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已发表论文
基于常见临床参数构建预测接受系统治疗的 BCLC B/C 期肝细胞癌患者恶病质发生的列线图
Authors Ma W, Han J, Yu H, Liang Z, Peng C, Lu Y
Received 21 August 2025
Accepted for publication 6 November 2025
Published 20 December 2025 Volume 2025:12 Pages 2845—2857
DOI https://doi.org/10.2147/JHC.S558288
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Mohamed Shaker
Weihong Ma,1,2 Jie Han,1,2 Hongli Yu,1,2 Zhipeng Liang,3 Caiyun Peng,1 Yinying Lu1,2
1Senior Department of Hepatology, The 5th Medical Center of the PLA General Hospital, Beijing, People’s Republic of China; 2Chinese PLA Medical School, Beijing, People’s Republic of China; 3Guizhou Medical University, Guizhou, People’s Republic of China
Correspondence: Yinying Lu, Email luyinying1973@163.com Caiyun Peng, Email 1419567156@qq.com
Background and Objective: During the disease course of patients with BCLC B/C hepatocellular carcinoma (HCC) receiving systemic therapy, approximately half of the patients will develop cachexia. Therefore, early identification of which patients are likely to develop cachexia is of crucial significance.This study aims to construct and validate a nomogram for predicting the risk of cachexia in this population based on common clinical parameters.
Patients and Methods: This retrospective single - center study involved 906 patients managed at the Fifth Medical Center of Chinese PLA General Hospital from January 2020 to December 2023. Baseline clinical imaging data, biochemical indicators, and relevant clinical data of patients before systemic treatment were collected. All patients were followed up to record treatment regimens and document weight changes for cachexia diagnosis. The data were stratified into a training cohort and a validation cohort. In this study, LASSO regression alongside univariate and multivariate Logistic regression analyses were utilized to ascertain independent risk factors linked to cachexia occurrence, and further to construct and validate a diagnostic nomogram.
Results: This nomogram incorporates predictors such as patient age, maximum size of intrahepatic lesions, extrahepatic metastasis, neutrophil-to-lymphocyte ratio (NLR), and total bile acids, demonstrating good predictive performance. In the training and validation cohorts, its Harrell’s concordance index (C-index) reached 0.865 (95% CI: 0.836– 0.895) and 0.820 (95% CI: 0.768– 0.871), respectively. Calibration curves demonstrated strong consistency between the nomogram’s predicted outcomes and the actual measured values, and decision curve analysis (DCA) further substantiated its clinical applicability.
Conclusion: This nomogram shows good predictive performance and can effectively identify high-risk individuals, but it is limited by its single-center retrospective design and requires further verification and optimization through multicenter prospective studies.
Keywords: cancer-related cachexia, targeted immunotherapy, model evaluation, weight loss
1Senior Department of Hepatology, The 5th Medical Center of the PLA General Hospital, Beijing, People’s Republic of China; 2Chinese PLA Medical School, Beijing, People’s Republic of China; 3Guizhou Medical University, Guizhou, People’s Republic of China
Correspondence: Yinying Lu, Email luyinying1973@163.com Caiyun Peng, Email 1419567156@qq.com
Background and Objective: During the disease course of patients with BCLC B/C hepatocellular carcinoma (HCC) receiving systemic therapy, approximately half of the patients will develop cachexia. Therefore, early identification of which patients are likely to develop cachexia is of crucial significance.This study aims to construct and validate a nomogram for predicting the risk of cachexia in this population based on common clinical parameters.
Patients and Methods: This retrospective single - center study involved 906 patients managed at the Fifth Medical Center of Chinese PLA General Hospital from January 2020 to December 2023. Baseline clinical imaging data, biochemical indicators, and relevant clinical data of patients before systemic treatment were collected. All patients were followed up to record treatment regimens and document weight changes for cachexia diagnosis. The data were stratified into a training cohort and a validation cohort. In this study, LASSO regression alongside univariate and multivariate Logistic regression analyses were utilized to ascertain independent risk factors linked to cachexia occurrence, and further to construct and validate a diagnostic nomogram.
Results: This nomogram incorporates predictors such as patient age, maximum size of intrahepatic lesions, extrahepatic metastasis, neutrophil-to-lymphocyte ratio (NLR), and total bile acids, demonstrating good predictive performance. In the training and validation cohorts, its Harrell’s concordance index (C-index) reached 0.865 (95% CI: 0.836– 0.895) and 0.820 (95% CI: 0.768– 0.871), respectively. Calibration curves demonstrated strong consistency between the nomogram’s predicted outcomes and the actual measured values, and decision curve analysis (DCA) further substantiated its clinical applicability.
Conclusion: This nomogram shows good predictive performance and can effectively identify high-risk individuals, but it is limited by its single-center retrospective design and requires further verification and optimization through multicenter prospective studies.
Keywords: cancer-related cachexia, targeted immunotherapy, model evaluation, weight loss