Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.

Journal: Journal of surgical oncology

Published Date: Dec 1, 2021

Abstract

BACKGROUND: Previous studies evaluating the prognostic value of computed tomography (CT)-derived body composition data have included few patients. Thus, we assessed the prevalence and prognostic value of sarcopenic obesity in a large population of gastric cancer patients using preoperative CT, as nutritional status is a predictor of long-term survival after gastric cancer surgery.

Authors

Jaehyuk Kim

Molecular Imaging, Medical Biophysics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Seung Hee Han

Molecular Imaging, Medical Biophysics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Hyoung-Il Kim

Molecular Imaging, Medical Biophysics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

Keywords

Body Composition Body Mass Index Case-Control Studies Cross-Sectional Studies Female Follow-Up Studies Gastrectomy Humans Machine Learning Male Middle Aged Muscle, Skeletal Obesity Prognosis Risk Factors Sarcopenia Stomach Neoplasms Survival Rate Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (34490899)

Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.

Abstract

Authors

Keywords

External Resources

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