Confounders in developing a machine learning model for colorectal liver metastasis post-hepatectomy prognostications  

作　　者：Kin Pan Au Tan To Cheung 

机构地区：[1]Department of Surgery,School of Clinical Medicine,Li Ka Shing Faculty of Medicine,The University of Hong Kong,Hong Kong,China

出　　处：《Hepatobiliary Surgery and Nutrition》2024年第2期389-390,共2页肝胆外科与营养（英文）

摘　　要：We extend our gratitude to Dr.Liu and colleagues for their valuable feedback and comments on our article(1).We developed a machine learning model which predicted the outcomes of surgical treatment for colorectal liver metastasis(CRLM)with good discriminative ability.While analyzing the data,we found that patients who underwent neoadjuvant chemotherapy before resection had lower rates of overall survival and disease-free survival.We agree that the negative impact of neoadjuvant chemotherapy on survival is probably a confounding factor due to a more advanced disease status at the time of presentation.This is supported by our data,which show that patients who received neoadjuvant therapy were more likely to have had bilobar liver metastasis(45.2%vs.23.3%,P<0.001)and multiple liver lesions(64.9%vs.42.3%,P<0.001)when compared to those who underwent upfront hepatectomy.

关 键 词：METASTASIS CHEMOTHERAPY COLORECTAL 

分 类 号：R73-37[医药卫生—肿瘤]