Background and Objectives: Partial nephrectomy (PN) is the preferred option for treating localized cT1 renal cell carcinoma (RCC), as it preserves renal function in most patients and offers non-inferior oncological outcomes compared to radical nephrectomy. In this study, we aimed to construct a predictive model for estimating the glomerular filtration rate (GFR) at one year after PN in patients with RCC, using various machine learning techniques.Methods: Retrospective data were collected from two academic centers, covering surgeries performed between 2010 and 2022. GFR was estimated using the Chronic Kidney Disease Epidemiology Collaboration 2021 (CKD-EPI) formula. Univariable linear regression (LR) was used to identify significant clinical predictors of 1-year postoperative GFR, followed by multivariable LR. The dataset was split into training and testing cohorts in a 70:30 ratio. Internal validation was performed on the test cohort, and various machine learning methods, including artificial neural networks (ANNs), support vector machines (SVMs), random forests (RFs), and XGBoost, were compared.Results: Among 615 patients treated with PN, 415 had complete follow-up GFR data and were included in the analysis. Only 8.7% of patients experienced significant GFR loss (>30% decrease) at 1 year. Multivariable LR identified baseline GFR (Estimate: 0.76,p< 0.001), tumor diameter on imaging (Estimate: −1.65,p= 0.005), and Charlson Comorbidity Index (Estimate: −1.95,p< 0.001) as independent predictors of 1-year GFR (R2= 0.67). A 10-fold cross-validation of the multivariable model yielded an R2of 0.68. In the testing cohort, ANN, SVM, RF, and XGBoost did not outperform the LR model, with R2values of 0.68, 0.66, 0.64, and 0.55, respectively.Conclusions: Preoperative factors, including baseline GFR, tumor size on imaging, and Charlson Comorbidity Index, are effective predictors of GFR at 1 year following PN. Our study demonstrates that a conventional LR model based on preoperative variables provides acceptable accuracy for predicting GFR after PN and is not inferior to more complex machine learning techniques.
**背景与目的：** 部分肾切除术是治疗局限性cT1期肾细胞癌的首选方案，因其能在多数患者中保留肾功能，且肿瘤学疗效不劣于根治性肾切除术。本研究旨在运用多种机器学习技术，构建一个预测模型，用于评估肾细胞癌患者部分肾切除术后一年的肾小球滤过率。 **方法：** 回顾性收集了2010年至2022年间两个学术中心的手术数据。肾小球滤过率采用慢性肾脏病流行病学协作组2021公式进行估算。首先使用单变量线性回归识别术后1年肾小球滤过率的显著临床预测因子，随后进行多变量线性回归分析。数据集按7:3的比例划分为训练集和测试集。在测试集上进行内部验证，并比较了多种机器学习方法，包括人工神经网络、支持向量机、随机森林和XGBoost。 **结果：** 在接受部分肾切除术的615例患者中，415例拥有完整的随访肾小球滤过率数据并被纳入分析。仅8.7%的患者在术后1年出现显著的肾小球滤过率下降（>30%）。多变量线性回归分析确定基线肾小球滤过率（估计值：0.76，p < 0.001）、影像学肿瘤直径（估计值：-1.65，p = 0.005）和查尔森合并症指数（估计值：-1.95，p < 0.001）是术后1年肾小球滤过率的独立预测因子（R² = 0.67）。对该多变量模型进行10折交叉验证，得到的R²为0.68。在测试集中，人工神经网络、支持向量机、随机森林和XGBoost模型的性能均未优于线性回归模型，其R²值分别为0.68、0.66、0.64和0.55。 **结论：** 包括基线肾小球滤过率、影像学肿瘤大小和查尔森合并症指数在内的术前因素，是预测部分肾切除术后1年肾小球滤过率的有效指标。我们的研究表明，基于术前变量的传统线性回归模型在预测部分肾切除术后肾小球滤过率方面具有可接受的准确性，且不逊于更复杂的机器学习技术。
肿瘤(癌症)患者之家