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人工智能对食障碍的评估:在头癌中评估语言肌肉组成

Laura Ferrera Alayón^1,2, Barbara Salas-Salas¹, Fiorella Ximena Palmas-Candia³

¹Department of Radiation Oncology, University Hospital Dr Negrín, Las Palmas de Gran Canaria, Spain.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico

|March 25, 2025

概括

头癌患者的舌头肌面积更大,密度更高,面临更高的消化不良风险. 治疗前的消化障碍也预测了治疗后的问题,突出了AI.

科学领域:

在瘤学瘤学.
放射学放射学是指放射学
人工智能的人工智能

背景情况:

口腔喉乱是头癌 (HNC) 患者常见的,使人衰弱的疾病.
准确评估食障碍风险对于及时干预和改善患者结果至关重要.

研究的目的:

评估舌头肌肉组成 (数量和质量) 与非手术治疗中HNC患者的消化障碍风险之间的关系.
为了利用人工智能 (AI) 分析治疗前的计算机断层扫描 (CT) 扫描来评估舌头肌肉特征.

主要方法:

对41名接受治疗放射治疗的非手术治疗的HNC患者进行前性分析.
基于人工智能的CT图像细分,以测量舌头肌肉量 (截面面积和身体组成百分比).
使用Hounsfield单位 (HU) 评估肌肉质量,以测量肌肉密度,并使用EAT-10调查问卷测量缺食症风险.

主要成果:

患有较高消化不良风险的患者 (EAT-10分数≥3) 的舌头肌面积百分比显著增加.
增加舌头肌肉密度 (HU) 与更高的失消症风险有关.
治疗前的消化障碍是治疗后消化障碍的重要预测因素,还发现了生物有效剂量 (BED),晚期瘤阶段和全身治疗的关联.

结论:

非手术治疗的HNC患者,舌部区域百分比增加和肌肉密度更高,患食症的风险更大.
治疗前的消化障碍是治疗后消化障碍的强烈预测因素.
基于人工智能的CT分析提供了一种精确的方法来识别有风险的患者,使得及时的干预能够改善吞功能和生活质量.

关键词:

人工智能的人工智能是人工智能.脱障碍脱障碍脱障碍头部和部的新生瘤.肌肉肌肉肌肉肌肉辐射疗法辐射疗法骨的骨架部分断层扫描 (Tomography) 是一个专业的技术.舌头的舌头是一种舌头.

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