Cui Feng, Dang Xiangji, Peng Daiyun, She Yuanhua, Wang Yubin, Yang Ruifeng, Han Zhiyao, Liu Yan, Yang Hanteng
Department of General Surgery, Lanzhou University Second Hospital, Cui Ying Men No.80Gansu Province, Lanzhou, 730030, People's Republic of China.
Department of Pharmaceutical, Lanzhou University Second Hospital, Cui Ying Men No.80, Lanzhou, 730030, Gansu Province, People's Republic of China.
BMC Cancer. 2025 May 22;25(1):919. doi: 10.1186/s12885-025-14303-9.
Sarcopenia is a clinicopathological condition characterized by a decrease in muscle strength and muscle mass, playing a crucial role in the prognosis of cancer. Therefore, this study aims to investigate the association between sarcopenia and both all-cause mortality and cancer-specific mortality among cancer patients. Furthermore, we plan to develop risk prediction models using machine learning algorithms to predict 3-year and 5-year survival rates in cancer patients.
This study included 1095 cancer patients from the National Health and Nutrition Examination Survey (NHANES) cohorts spanning 1999-2006 and 2011-2014. Initially, we used the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression models for feature selection. Subsequently, we employed multivariable Cox regression models to investigate the association between sarcopenia and all-cause and cancer-specific mortality in cancer patients. We developed five machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), LightGBM, and XGBoost, to predict 3-year and 5-year survival rates and to perform risk stratification.
The multivariable COX regression model showed sarcopenia significantly increases the risk of all-cause mortality (HR = 1.33, 95%CI:1.05, 1.70, P = 0.0194) and cancer-specific mortality (HR = 1.67, 95%CI:1.09, 2.55, P = 0.0176) in cancer patients. Among the five machine learning algorithms developed, the LightGBM model demonstrated strong performance in the 3-year and 5-year survival prediction tasks, making it the optimal model selection. Decision curve analysis and Kaplan-Meier curves further confirmed our model's ability to identify high-risk individuals effectively.
Sarcopenia significantly increases the risk of mortality in cancer patients. We developed a survival prediction model for cancer patients that effectively identifies high-risk individuals, thereby providing a foundation for personalized survival assessment.
肌肉减少症是一种临床病理状况，其特征为肌肉力量和肌肉质量下降，在癌症预后中起关键作用。因此，本研究旨在调查癌症患者中肌肉减少症与全因死亡率和癌症特异性死亡率之间的关联。此外，我们计划使用机器学习算法开发风险预测模型，以预测癌症患者的3年和5年生存率。
本研究纳入了来自1999 - 2006年和2011 - 2014年国家健康与营养检查调查（NHANES）队列的1095名癌症患者。最初，我们使用最小绝对收缩和选择算子（LASSO）- Cox回归模型进行特征选择。随后，我们采用多变量Cox回归模型来研究癌症患者中肌肉减少症与全因死亡率和癌症特异性死亡率之间的关联。我们开发了五种机器学习算法，包括支持向量机（SVM）、逻辑回归（LR）、随机森林（RF）、LightGBM和XGBoost，以预测3年和5年生存率并进行风险分层。
多变量COX回归模型显示，肌肉减少症显著增加了癌症患者的全因死亡率风险（HR = 1.33，95%CI：1.05，1.70，P = 0.0194）和癌症特异性死亡率风险（HR = 1.67，95%CI：1.09，2.55，P = 0.0176）。在开发的五种机器学习算法中，LightGBM模型在3年和5年生存预测任务中表现出色，成为最佳模型选择。决策曲线分析和Kaplan - Meier曲线进一步证实了我们模型有效识别高危个体的能力。
肌肉减少症显著增加了癌症患者的死亡风险。我们为癌症患者开发了一种生存预测模型，该模型能有效识别高危个体，从而为个性化生存评估提供了基础。
