Department of Radiation Oncology, Clinic for Radiation Oncology and Diagnostics, Institute for Oncology and Radiology of Serbia, Belgrade, Serbia.
Department of Nuclear Medicine, Institute for Oncology and Radiology of Serbia, Belgrade, Serbia.
Biomed Res Int. 2022 Feb 7;2022:7943609. doi: 10.1155/2022/7943609. eCollection 2022.
After primary treatment of localized prostate carcinoma (PC), up to a third of patients have disease recurrence. Different predictive models have already been used either for initial stratification of PC patients or to predict disease recurrence. Recently, artificial intelligence has been introduced in the diagnosis and management of PC with a potential to revolutionize this field. The aim of this study was to analyze machine learning (ML) classifiers in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during follow-up. The study cohort consisted of 109 PC patients treated with external beam radiotherapy alone or in combination with androgen deprivation therapy. We developed and evaluated the performance of two ML algorithms based on artificial neural networks (ANN) and naïve Bayes (NB). Of all patients, 72.5% was randomly selected for a training set while the remaining patients were used for testing of the models. The presence/absence of disease progression was defined as the output variable. The input variables for models were conducted from the univariate analysis preformed among two groups of patients in the training set. They included two pretreatment variables (UICC stage and Gleason's score risk group) and five posttreatment variables (nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, and PSA in the moment of disease reevaluation). The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy was calculated to test the models' performance. The results showed that specificity was similar for both models, while NB achieved better sensitivity then ANN (100.0% versus 94.4%). The ANN showed an accuracy of 93.3%, and the matching for NB model was 96.7%. In this study, ML classifiers have shown potential for application in routine clinical practice during follow-up when disease progression was suspected.
根治性治疗局限性前列腺癌（PC）后，多达三分之一的患者会出现疾病复发。目前已经有多种预测模型用于对 PC 患者进行初始分层或预测疾病复发。最近，人工智能已被引入 PC 的诊断和管理中，具有彻底改变这一领域的潜力。本研究旨在分析机器学习（ML）分类器，以预测在随访期间 PSA 升高时疾病进展的情况。该研究队列包括 109 例接受单纯外照射放疗或联合雄激素剥夺治疗的 PC 患者。我们开发并评估了两种基于人工神经网络（ANN）和朴素贝叶斯（NB）的 ML 算法的性能。所有患者中有 72.5%被随机选择用于训练集，其余患者用于模型测试。疾病进展的存在/不存在被定义为输出变量。模型的输入变量来自训练集中两组患者的单变量分析。它们包括两个预处理变量（UICC 分期和 Gleason 评分风险组）和五个治疗后变量（最低 PSA 值、达到最低 PSA 值的时间、PSA 倍增时间、PSA 速度和疾病再评估时的 PSA 值）。计算曲线下面积、敏感性、特异性、阳性预测值、阴性预测值和预测准确性来测试模型的性能。结果表明，两种模型的特异性相似，而 NB 的敏感性优于 ANN（100.0%对 94.4%）。ANN 的准确率为 93.3%，NB 模型的准确率为 96.7%。在这项研究中，ML 分类器在怀疑疾病进展时的随访中具有应用于常规临床实践的潜力。
