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使用计算机断层扫描 (CT) 深度学习功能和临床数据预测胃癌患者的营养不良

Weijia Huang¹, Congjun Wang¹, Ye Wang¹

¹Department 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.

Clinical nutrition (Edinburgh, Scotland)

|February 20, 2024

概括

这项研究开发了一种结合临床因素和基于CT的深度学习特征的新型模型,以预测胃癌患者的手术前营养不良. 该模型对评估营养状况和改善生存预测有希望.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
医疗成像医学成像

背景情况:

营养不良是胃癌患者的一个重大问题,影响治疗结果.
准确的手术前营养评估对于优化患者管理至关重要.
现有的评估工具在预测营养不良风险方面可能存在局限性.

研究的目的:

开发和验证胃癌手术前营养不良的预测模型.
整合临床因素与深度学习衍生CT特征的psoas肌肉.
建立一种用于胃癌患者营养和生存评估的新工具.

主要方法:

对312名胃癌患者的回顾性分析.
从L3 psoas肌肉CT扫描中提取深度学习特征.
开发一种结合临床预测因素 (BMI,淋巴细胞,白蛋白) 和深度学习特征的混合模型.
使用ROC,AUC,精度,灵敏度,特异性,校准曲线和决策曲线分析进行验证.

主要成果:

临床预测指标BMI,淋巴细胞和白蛋白独立与营养不良有关.
混合模型实现了高性能 (在训练中AUC高达0.909,在测试中为0.857).
该模型证明了与GLIM标准的良好诊断一致性,并预测了生存结果.

结论:

一个基于深度学习的混合模型显示了预测胃癌手术前营养不良的潜力.
这种方法为营养状况和生存率的评估提供了一个新的工具.
进一步的验证可能会在临床实践中建立这种模式,以改善患者护理.

关键词:

计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.胃癌胃癌是指胃癌.营养不良营养不良主要的Psoas主要的Psoas.

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