Wang Zhigang, Sun Yi, Ren Wei, Guan Zhenfeng, Cheng Ji, Pei Xinqi, Dong Qingchuan
Urology Surgery, Shaanxi Provincial People's Hospital Xi'an 710068, Shaanxi, China.
Am J Transl Res. 2023 Feb 15;15(2):1502-1509. eCollection 2023.
This study aims to establish and validate a predictive model for bone metastasis in prostate cancer patients based on multiple immune inflammatory parameters.
In this retrospective study, 162 prostate cancer patients who met the inclusion criteria were selected by Urology Surgery, Shaanxi Provincial People's Hospital. Based on the medical record number of patients and the random number table method, 40 patients were randomly included in a validation group, and the rest were in a modeling group. The patients in the modeling group were divided into a metastatic group (n=67) and a non-metastatic group (n=55) according to the whole-body bone imaging results.
The predictive model was established based on the results of Logistics regression analysis: Logit (P) = -5.341 + 0.930total Gleason score + 1.426total prostate specific antigen + 0.836neutrophil-lymphocyte ratio + 0.896platelet lymphocyte ratio + 0.641lymphocyte/monocyte ratio + 0.750albumin/globulin ratio. ROC analysis showed that the areas under the curve of the predictive model for bone metastasis in the modeling and validation groups were 0.896 and 0.870, respectively. Hosmer-Lemeshow test showed that P=0.253, indicating a high degree of the fitting. External verification results showed that the C-index for predicting prostate cancer bone metastasis in the predictive model established in this study was 0.760 (95% CI: 0.670-0.851).
The bone metastasis predictive model based on the multiple immune inflammatory parameters (neutrophil-lymphocyte ratio, platelet lymphocyte ratio, lymphocyte/monocyte ratio and albumin/globulin ratio) in prostate cancer patients can reasonably predict the occurrence of bone metastasis and is well worth clinical application.
本研究旨在基于多种免疫炎症参数建立并验证前列腺癌患者骨转移的预测模型。
在这项回顾性研究中，陕西省人民医院泌尿外科挑选出162例符合纳入标准的前列腺癌患者。根据患者病历号及随机数字表法，将40例患者随机纳入验证组，其余患者纳入建模组。建模组患者根据全身骨显像结果分为转移组（n = 67）和非转移组（n = 55）。
基于Logistic回归分析结果建立预测模型：Logit（P）= -5.341 + 0.930×总 Gleason评分 + 1.426×总前列腺特异性抗原 + 0.836×中性粒细胞与淋巴细胞比值 + 0.896×血小板与淋巴细胞比值 + 0.641×淋巴细胞与单核细胞比值 + 0.750×白蛋白与球蛋白比值。ROC分析显示，建模组和验证组骨转移预测模型的曲线下面积分别为0.896和0.870。Hosmer-Lemeshow检验显示P = 0.253，表明拟合度较高。外部验证结果显示，本研究建立的预测模型预测前列腺癌骨转移的C指数为0.760（95%CI：0.670 - 0.851）。
基于前列腺癌患者多种免疫炎症参数（中性粒细胞与淋巴细胞比值、血小板与淋巴细胞比值、淋巴细胞与单核细胞比值及白蛋白与球蛋白比值）的骨转移预测模型能够合理预测骨转移的发生，具有良好的临床应用价值。
