Saitta Cesare, Buffi NicolòMaria, Avolio Pierpaolo, Beatrici Edoardo, Paciotti Marco, Lazzeri Massimo, Fasulo Vittorio, Cella Ludovica, Garofano Giuseppe, Piccolini Andrea, Contieri Roberto, Nazzani Sebastiano, Silvani Carlo, Catanzaro Mario, Nicolai Nicola, Hurle Rodolfo, Casale Paolo, Saita Alberto, Lughezzani Giovanni
Department of Biomedical Sciences, Humanitas University, 20090 Pieve, Emanuele, Italy.
Department of Urology, IRCCS Humanitas Clinical and Research Hospital, Rozzano, Italy.
World J Urol. 2025 Aug 18;43(1):502. doi: 10.1007/s00345-025-05797-w.
INTRODUCTION & OBJECTIVES: Detecting clinically significant prostate cancer (csPCa) remains a top priority in delivering high-quality care, yet consensus on an optimal diagnostic pathway is constantly evolving. In this study, we present an innovative diagnostic approach, leveraging a machine learning model tailored to the emerging role of prostate micro-ultrasound (micro-US) in the setting of csPCa diagnosis.
MATERIALS & METHODS: We queried our prospective database for patients who underwent Micro-US for a clinical suspicious of prostate cancer. CsPCa was defined as any Gleason group grade > 1. Primary outcome was the development of a diagnostic pathway which implements clinical and radiological findings using machine learning algorithm. The dataset was divided into training (70%) and testing subsets. Boruta algorithms was used for variable selection, then based on the importance coefficients multivariable logistic regression model (MLR) was fitted to predict csPCA. Classification and Regression Tree (CART) model was fitted to create the decision tree. Accuracy of the model was tested using receiver characteristic curve (ROC) analysis using estimated area under the curve (AUC).
Overall, 1422 patients were analysed. Multivariable LR revealed PRI-MUS score ≥ 3 (OR 4.37, p < 0.001), PI-RADS score ≥ 3 (OR 2.01, p < 0.001), PSA density ≥ 0.15 (OR 2.44, p < 0.001), DRE (OR 1.93, p < 0.001), anterior lesions (OR 1.49, p = 0.004), prostate cancer familiarity (OR 1.54, p = 0.005) and increasing age (OR 1.031, p < 0.001) as the best predictors for csPCa, demonstrating an AUC in the validation cohort of 83%, 78% sensitivity, 72.1% specificity and 81% negative predictive value. CART analysis revealed elevated PRIMUS score as the main node to stratify our cohort.
By integrating clinical features, serum biomarkers, and imaging findings, we have developed a point of care model that accurately predicts the presence of csPCa. Our findings support a paradigm shift towards adopting MicroUS as a first level diagnostic tool for csPCa detection, potentially optimizing clinical decision making. This approach could improve the identification of patients at higher risk for csPca and guide the selection of the most appropriate diagnostic exams. External validation is essential to confirm these results.
在提供高质量医疗服务方面，检测具有临床意义的前列腺癌（csPCa）仍然是首要任务，然而对于最佳诊断途径的共识仍在不断演变。在本研究中，我们提出了一种创新的诊断方法，利用机器学习模型来适应前列腺微超声（micro-US）在csPCa诊断中的新作用。
我们在我们的前瞻性数据库中查询了因临床怀疑前列腺癌而接受微超声检查的患者。csPCa被定义为任何Gleason组分级>1。主要结果是开发一种诊断途径，该途径使用机器学习算法来整合临床和放射学检查结果。数据集被分为训练集（70%）和测试子集。使用Boruta算法进行变量选择，然后基于重要性系数拟合多变量逻辑回归模型（MLR）来预测csPCA。拟合分类与回归树（CART）模型以创建决策树。使用估计的曲线下面积（AUC）通过受试者特征曲线（ROC）分析来测试模型的准确性。
总体而言，分析了1422例患者。多变量LR显示PRI-MUS评分≥3（OR 4.37，p<0.001）、PI-RADS评分≥3（OR 2.01，p<0.001）、PSA密度≥0.15（OR 2.44，p<0.001）、直肠指检（OR 1.93，p<0.001）、前部病变（OR 1.49，p = 0.004）、前列腺癌家族史（OR 1.54，p = 0.005）和年龄增长（OR 1.031，p<0.001）是csPCa的最佳预测因素，在验证队列中的AUC为83%，敏感性为78%，特异性为72.1%，阴性预测值为81%。CART分析显示PRIMUS评分升高是对我们的队列进行分层的主要节点。
通过整合临床特征、血清生物标志物和影像学检查结果，我们开发了一种即时护理模型，可准确预测csPCa的存在。我们的研究结果支持将微超声作为csPCa检测的一级诊断工具的范式转变，这可能会优化临床决策。这种方法可以改善对csPca高危患者的识别，并指导选择最合适的诊断检查。外部验证对于确认这些结果至关重要。
