Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
JCO Precis Oncol. 2024 Oct;8:e2400089. doi: 10.1200/PO.24.00089. Epub 2024 Oct 21.
Robotic-assisted proctectomy (RAP) has emerged as the predominant surgical approach for patients with rectal cancer in recent years; although good postoperative patient recovery with accurate prediction is a guarantee of adaptive surveillance management, there is still a lack of easy-to-use prognostic tools and risk scores designed specifically for those patients undergoing RAP.
This study used the electronic health records of 506 RAP participants, including a National Specialist Center for da Vinci Robotic Colorectal Surgery (NSCVRCS) meta cohort, and an independent external validation Sun Yat-sen Memorial Hospital cohort. In the NSCVRCS meta cohort, patients were divided into a discovery cohort (70%, n = 268), where the best-fit model was applied to model our prediction system, RAP-AIscore. Subsequently, an internal validation process for RAP-AIscore was conducted using a replication cohort (30%, n = 116). The study designed and implemented a large-scale artificial intelligence (AI) hybrid framework to identify the best strategy for building a survival assessment system, the RAP-AIscore, from 132 potential modeling scenarios through a combination of iterative cross-validation, Monte Carlo cross-validation, and bootstrap resampling. The 10 variables most relevant to clinical interpretability were identified on the basis of the AI hybrid optimal model values, which helps provide reliable prognostic survival guidance for new patients.
The consistent evaluation of discrimination, calibration, generalization, and prognostic value across cohorts reaffirmed the accuracy and robust extrapolation capability of this system. The 10 feature variables most associated with clinical interpretability on the basis of Shapley values were identified, facilitating reliable prognostic survival guidance for new patients.
This study introduces a promising and informative tool, the RAP-AIscore, which can be explained through nomograms for interpreting clinical outcomes. It facilitates postoperative risk stratification management and enhances clinical management of prognosis for RAP patients.
近年来，机器人辅助直肠切除术（RAP）已成为直肠癌患者的主要手术方法；尽管准确预测有利于患者术后恢复，但仍缺乏专门为接受 RAP 的患者设计的易于使用的预后工具和风险评分。
本研究使用了 506 名 RAP 参与者的电子健康记录，包括达芬奇机器人结直肠外科国家专家中心（NSCVRCS）元队列和中山大学孙逸仙纪念医院独立外部验证队列。在 NSCVRCS 元队列中，患者被分为发现队列（70%，n=268），在该队列中应用最佳拟合模型来构建我们的预测系统 RAP-AIscore。随后，使用复制队列（30%，n=116）对 RAP-AIscore 进行内部验证。该研究设计并实施了一个大规模的人工智能（AI）混合框架，通过迭代交叉验证、蒙特卡罗交叉验证和自举重采样相结合，从 132 种潜在建模方案中确定了构建生存评估系统 RAP-AIscore 的最佳策略。根据 AI 混合最优模型值确定了与临床可解释性最相关的 10 个变量，这有助于为新患者提供可靠的预后生存指导。
通过一致性评估，该系统在不同队列中的区分度、校准度、泛化能力和预后价值得到了验证，证明了其准确性和稳健的外推能力。根据 Shapley 值确定了与临床可解释性最相关的 10 个特征变量，为新患者提供了可靠的预后生存指导。
本研究引入了一种有前途和信息丰富的工具，即 RAP-AIscore，它可以通过列线图来解释临床结果。它有助于术后风险分层管理，并增强 RAP 患者的预后临床管理。
