AIMC Topic: Head and Neck Neoplasms

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Development of a patient reported outcomes based machine learning model to predict recurrences in head and neck cancer.

Oral oncology
INTRODUCTION: Recurrence rates among Head and Neck Cancer (HNC) patients are high, with earlier detection associated with improved survival. Patient-reported outcomes (PROs) have increasingly been found to predict patient care needs. Here, we examine...

Machine learning in risk assessment for microvascular head and neck surgery.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: The integration of machine learning (ML) into microvascular surgery for the head and neck offers significant potential to enhance risk stratification, outcome prediction, and decision support. Traditional risk assessment methods are often li...

Improvement of deep learning-based dose conversion accuracy to a Monte Carlo algorithm in proton beam therapy for head and neck cancers.

Journal of radiation research
This study is aimed to clarify the effectiveness of the image-rotation technique and zooming augmentation to improve the accuracy of the deep learning (DL)-based dose conversion from pencil beam (PB) to Monte Carlo (MC) in proton beam therapy (PBT). ...

On factors that influence deep learning-based dose prediction of head and neck tumors.

Physics in medicine and biology
This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components ne...

Navigating challenging patient factors in systemic therapy for head and neck cancer.

The oncologist
Patients with head and neck cancer often present with complex challenges due to a substantial comorbidity burden, including substance use disorders, and the tumor's location in regions that are both cosmetically and anatomically sensitive. These chal...

Identification of a telomere-related gene signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma by integrated analysis of machine learning and Mendelian randomization.

Medicine
Telomere-related genes (TRGs) are vital in diverse tumor types. Nevertheless, there is a notable lack of in-depth research concerning their significance in head and neck squamous cell carcinoma (HNSCC). In this context, the present study aims to asse...

Machine learning-driven identification of exosome- related biomarkers in head and neck squamous cell carcinoma.

Frontiers in immunology
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a common cancer associated with elevated mortality rates. Exosomes, diminutive extracellular vesicles, significantly contribute to tumour development, immunological evasion, and treatment r...

Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

Medicine
To assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for elderly head and neck squamous cell carcinoma (HNSCC) patients who are not surgery candidates. A comp...

Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study.

JCO global oncology
PURPOSE: Most research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study ...