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• W3197832936 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. Methods Preoperative CT images were analyzed for 840 gastric cancer patients who underwent gastrectomy between March 2009 and June 2018. Machine learning algorithms were used to automatically detect the third lumbar (L3) vertebral level and segment the body composition. Visceral fat area and skeletal muscle index at L3 were determined and used to classify patients into obesity, sarcopenia, or sarcopenic obesity groups. Results Out of 840 patients (mean age = 60.4 years; 526 [62.6%] men), 534 (63.5%) had visceral obesity, 119 (14.2%) had sarcopenia, and 48 (5.7%) patients had sarcopenic obesity. Patients with sarcopenic obesity had a poorer prognosis than those without sarcopenia (hazard ratio [HR] = 3.325; 95% confidence interval [CI] = 1.698–6.508). Multivariate analysis identified sarcopenic obesity as an independent risk factor for increased mortality (HR = 2.608; 95% CI = 1.313–5.179). Other risk factors were greater extent of gastrectomy (HR = 1.928; 95% CI = 1.260–2.950), lower prognostic nutritional index (HR = 0.934; 95% CI = 0.901–0.969), higher neutrophil count (HR = 1.101; 95% CI = 1.031–1.176), lymph node metastasis (HR = 6.291; 95% CI = 3.498–11.314), and R1/2 resection (HR = 4.817; 95% CI = 1.518–9.179). Conclusion Body composition analysis automated by machine learning predicted long-term survival in patients with gastric cancer." @default.
• W3197832936 created "2021-09-13" @default.
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• W3197832936 date "2021-09-07" @default.
• W3197832936 modified "2023-10-18" @default.
• W3197832936 title "Detection of sarcopenic obesity and prediction of long‐term survival in patients with gastric cancer using preoperative computed tomography and machine learning" @default.
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• W3197832936 doi "https://doi.org/10.1002/jso.26668" @default.
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