Amongero Martina, Mastrantonio Gianluca, De Luca Stefano, Gasparini Mauro
Department of Economics, Social Studies, Applied Mathematics, and Statistics, Universitá di Torino, Torino, Italy.
Department of Mathematical Sciences, Politecnico di Torino, Torino, Italy.
Biom J. 2025 Jun;67(3):e70058. doi: 10.1002/bimj.70058.
Prostatectomized patients are at risk of resurgence, and for this reason, during a follow-up period, they are monitored for prostate-specific antigen (PSA) growth, an indicator of tumor progression. The presence of tumors can be evaluated with an expensive exam, called positron emission tomography with prostate-specific membrane antigen (PET-PSMA). To justify the high cost of the PET-PSMA and, at the same time, to contain the risk for the patient, this exam should be recommended only when the evidence of tumor progression is strong. With the aim of estimating the optimal time to recommend the exam based on the patient's history and collected data, we build a hierarchical Bayesian model that describes, jointly, the PSA growth curve and the probability of a positive PET-PSMA. With our proposal, we process all past and present information about the patients PSA measurement and PET-PSMA results, in order to give an informed estimate of the optimal time, improving current practice.
前列腺切除术后的患者有复发风险,因此,在随访期间,会对他们进行前列腺特异性抗原(PSA)增长监测,这是肿瘤进展的一个指标。肿瘤的存在可以通过一种昂贵的检查来评估,即前列腺特异性膜抗原正电子发射断层扫描(PET-PSMA)。为了证明PET-PSMA检查成本高昂的合理性,同时控制患者风险,只有在肿瘤进展证据确凿时才应推荐此项检查。为了基于患者病史和收集的数据估计推荐该检查的最佳时间,我们构建了一个分层贝叶斯模型,该模型联合描述了PSA增长曲线和PET-PSMA呈阳性的概率。通过我们的提议,我们处理有关患者PSA测量和PET-PSMA结果的所有过去和当前信息,以便对最佳时间做出明智的估计,改进当前的做法。