Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.
Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.
Clin Nutr. 2024 Mar;43(3):881-891. doi: 10.1016/j.clnu.2024.02.005. Epub 2024 Feb 6.
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.
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.
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).
Deep learning based on mixed model may be a potential tool for predicting malnutrition in gastric cancer patients.
本研究旨在利用临床因素和非增强 CT 下腰 3 椎体水平竖脊肌的深度特征构建一个模型，以预测胃癌术前的营养不良，并为胃癌患者提供新的营养状况评估和生存评估工具。
回顾性分析了 312 例胃癌患者，根据营养风险筛查 2002（NRS-2002）将其分为营养不良组和正常组。在非增强 CT 下勾画 L3 水平的 312 个感兴趣区（ROI）。使用深度迁移模型从 ROI 中提取深度学习特征，并通过主成分分析（PCA）和最小二乘算子（LASSO）进行筛选。临床预测指标包括体重指数（BMI）、淋巴细胞和白蛋白。使用 11 种分类器构建了深度学习模型（包括深度学习特征）和混合模型（包括选定的深度学习特征和选定的临床预测指标）。通过计算接收者操作特征（ROC）、曲线下面积（AUC）、准确性、灵敏度和特异性、校准曲线和决策曲线分析（DCA）来评估和选择模型。采用 Cohen's Kappa 系数（κ）比较混合模型和 GLIM 对胃癌患者营养不良的诊断一致性。
多变量逻辑回归分析结果表明，BMI [OR=0.569（95%CI 0.491-0.660）]、淋巴细胞 [OR=0.638（95%CI 0.408-0.998）]和白蛋白 [OR=0.924（95%CI 0.859-0.994）]是胃癌患者临床独立的营养不良预测因素（P<0.05）。在 11 种分类器中，多层感知器（MLP）被选为最佳分类器。深度学习模型的训练集和测试集的 AUC 分别为 0.806（95%CI 0.7485-0.8635）和 0.769（95%CI 0.673-0.863），准确率分别为 0.734 和 0.766。混合模型的训练集和测试集的 AUC 分别为 0.909（95%CI 0.869-0.948）和 0.857（95%CI 0.782-0.931），准确率分别为 0.845 和 0.861。DCA 证实了两种模型的临床获益。混合模型和 GLIM 标准的 Cohen's Kappa 系数（κ）为 0.647（P<0.001）。两种方法对营养不良的诊断一致性良好。使用混合模型计算胃癌患者营养不良的预测概率，以中位数将其分为高危和低危组，生存分析显示高危组的总生存时间明显低于低危组（P=0.005）。
基于混合模型的深度学习可能是预测胃癌患者营养不良的一种有潜力的工具。
