Comparison of the prognostic value of different skeletal muscle radiodensity parameters in endometrial cancer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Nathalia Silva de Paula, Camila Santos Rodrigues, Gabriela Villaça Chaves

ABSTRACT

BACKGROUND/OBJECTIVES: Recent data have shown that dividing skeletal muscle (SM) into sub-ranges of radiodensity can improve the prediction of short-term outcomes in the oncology setting. We aim to investigate whether the skeletal muscle mass, when divided into sub-ranges of low or high-radiodensity, improves the prediction of short-term survival in endometrial cancer (EC) patients when compared to average muscle attenuation and to the overall skeletal muscle radiodensity. SUBJECTS/METHODS: EC patients who had computed tomography (CT) images available within 30 days before treatment were enrolled in this retrospective cohort (n = 232). CT images at the third lumbar vertebra (L3) were used to assess overall skeletal muscle index (SMI). Then we divided SMI into sub-ranges of radiation attenuation: low-radiodensity skeletal muscle index (LRSMI) and high-radiodensity skeletal muscle index (HRSMI). The average muscle radiation attenuation was also assessed. Low SMI was defined when SMI was <38.9 cm2/m2. One-year survival was evaluated by Kaplan-Meier method and Cox Regression. RESULTS: Sarcopenia was found in 25.8% of the patients. Roughly 80% of the patients in the highest quartile of LRSMI were obese. All the skeletal muscle parameters were significantly associated with shorter 1-year survival, LRSMI presented a trend for significance in the adjusted model. When the SM parameters were additionally adjusted for low SMI, only HRSMI and LRSMI remained in the model as early-mortality predictors. CONCLUSIONS: Classifying the skeletal muscle into sub-ranges of radiodensity have an additional value than using the average muscle attenuation of the overall skeletal muscle area and should be exploited in further studies. More... »

PAGES

1-7

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URI

http://scigraph.springernature.com/pub.10.1038/s41430-018-0163-5

DOI

http://dx.doi.org/10.1038/s41430-018-0163-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103549206

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29691486


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