AIMC Topic: Head and Neck Neoplasms
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Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset.
Clinical oncology (Royal College of Radiologists (Great Britain))
Mar 1, 2025
AIMS: To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).
Worldwide research trends on artificial intelligence in head and neck cancer: a bibliometric analysis.
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 27, 2025
OBJECTIVE: This bibliometric analysis aims to explore scientific data on Artificial Intelligence (AI) and Head and Neck Cancer (HNC).
Democratizing cancer detection: artificial intelligence-enhanced endoscopy could address global disparities in head and neck cancer outcomes.
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
Feb 18, 2025
INTRODUCTION: This article explores the potential role of artificial intelligence (AI) in enhancing the early detection and diagnosis of head and neck squamous cell carcinoma (HNSCC).
Multi-task learning for automated contouring and dose prediction in radiotherapy.
Physics in medicine and biology
Feb 18, 2025
. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve the efficiency and accuracy of radiotherapy. However, conventional radiotherapy treatment planning process has the automated contouring and treatment pl...
Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma.
Cancer imaging : the official publication of the International Cancer Imaging Society
Feb 16, 2025
BACKGROUND: Immunotherapy has revolutionized the treatment landscape for head and neck squamous cell carcinoma (HNSCC) and PD-L1 combined positivity score (CPS) scoring is recommended as a biomarker for immunotherapy. Therefore, this study aimed to d...
Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.
Medical physics
Feb 10, 2025
BACKGROUND: Given the recent increased emphasis on multimodal neural networks to solve complex modeling tasks, the problem of outcome prediction for a course of treatment can be framed as fundamentally multimodal in nature. A patient's response to tr...
Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 8, 2025
PURPOSE: This study investigated the effect of multiple magnetic resonance (MR) sequences on the quality of deep-learning-based synthetic computed tomography (sCT) generation in the head and neck region.
Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC.
Hereditas
Feb 6, 2025
BACKGROUND: Immunotherapy has introduced new breakthroughs in improving the survival of head and neck squamous cell carcinoma (HNSCC) patients, yet drug resistance remains a critical challenge. Developing personalized treatment strategies based on th...
A comparison of different machine learning classifiers in predicting xerostomia and sticky saliva due to head and neck radiotherapy using a multi-objective, multimodal radiomics model.
Biomedical physics & engineering express
Feb 6, 2025
. Although radiotherapy techniques are a primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity and side effects. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on featu...
An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to identify disease-associated genes to estimate cancer mortality risk remains challenging...
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