Department of Urology, First Affiliated Hospital of Nanchang University, Nanchang, 330000, China.
BMC Urol. 2023 Jan 31;23(1):12. doi: 10.1186/s12894-023-01177-x.
Numerous studies have shown that local therapy can improve long-term survival in patients with metastatic prostate cancer. However, it is unclear which patients are the potential beneficiaries.
We obtained information on prostate cancer patients from the Surveillance, Epidemiology, and End Results database and divided eligible patients into the local treatment group and non-local treatment group. Propensity score matching (PSM) was used to reduce the influence of confounding factors. In the matched local treatment (LT) group, if the median overall survival time (OS) was longer than the Nonlocal treatment (NLT) group, it was defined as a benefit group, otherwise, it was a non-benefit group. Then, univariate and multivariate logistic regression were used to screen out predictors associated with benefits, and a nomogram model was constructed based on these factors. The accuracy and clinical value of the models were assessed through calibration plots and decision curve analysis.
The study enrolled 7255 eligible patients, and after PSM, each component included 1923 patients. After matching, the median OS was still higher in the LT group than in the NLT group [42 (95% confidence interval: 39-45) months vs 40 (95% confidence interval: 38-42) months, p = 0.03]. The independent predictors associated with benefit were age, PSA, Gleason score, T stage, N stage, and M stage. The nomogram model has high accuracy and clinical application value in both the training set (C-index = 0.725) and the validation set (C-index = 0.664).
The nomogram model we constructed can help clinicians identify patients with potential benefits from LT and formulate a reasonable treatment plan.
大量研究表明，局部治疗可改善转移性前列腺癌患者的长期生存。然而，目前尚不清楚哪些患者是潜在的获益者。
我们从监测、流行病学和最终结果数据库中获取前列腺癌患者信息，并将符合条件的患者分为局部治疗组和非局部治疗组。采用倾向评分匹配（PSM）来减少混杂因素的影响。在匹配的局部治疗（LT）组中，如果中位总生存时间（OS）长于非局部治疗（NLT）组，则定义为获益组，否则定义为非获益组。然后，采用单因素和多因素逻辑回归筛选与获益相关的预测因素，并基于这些因素构建列线图模型。通过校准图和决策曲线分析评估模型的准确性和临床价值。
本研究纳入了 7255 名符合条件的患者，PSM 后，每个组各包含 1923 名患者。匹配后，LT 组的中位 OS 仍高于 NLT 组[42（95%置信区间：39-45）个月比 40（95%置信区间：38-42）个月，p=0.03]。与获益相关的独立预测因素包括年龄、PSA、Gleason 评分、T 分期、N 分期和 M 分期。列线图模型在训练集（C 指数=0.725）和验证集（C 指数=0.664）中均具有较高的准确性和临床应用价值。
我们构建的列线图模型可以帮助临床医生识别可能从 LT 中获益的患者，并制定合理的治疗计划。
