Oncology research and treatment · 2026
The risk of radiotherapy toxicity in patients with laryngeal cancer: predictive factors and radiobiological modelling.
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Korinek Tomas, Pechacova Zdenka, Lohynska Radka, Koniarova Irena
Аннотация
INTRODUCTION: The assessment of post-radiation toxicity and quality of life is a crucial aspect of patient care. To evaluate and predict post-irradiation toxicity in a cohort of patients with laryngeal cancer, radiobiological modelling, biological and medical indicators, dose-volume variables, and radiomics were used.
METHODS: For the comprehensive evaluation, biological and medical factors, treatment plan doses, and radiomic features from pre-treatment CT images were analyzed using the PyRadiomics Python library. The Lyman-Kutcher-Burman (LKB) and Niemierko general equivalent uniform dose (gEUD) formalisms were applied for modelling and parameter estimation. The chi-square test assessed significance between categorical variables, while one-way ANOVA and the Spearman correlation were applied for continuous variables. All analyses were conducted using a custom Python 3.8 script.
RESULTS: Significant indicators impacting post-irradiation toxicity were identified. The American Society of Anesthesiologists (ASA) comorbidity score, pre-treatment body weight loss, and weight loss during therapy were consistently associated with acute hematological, skin, and gastrointestinal toxicities. For late outcomes, pre-treatment weight loss, alcohol consumption, diabetes, and first symptoms emerged as significantly associated with overall and salivary gland toxicity. In addition, dose-volume parameters of the buccal mucosa, pharyngeal constrictor, and tongue correlated with acute mucositis, dysphagia, and xerostomia. Radiomic analysis revealed that larger and flatter target volumes were linked to increased toxicities, with PTV (Planning Target Volume) size strongly associated with hematological effects. The LKB model further identified parameters characterising the dose-response relationship for xerostomia, dysphagia, acute gastrointestinal toxicity, and overall acute toxicity.
CONCLUSION: The study identified significant factors influencing post-treatment health status and established LKB model parameters associated with radiation-induced complications.
METHODS: For the comprehensive evaluation, biological and medical factors, treatment plan doses, and radiomic features from pre-treatment CT images were analyzed using the PyRadiomics Python library. The Lyman-Kutcher-Burman (LKB) and Niemierko general equivalent uniform dose (gEUD) formalisms were applied for modelling and parameter estimation. The chi-square test assessed significance between categorical variables, while one-way ANOVA and the Spearman correlation were applied for continuous variables. All analyses were conducted using a custom Python 3.8 script.
RESULTS: Significant indicators impacting post-irradiation toxicity were identified. The American Society of Anesthesiologists (ASA) comorbidity score, pre-treatment body weight loss, and weight loss during therapy were consistently associated with acute hematological, skin, and gastrointestinal toxicities. For late outcomes, pre-treatment weight loss, alcohol consumption, diabetes, and first symptoms emerged as significantly associated with overall and salivary gland toxicity. In addition, dose-volume parameters of the buccal mucosa, pharyngeal constrictor, and tongue correlated with acute mucositis, dysphagia, and xerostomia. Radiomic analysis revealed that larger and flatter target volumes were linked to increased toxicities, with PTV (Planning Target Volume) size strongly associated with hematological effects. The LKB model further identified parameters characterising the dose-response relationship for xerostomia, dysphagia, acute gastrointestinal toxicity, and overall acute toxicity.
CONCLUSION: The study identified significant factors influencing post-treatment health status and established LKB model parameters associated with radiation-induced complications.