Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Ningbo Diagnostic Pathology Center, Ningbo City, Zhejiang Province, China.
Clin Genitourin Cancer. 2024 Jun;22(3):102061. doi: 10.1016/j.clgc.2024.02.012. Epub 2024 Feb 29.
There is an urgent need to identify a robust predictor for BCG response in patients with non-muscle-invasive bladder cancer (NMIBC). We aimed to employ the Lasso regression model for the selection and construction of an index (BCGI) utilizing inflammation and nutrition indicators to predict the response to BCG therapy.
After acquiring the ethics approval, we searched the electric medical records in our institution and performed data screening. Then, we developed the BCGI using a Lasso regression model and subsequently evaluated its performance in both the train and internal test datasets through Kaplan-Meier survival curves and Cox regression analysis. Then, we also evaluated the prognostic value of BCGI alongside the EAU2021 model.
The training dataset and internal test dataset contained 295 and 196 patients, respectively. Referring to the Lasso results, BCGI consisted of hemoglobin, albumin, and platelet count, which could significantly predict the recurrence of NMIBC patients who accepted BCG in train (P = .012) and test (P = .004) datasets. The BCGI also exhibited statistically prognostic value in no smoking history, World Health Organization high grade, and T1 subgroups, both in train and test datasets. In multivariable analysis, BCGI exhibited independent prognostic value in train (P = .012) and test (P = .012) datasets. Finally, we constructed a nomogram that consisted of smoking history, T stage, World Health Organization grade, tumor size, and BCGI. Then, BCGI demonstrated significant independent prognostic value in NMIBC patients treated with BCG, a result not observed with the EAU2021 score or classification.
Based on the results, we reasonably suggest that BCGI may be a useful predictor for NMIBC patients who accepted BCG. Furthermore, we have demonstrated the efficacy of constructing a prognostic index using clinical factors and a Lasso regression model, a versatile approach applicable to various medical conditions.
迫切需要确定一种能够准确预测非肌层浸润性膀胱癌（NMIBC）患者卡介苗（BCG）反应的可靠指标。本研究旨在采用 Lasso 回归模型，选择和构建一个利用炎症和营养指标的指数（BCGI），以预测 BCG 治疗反应。
在获得伦理批准后，我们检索了我院的电子病历并进行了数据筛选。然后，我们利用 Lasso 回归模型构建了 BCGI，并通过 Kaplan-Meier 生存曲线和 Cox 回归分析在训练和内部测试数据集评估其性能。此外，我们还评估了 BCGI 与 EAU2021 模型的预后价值。
训练数据集和内部测试数据集分别包含 295 例和 196 例患者。根据 Lasso 结果，BCGI 由血红蛋白、白蛋白和血小板计数组成，可显著预测接受 BCG 治疗的 NMIBC 患者的复发（训练数据集 P =.012，测试数据集 P =.004）。BCGI 在训练和测试数据集中均具有统计学预后价值，且与无吸烟史、世界卫生组织高分级和 T1 亚组相关。多变量分析显示，BCGI 在训练数据集（P =.012）和测试数据集（P =.012）中具有独立的预后价值。最后，我们构建了一个包含吸烟史、T 分期、世界卫生组织分级、肿瘤大小和 BCGI 的列线图。然后，BCGI 在接受 BCG 治疗的 NMIBC 患者中具有显著的独立预后价值，而 EAU2021 评分或分类则没有观察到这种作用。
基于这些结果，我们合理地认为 BCGI 可能是一种预测接受 BCG 治疗的 NMIBC 患者的有用指标。此外，我们还证明了使用临床因素和 Lasso 回归模型构建预后指数的有效性，这是一种适用于各种医疗情况的通用方法。
