Background/Objectives: This study evaluates whether combining68Ga-PSMA-11-PET/CT derived imaging biomarkers with clinical risk factors improves the prediction of early biochemical recurrence (eBCR) or clinical progress in patients with high-risk prostate cancer (PCa) after primary treatment, using machine learning (ML) models. Methods: We analyzed data from 93 high-risk PCa patients who underwent68Ga-PSMA-11 PET/CT and received primary treatment at a single center. Two predictive models were developed: a logistic regression (LR) model and an ML derived probabilistic graphical model (PGM) based on a naïve Bayes framework. Both models were compared against each other and against the CAPRA risk score. The models’ input variables were selected based on statistical analysis and domain expertise including a literature review and expert input. A decision tree was derived from the PGM to translate its probabilistic reasoning into a transparent classifier. Results: The five key input variables were as follows: binarized CAPRA score, maximal intraprostatic PSMA uptake intensity (SUVmax), presence of bone metastases, nodal involvement at common iliac bifurcation, and seminal vesicle infiltration. The PGM achieved superior predictive performance with a balanced accuracy of 0.73, sensitivity of 0.60, and specificity of 0.86, substantially outperforming both the LR (balanced accuracy: 0.50, sensitivity: 0.00, specificity: 1.00) and CAPRA (balanced accuracy: 0.59, sensitivity: 0.20, specificity: 0.99). The decision tree provided an explainable classifier with CAPRA as a primary branch node, followed by SUVmax and specific PET-detected tumor sites. Conclusions: Integrating68Ga-PSMA-11 imaging biomarkers with clinical parameters, such as CAPRA, significantly improves models to predict progression in patients with high-risk PCa undergoing primary treatment. The PGM offers superior balanced accuracy and enables risk stratification that may guide personalized treatment decisions.
背景/目的：本研究旨在评估通过机器学习模型，将68Ga-PSMA-11 PET/CT衍生的影像学生物标志物与临床风险因素相结合，是否能提高对高危前列腺癌患者在接受初次治疗后早期生化复发或临床进展的预测能力。方法：我们分析了93例高危前列腺癌患者的数据，这些患者均接受了68Ga-PSMA-11 PET/CT检查并在单一中心接受了初次治疗。研究构建了两种预测模型：逻辑回归模型和基于朴素贝叶斯框架的机器学习概率图模型。两种模型相互比较，并与CAPRA风险评分进行比较。模型输入变量的选择基于统计分析、领域专业知识（包括文献综述和专家意见）。从概率图模型中推导出决策树，将其概率推理转化为透明的分类器。结果：五个关键输入变量如下：二分类CAPRA评分、前列腺内最大PSMA摄取强度、骨转移的存在、髂总动脉分叉处淋巴结受累以及精囊浸润。概率图模型取得了更优的预测性能，其平衡准确度为0.73，敏感性为0.60，特异性为0.86，显著优于逻辑回归模型（平衡准确度：0.50，敏感性：0.00，特异性：1.00）和CAPRA评分（平衡准确度：0.59，敏感性：0.20，特异性：0.99）。决策树提供了一个可解释的分类器，以CAPRA作为主要分支节点，其次是SUVmax和PET检测到的特定肿瘤部位。结论：将68Ga-PSMA-11影像学生物标志物与CAPRA等临床参数相结合，显著提高了预测接受初次治疗的高危前列腺癌患者进展的模型性能。概率图模型提供了更优的平衡准确度，并能够实现风险分层，从而可能指导个体化治疗决策。
肿瘤(癌症)患者之家