Predicting cachexia in hepatocellular carcinoma patients: a nomogram based on MRI features and body composition
Xin-Xiang Li1, Bing Liu2, Yu-Fei Zhao1
1Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, PR China.
Acta Radiologica (Stockholm, Sweden : 1987)
|July 25, 2024
Summary
Predicting cachexia in hepatocellular carcinoma (HCC) patients is crucial. A new nomogram using MRI and body composition shows promise for early detection of cachexia in HCC.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
Background:
- Cachexia affects approximately 50% of hepatocellular carcinoma (HCC) patients.
- Early prediction of cachexia is vital for timely intervention in HCC management.
Purpose of the Study:
- To develop and validate a nomogram for predicting cachexia in HCC patients.
- The nomogram integrates magnetic resonance imaging (MRI) features and body composition data.
Main Methods:
- Retrospective study of 411 HCC patients across two centers.
- Data divided into development, internal validation, and external validation cohorts.
- Logistic regression analysis identified independent predictors of cachexia.
Main Results:
- Key predictors identified: tumor size > 5 cm, intratumoral artery, skeletal muscle index, and subcutaneous fat area.
- The nomogram demonstrated strong predictive performance with an AUC of 0.819 (development), 0.783 (internal validation), and 0.814 (external validation).
Conclusions:
- The developed multivariable nomogram exhibits good performance for predicting cachexia risk in HCC patients.
- This tool can aid in early identification and management of cachexia in HCC.
