BackgroundBreast cancer is one of the most commonly diagnosed cancers, and the fourth leading cause of cancer deaths in females worldwide. Sarcopenia is related to adverse clinical outcomes in patients with malignancies. Muscle index is a key parameter in evaluating sarcopenia. However, there is no data investigating the association between muscle index and distant metastasis in breast cancer. The aim of this study was to explore whether muscle index can effectively predict distant metastasis and death outcomes in breast cancer patients.Study DesignThe clinical data of 493 breast cancer patients at the Harbin Medical University Cancer Hospital between January 2014 and December 2015 were retrospectively analyzed. Quantitative measurements of pectoralis muscle area and skeletal muscle area were performed at the level of the fourth thoracic vertebra (T4) and the eleventh thoracic vertebra (T11) of the chest computed tomography image, respectively. The pectoralis muscle index (PMI) and skeletal muscle index (SMI) were assessed by the normalized muscle area (area/the square of height). Survival analysis was performed using the log-rank test and Cox proportional hazards regression analysis.ResultThe patients with metastases had lower PMI at T4 level (PMI/T4) and SMI at T11 level (SMI/T11) compared with the patients without metastases. Moreover, there were significant correlations between PMI/T4 and lymphovascular invasion, Ki67 expression, multifocal disease, and molecular subtype. In addition, multivariate analysis revealed that PMI/T4, not SMI/T11, was an independent prognostic factor for distant metastasis-free survival (DMFS) and overall survival (OS) in breast cancer patients.ConclusionsLow PMI/T4 is associated with worse DMFS and OS in breast cancer patients. Future prospective studies are needed.
Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942–0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.