（PI-RADS）v2进行比较。
本研究通过结合影像组学、前列腺特异性抗原水平和年龄，探索了一种完善的风险模型在前列腺癌客观准确风险分层中的相对效用，并为随访期间临床决策过程提供支持。
