Artificial intelligence in dysphagia assessment: evaluating lingual muscle composition in head and neck cancer.

IF 2.8 3区医学 Q2 ONCOLOGY

Clinical & Translational Oncology Pub Date : 2025-03-25 DOI:10.1007/s12094-025-03900-6

Laura Ferrera Alayón, Barbara Salas-Salas, Fiorella Ximena Palmas-Candia, Raquel Diaz-Saavedra, Anais Ramos-Ortiz, Pedro C Lara, Marta Lloret Sáez-Bravo

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引用次数: 0

Abstract

Purpose: Oropharyngeal dysphagia is a common and debilitating condition in head and neck cancer (HNC) patients. This study aimed to evaluate the relationship between tongue muscle composition (quantity and quality) and the risk of dysphagia in non-surgically treated HNC patients, using artificial intelligence (AI) analysis of pretreatment computed tomography (CT) scans.

Methods: A prospective analysis was conducted on 41 non-surgically treated HNC patients under-going curative radiotherapy. Tongue muscle quantity was measured as cross-sectional area (cm²) and as a percentage of body composition using AI-based segmentation of CT images. Muscle quality was assessed through Hounsfield Units (HU), representing muscle density. Dysphagia risk was evaluated with the validated EAT-10 questionnaire, considering scores ≥ 3 as indicative of increased risk.

Results: A significant association was found between EAT-10 categorical scores and dysphagia risk (Chi² = 26.07, p < 0.0001). However, no significant correlation was observed between the percentage of tongue muscle and density (R = 0.081, p = 0.07). Patients with EAT-10 scores ≥ 3 had significantly larger percentages of tongue muscle area (mean 61.17 ± 10.44 cm²) compared to those with EAT-10 < 3 (mean 56.58 ± 5.77 cm²; p = 0.004). Additionally, higher tongue muscle density (HU) was associated with increased dysphagia risk (p = 0.046). A significant association was also observed between pre-treatment and post-treatment dysphagia, with patients who reported pre-treatment dysphagia (EAT-10 ≥ 3) continuing to experience higher post-treatment dysphagia (p = 0.009, R = 0.411). Biologically Effective Dose (BED) (p = 0.0042), advanced tumor stage (p = 0.004), and systemic treatment (p = 0.027) were further associated with increased post-treatment dysphagia risk.

Conclusions: The study demonstrates that non-surgically treated HNC patients with increased tongue area percentages and higher muscle density are at greater risk of dysphagia. Additionally, pre-treatment dysphagia was found to be a strong predictor of post-treatment dysphagia. The use of AI-based CT analysis provides a precise method for identifying patients at risk, allowing for timely interventions to improve swallowing function and quality of life.

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人工智能在吞咽困难评估中的应用：头颈癌患者舌肌组成的评估。

目的：口咽吞咽困难是头颈癌（HNC）患者常见的虚弱症状。本研究旨在利用人工智能（AI）分析预处理计算机断层扫描（CT）扫描结果，评估非手术治疗HNC患者舌肌组成（数量和质量）与吞咽困难风险之间的关系。方法：对41例接受根治性放疗的非手术治疗的HNC患者进行前瞻性分析。使用基于人工智能的CT图像分割，以横截面积（cm2）和身体组成的百分比来测量舌肌量。肌肉质量通过代表肌肉密度的Hounsfield单位（HU）进行评估。使用经验证的EAT-10问卷评估吞咽困难风险，考虑得分≥3为风险增加的指标。结果：与EAT-10评分组相比，EAT-10分类评分与吞咽困难风险之间存在显著关联（ch2 = 26.07, p 2）；p = 0.004)。此外，较高的舌肌密度（HU）与吞咽困难风险增加相关（p = 0.046）。治疗前和治疗后的吞咽困难之间也存在显著关联，治疗前报告吞咽困难（EAT-10≥3）的患者在治疗后继续经历更高的吞咽困难（p = 0.009, R = 0.411）。生物有效剂量（BED）（p = 0.0042）、肿瘤分期（p = 0.004）和全身治疗（p = 0.027）与治疗后吞咽困难风险增加进一步相关。结论：本研究表明，非手术治疗的舌面积百分比和肌肉密度增加的HNC患者发生吞咽困难的风险更大。此外，治疗前的吞咽困难被发现是治疗后吞咽困难的一个强有力的预测因子。使用基于人工智能的CT分析提供了一种精确的方法来识别处于危险中的患者，允许及时干预以改善吞咽功能和生活质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical & Translational Oncology 医学-肿瘤学

CiteScore

6.20

自引率

2.90%

发文量

240

审稿时长

1 months

期刊介绍： Clinical and Translational Oncology is an international journal devoted to fostering interaction between experimental and clinical oncology. It covers all aspects of research on cancer, from the more basic discoveries dealing with both cell and molecular biology of tumour cells, to the most advanced clinical assays of conventional and new drugs. In addition, the journal has a strong commitment to facilitating the transfer of knowledge from the basic laboratory to the clinical practice, with the publication of educational series devoted to closing the gap between molecular and clinical oncologists. Molecular biology of tumours, identification of new targets for cancer therapy, and new technologies for research and treatment of cancer are the major themes covered by the educational series. Full research articles on a broad spectrum of subjects, including the molecular and cellular bases of disease, aetiology, pathophysiology, pathology, epidemiology, clinical features, and the diagnosis, prognosis and treatment of cancer, will be considered for publication.