Predicting malnutrition in gastric cancer patients using computed tomography(CT) deep learning features and clinical data

OBJECTIVE: The aim of this study is using clinical factors and non-enhanced computed tomography (CT) deep features of the psoas muscles at third lumbar vertebral (L3) level to construct a model to predict malnutrition in gastric cancer before surgery, and to provide a new nutritional status assessment and survival assessment tool for gastric cancer patients. METHODS: A retrospective analysis of 312 patients of gastric cancer were divided into malnutrition group and normal group based on Nutrition Risk Screening 2002(NRS-2002). 312 regions of interest (ROI) of the psoas muscles at L3 level of non-enhanced CT were delineated. Deep learning (DL) features were extracted from the ROI using a deep migration model and were screened by principal component analysis (PCA) and least-squares operator (LASSO). The clinical predictors included Body Mass Index (BMI), lymphocyte and albumin. Both deep learning model (including deep learning features) and mixed model (including selected deep learning features and selected clinical predictors) were constructed by 11 classifiers. The model was evaluated and selected by calculating receiver operating characteristic (ROC), area under curve (AUC), accuracy, sensitivity and specificity, calibration curve and decision curve analysis (DCA). The Cohen's Kappa coefficient (κ) was using to compare the diagnostic agreement for malnutrition between the mixed model and the GLIM in gastric cancer patients. RESULT: The results of logistics multivariate analysis showed that BMI [OR = 0.569 (95% CI 0.491-0.660)], lymphocyte [OR = 0.638 (95% CI 0.408-0.998)], and albumin [OR = 0.924 (95% CI 0.859-0.994)] were clinically independent malnutrition of gastric cancer predictor(P < 0.05). Among the 11 classifiers, the Multilayer Perceptron (MLP)were selected as the best classifier. The AUC of the training and test sets for deep learning model were 0.806 (95% CI 0.7485-0.8635) and 0.769 (95% CI 0.673-0.863) and with accuracies were 0.734 and 0.766, respectively. The AUC of the training and test sets for the mixed model were 0.909 (95% CI 0.869-0.948) and 0.857 (95% CI 0.782-0.931) and with accuracies of 0.845 and 0.861, respectively. The DCA confirmed the clinical benefit of the both models. The Cohen's Kappa coefficient (κ) was 0.647 (P < 0.001). Diagnostic agreement for malnutrition between the mixed model and GLIM criteria was good. The mixed model was used to calculate the predicted probability of malnutrition in gastric cancer patients, which was divided into high-risk and low-risk groups by median, and the survival analysis showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group (P = 0.005). CONCLUSION: Deep learning based on mixed model may be a potential tool for predicting malnutrition in gastric cancer patients.