Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin-si, Gyeonggi-do, Republic of Korea.
Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
J Cachexia Sarcopenia Muscle. 2023 Apr;14(2):847-859. doi: 10.1002/jcsm.13176. Epub 2023 Feb 12.
Personalized survival prediction is important in gastric cancer patients after gastrectomy based on large datasets with many variables including time-varying factors in nutrition and body morphometry. One year after gastrectomy might be the optimal timing to predict long-term survival because most patients experience significant nutritional change, muscle loss, and postoperative changes in the first year after gastrectomy. We aimed to develop a personalized prognostic artificial intelligence (AI) model to predict 5 year survival at 1 year after gastrectomy.
From a prospectively built gastric surgery registry from a tertiary hospital, 4025 gastric cancer patients (mean age 56.1 ± 10.9, 36.2% females) treated gastrectomy and survived more than a year were selected. Eighty-nine variables including clinical and derived time-varying variables were used as input variables. We proposed a multi-tree extreme gradient boosting (XGBoost) algorithm, an ensemble AI algorithm based on 100 datasets derived from repeated five-fold cross-validation. Internal validation was performed in split datasets (n = 1121) by comparing our proposed model and six other AI algorithms. External validation was performed in 590 patients from other hospitals (mean age 55.9 ± 11.2, 37.3% females). We performed a sensitivity analysis to analyse the effect of the nutritional and fat/muscle indices using a leave-one-out method.
In the internal validation, our proposed model showed AUROC of 0.8237, which outperformed the other AI algorithms (0.7988-0.8165), 80.00% sensitivity, 72.34% specificity, and 76.17% balanced accuracy. In the external validation, our model showed AUROC of 0.8903, 86.96% sensitivity, 74.60% specificity, and 80.78% balanced accuracy. Sensitivity analysis demonstrated that the nutritional and fat/muscle indices influenced the balanced accuracy by 0.31% and 6.29% in the internal and external validation set, respectively. Our developed AI model was published on a website for personalized survival prediction.
Our proposed AI model provides substantially good performance in predicting 5 year survival at 1 year after gastric cancer surgery. The nutritional and fat/muscle indices contributed to increase the prediction performance of our AI model.
基于包含营养和身体形态学等时变因素的大型数据集，对胃癌患者术后进行个性化生存预测非常重要。胃癌患者术后 1 年可能是预测长期生存的最佳时机，因为大多数患者在术后 1 年内会经历显著的营养变化、肌肉损失和术后变化。我们旨在开发一种个性化预后人工智能（AI）模型，以预测胃癌患者术后 1 年的 5 年生存率。
从一家三级医院前瞻性建立的胃外科登记处中，选择了 4025 例接受胃癌手术且存活超过 1 年的胃癌患者（平均年龄 56.1±10.9 岁，36.2%为女性）。我们将 89 个变量（包括临床和衍生的时变变量）作为输入变量。我们提出了一种多树极端梯度提升（XGBoost）算法，这是一种基于 100 个重复五重交叉验证数据集的集成 AI 算法。通过比较我们提出的模型和其他六种 AI 算法，在分割数据集（n=1121）中进行了内部验证。在来自其他医院的 590 例患者（平均年龄 55.9±11.2 岁，37.3%为女性）中进行了外部验证。我们通过使用留一法进行了敏感性分析，以分析营养和脂肪/肌肉指数的影响。
在内部验证中，我们提出的模型的 AUROC 为 0.8237，优于其他 AI 算法（0.7988-0.8165），灵敏度为 80.00%，特异性为 72.34%，平衡准确率为 76.17%。在外部验证中，我们的模型的 AUROC 为 0.8903，灵敏度为 86.96%，特异性为 74.60%，平衡准确率为 80.78%。敏感性分析表明，在内部和外部验证集中，营养和脂肪/肌肉指数分别对平衡准确率的影响为 0.31%和 6.29%。我们开发的 AI 模型已在一个用于个性化生存预测的网站上发布。
我们提出的 AI 模型在预测胃癌手术后 1 年的 5 年生存率方面表现出了优异的性能。营养和脂肪/肌肉指数有助于提高我们 AI 模型的预测性能。
