Laura Ferrera Alayón, Barbara Salas-Salas, Fiorella Ximena Palmas-Candia, Raquel Diaz-Saavedra, Anais Ramos-Ortiz, Pedro C Lara, Marta Lloret Sáez-Bravo
{"title":"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。","authors":"Laura Ferrera Alayón, Barbara Salas-Salas, Fiorella Ximena Palmas-Candia, Raquel Diaz-Saavedra, Anais Ramos-Ortiz, Pedro C Lara, Marta Lloret Sáez-Bravo","doi":"10.1007/s12094-025-03900-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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 (cm<sup>2</sup>) 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.</p><p><strong>Results: </strong>A significant association was found between EAT-10 categorical scores and dysphagia risk (Chi<sup>2</sup> = 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 cm<sup>2</sup>) compared to those with EAT-10 < 3 (mean 56.58 ± 5.77 cm<sup>2</sup>; 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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":50685,"journal":{"name":"Clinical & Translational Oncology","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in dysphagia assessment: evaluating lingual muscle composition in head and neck cancer.\",\"authors\":\"Laura Ferrera Alayón, Barbara Salas-Salas, Fiorella Ximena Palmas-Candia, Raquel Diaz-Saavedra, Anais Ramos-Ortiz, Pedro C Lara, Marta Lloret Sáez-Bravo\",\"doi\":\"10.1007/s12094-025-03900-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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 (cm<sup>2</sup>) 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.</p><p><strong>Results: </strong>A significant association was found between EAT-10 categorical scores and dysphagia risk (Chi<sup>2</sup> = 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 cm<sup>2</sup>) compared to those with EAT-10 < 3 (mean 56.58 ± 5.77 cm<sup>2</sup>; 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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":50685,\"journal\":{\"name\":\"Clinical & Translational Oncology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-03-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical & Translational Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12094-025-03900-6\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical & Translational Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12094-025-03900-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Artificial intelligence in dysphagia assessment: evaluating lingual muscle composition in head and neck cancer.
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.
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.
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