Javid Mohamed, Eldefrawy Mahmoud, Sridhar Sai Raghavendra, Roy Mukesh, Rubens Muni, Manoharan Murugesan
Miami Cancer Institute, Baptist Health South Florida, Miami, FL, USA.
Texas A & M University-Corpus Christi, Corpus Christi, TX, USA.
Int Urol Nephrol. 2025 Apr 1. doi: 10.1007/s11255-025-04467-5.
To explore and assess the role of artificial intelligence (AI) in predicting the postoperative renal function in Renal Cell Carcinoma (RCC) patients undergoing nephrectomy.
A comprehensive literature search was conducted across multiple databases, including PubMed, Embase, Scopus, and Web of Science. PRISMA guidelines were followed throughout the systematic review and meta-analysis. The studies that used AI models to predict renal function after nephrectomy were included in our review. The details of different AI models, the input variables used to train and validate them, and the output generated from these models were recorded and analysed. The risk of bias was assessed using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST).
After the screening, a total of nine studies were included for the final analysis. The most common AI algorithms that were used to predict were based on machine learning models, namely Random Forest (RF), support vector machine (SVM) and XGBoost. Different performance metrics of various AI models were analysed. The pooled AUROC (area under the receiver operating curve) of the AI models was 0.79 (0.75-0.84), I = 15.26%.
AI models exhibit significant potential for determining postoperative renal function in RCC patients. They integrate multimodal data to generate more accurate results. However, standardising the methodologies and reporting, utilising diverse datasets, and improving model interpretability can lead to widespread clinical adaptation.
探讨并评估人工智能（AI）在预测接受肾切除术的肾细胞癌（RCC）患者术后肾功能中的作用。
在多个数据库中进行了全面的文献检索，包括PubMed、Embase、Scopus和Web of Science。在整个系统评价和荟萃分析过程中遵循PRISMA指南。我们的综述纳入了使用AI模型预测肾切除术后肾功能的研究。记录并分析了不同AI模型的详细信息、用于训练和验证它们的输入变量以及这些模型产生的输出。使用预测模型研究偏倚风险评估工具（PROBAST）评估偏倚风险。
筛选后，共纳入9项研究进行最终分析。用于预测的最常见AI算法基于机器学习模型，即随机森林（RF）、支持向量机（SVM）和XGBoost。分析了各种AI模型的不同性能指标。AI模型的合并受试者工作特征曲线下面积（AUROC）为0.79（0.75 - 0.84），I = 15.26%。
AI模型在确定RCC患者术后肾功能方面具有显著潜力。它们整合多模态数据以产生更准确的结果。然而，使方法和报告标准化、利用多样的数据集以及提高模型的可解释性可促进其在临床上的广泛应用。
