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

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.

Keywords: body mass index; gastric cancer; machine learning; nutrition process; sarcopenic obesity; survival.

Figure 1

Machine learning algorithm for body composition analysis. (A) Maximum intensity projection (MIP) method for L3 annotation. (B) Machine learning network: layers of the network were based on DeepLab V3+, and Resnet‐18 was used as the base network. CT, computed tomography

Figure 2

Representative computed tomography images of groups based on sarcopenia and obesity criteria. Red color stands for the skeletal muscle area, yellow color stands for the visceral fat area

Figure 3

Segmentation and patient groups. (A) Sarcopenia according to skeletal mass index (SMI). (B) Obesity according to visceral fat area (VFA). (C) Scatter plots of VFA and SMI. (D) Distribution of body mass index according to patient group

Figure 4

Overall survival according to the sarcopenia, obesity, and sarcopenic obesity groups. (A) Sarcopenia versus non‐sarcopenia: all patients (p = 0.018). (B) Sarcopenia versus non‐sarcopenia: patients with stage I/II disease (p = 0.030). (C) Sarcopenia versus non‐sarcopenia: patients with stage III/IV disease (p = 0.945). (D) Obesity versus non‐obesity: all patients (p = 0.293). (E) Obesity versus non‐obesity: patients with stage I/II disease (p = 0.046). (F) Obesity versus non‐obesity: patients with stage III/IV disease (p = 0.825). (G) Sarcopenic obesity versus non‐sarcopenic obesity: all patients (p < 0.001). (H) Sarcopenic obesity versus non‐sarcopenic obesity: patients with stage I/II disease (p < 0.001). (I) Sarcopenic obesity versus non‐sarcopenic obesity: patients with stage III/IV disease (p = 0.392)