研究动态
Articles below are published ahead of final publication in an issue. Please cite articles in the following format: authors, (year), title, journal, DOI.

结直肠肝转移术后预后预测的机器学习模型。

A machine learning model for colorectal liver metastasis post-hepatectomy prognostications.

发表日期:2023 Aug 01
作者: Cynthia Sin Nga Lam, Alina Ashok Bharwani, Evelyn Hui Yi Chan, Vernice Hui Yan Chan, Howard Lai Ho Au, Margaret Kay Ho, Shireen Rashed, Bernard Ming Hong Kwong, Wentao Fang, Ka Wing Ma, Chung Mau Lo, Tan To Cheung
来源: Hepatobiliary Surgery and Nutrition

摘要:

目前,手术切除是结直肠肝转移瘤(CRLM)管理的主要方法,也是唯一潜在的治愈性治疗模式。预后工具可以支持患者选择手术切除以实现最大的治疗效益。本研究旨在利用基于香港多中心患者样本的机器学习开发一个生存预测模型。研究对象包括2009年1月1日至2018年12月31日期间在香港四家医院进行肝切除术治疗CRLM的患者。采用Cox比例风险模型进行生存分析。对使用最小绝对收缩和选择算子(LASSO)回归的Cox多变量模型进行逐步选择,以构建预测模型的多次插补数据集。验证集中对该模型进行验证,并与Fong临床危险评分(CRS)的性能进行比较,采用协调指数进行评估。 共纳入572名患者,随访中位数为3.6年。总体生存(OS)和无复发生存(RFS)的完整模型包括相同的8个已建立和新的变量,即结直肠癌淋巴结分期、CRLM新辅助治疗、Charlson合并症评分、肝切除前胆红素和胎儿抗原(CEA)水平、CRLM最大肿瘤直径、PET-CT检测到的肝外转移以及KRAS状态。我们的CRLM机器学习算法预后模型(CMAP)在预测OS方面表现出更好的能力(C指数=0.651),与Fong CRS相比,1年(C指数=0.571)和5年OS(C指数=0.574)。对于RFS,该模型也达到了0.651的C指数。 我们提出了一个有前景的机器学习算法,可以针对CRLM切除术后的患者进行个体化预后评估,并具有很好的区分能力。 2023年《肝胆外科》及《营养学》。版权所有。
Currently, surgical resection is the mainstay for colorectal liver metastases (CRLM) management and the only potentially curative treatment modality. Prognostication tools can support patient selection for surgical resection to maximize therapeutic benefit. This study aimed to develop a survival prediction model using machine learning based on a multicenter patient sample in Hong Kong.Patients who underwent hepatectomy for CRLM between 1 January 2009 and 31 December 2018 in four hospitals in Hong Kong were included in the study. Survival analysis was performed using Cox proportional hazards (CPH). A stepwise selection on Cox multivariable models with Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to a multiply-imputed dataset to build a prediction model. The model was validated in the validation set, and its performance was compared with that of Fong Clinical Risk Score (CRS) using concordance index.A total of 572 patients were included with a median follow-up of 3.6 years. The full models for overall survival (OS) and recurrence-free survival (RFS) consist of the same 8 established and novel variables, namely colorectal cancer nodal stage, CRLM neoadjuvant treatment, Charlson Comorbidity Score, pre-hepatectomy bilirubin and carcinoembryonic antigen (CEA) levels, CRLM largest tumor diameter, extrahepatic metastasis detected on positron emission-tomography (PET)-scan as well as KRAS status. Our CRLM Machine-learning Algorithm Prognostication model (CMAP) demonstrated better ability to predict OS (C-index =0.651), compared with the Fong CRS for 1-year (C-index =0.571) and 5-year OS (C-index =0.574). It also achieved a C-index of 0.651 for RFS.We present a promising machine learning algorithm to individualize prognostications for patients following resection of CRLM with good discriminative ability.2023 Hepatobiliary Surgery and Nutrition. All rights reserved.