文章：

结直肠肝转移预后临床预测模型：基于回归与机器学习模型的全面综述

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

原文发布日期：25 April 2024

DOI: 10.3390/cancers16091645

类型: Article

开放获取: 是

英文摘要：

Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of “high-risk” patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework.

摘要翻译： 

结直肠癌肝转移（CRLM）因其高发率和潜在可治愈性而值得特别关注。识别“高风险”患者正日益成为风险分层和管理路径个性化的重要方向。传统基于回归的方法已被用于构建此类患者的预测模型，近期研究焦点逐渐转向基于人工智能的模型，并采用了多种监督与非监督学习技术。这些研究选取了总生存期（OS）、无病生存期（DFS）以及术后并发症发生或复发等多重终点作为结局指标。本综述系统梳理了现有聚焦CRLM预后的临床预测模型，重点阐释了各模型纳入的不同预测因子类型，概述了建模策略与结局指标选择原则，详细探讨了模型中包含的特定患者特征与治疗参数，并对模型构建与验证方法进行批判性评述，最后在提出的评估框架内对各模型性能进行了系统性评价。

原文链接：

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models