Deep Machine Learning of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data for identifying Prostate Cancer and its Clinical Risk in Asymptomatic Men

The peripheral blood of 130 asymptomatic men having elevated Prostate-Specific Antigen (PSA) levels was immune profiled using multiparametric whole blood flow cytometry. Of these men, 42 were subsequently diagnosed as having benign prostate disease and 88 as having PCa on biopsy-based evidence. We built a bidirectional Long Short-Term Memory Deep Neural Network (biLSTM) model for detecting the presence of PCa in men which combined the previously-identified phenotypic features (CD8+CD45RA-CD27-CD28- (CD8+ Effector Memory cells), CD4+CD45RA-CD27-CD28- (CD4+ Effector Memory cells), CD4+CD45RA+CD27-CD28- (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA), CD3-CD19+ (B cells), CD3+CD56+CD8+CD4+ (NKT cells) with Age. The performance of the PCa presence ‘detection’ model was: Acc: 86.79 (±0.10), Sensitivity: 82.78% (± 0.15); Specificity: 95.83% (± 0.11) on the test set (test set that was not used during training and validation); AUC: 89.31% (± 0.07), ORP-FPR: 7.50% (± 0.20), ORP-TPR: 84.44% (± 0.14). A second biLSTM ‘risk’ model combined the immunophenotypic features with PSA to predict whether a patient with PCa has high-risk disease (defined by the D'Amico Risk Classification) achieved the following: Acc: 94.90% (± 6.29), Sensitivity: 92% (± 21.39); Specificity: 96.11 (± 0.00); AUC: 94.06% (± 10.69), ORP-FPR: 3.89% (± 0.00), ORP-TPR: 92% (± 21.39). The ORP-FPR for predicting the presence of PCa when combining FC+PSA was lower than that of PSA alone. This study demonstrates that AI approaches based on peripheral blood phenotyping profiles can distinguish between benign prostate disease and PCa and predict clinical risk in asymptomatic men having elevated PSA levels.本研究纳入130例前列腺特异性抗原（Prostate-Specific Antigen, PSA）水平升高的无症状男性，采用多参数全血流式细胞术对其外周血开展免疫表型分析。其中42例受试者后续经活检证实罹患良性前列腺疾病，88例被确诊为前列腺癌（PCa）。 本研究构建了一款双向长短期记忆深度神经网络（bidirectional Long Short-Term Memory Deep Neural Network, biLSTM）模型，用于检测男性前列腺癌（PCa）的发生风险。该模型整合了此前已确认的免疫表型特征：CD8+CD45RA-CD27-CD28-（CD8+效应记忆T细胞）、CD4+CD45RA-CD27-CD28-（CD4+效应记忆T细胞）、CD4+CD45RA+CD27-CD28-（重新表达CD45RA的CD4+终末分化效应记忆T细胞）、CD3-CD19+（B细胞）、CD3+CD56+CD8+CD4+（自然杀伤T细胞，NKT细胞），并结合年龄因素。该前列腺癌检测模型在训练与验证阶段未使用的独立测试集上的性能指标如下：准确率（Accuracy, Acc）为86.79%（±0.10）、灵敏度为82.78%（±0.15）、特异度为95.83%（±0.11）；受试者工作特征曲线下面积（Area Under Curve, AUC）为89.31%（±0.07），最优工作点假阳性率（ORP-FPR）为7.50%（±0.20），最优工作点真阳性率（ORP-TPR）为84.44%（±0.14）。 第二款biLSTM风险预测模型整合免疫表型特征与PSA水平，用于预测前列腺癌患者是否存在高危疾病（基于达米科风险分层标准D'Amico Risk Classification定义），其性能表现为：准确率为94.90%（±6.29）、灵敏度为92%（±21.39）、特异度为96.11%（±0.00）；受试者工作特征曲线下面积为94.06%（±10.69），最优工作点假阳性率为3.89%（±0.00），最优工作点真阳性率为92%（±21.39）。相较于单独使用PSA检测，联合流式细胞术（FC）与PSA检测前列腺癌的最优工作点假阳性率更低。 本研究证实，基于外周血免疫表型谱的人工智能方法，可有效区分良性前列腺疾病与前列腺癌，并能预测PSA水平升高的无症状男性的临床风险分层。