AIMC Topic: Head and Neck Neoplasms

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Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Medical physics
BACKGROUND: Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly conc...

Current Status and Future Directions of Research on Artificial Intelligence in Nasopharyngolaryngoscopy.

Respiration; international review of thoracic diseases
BACKGROUND: The nasopharyngolaryngoscopy (NPL) has emerged as a valuable tool for detecting early cases of head and neck cancers. However, misdiagnoses and missed diagnoses are still common phenomena. The expertise of examining physicians often serve...

Quality and mechanical efficiency of automated knowledge-based planning for volumetric-modulated arc therapy in head and neck cancer.

Journal of applied clinical medical physics
OBJECTIVES: This study aimed to examine the effectiveness of the automated RapidPlan in assessing plan quality and to explore how beam complexity affects the mechanical performance of volumetric modulated arc therapy for head and neck cancers.

Deep learning prediction of scenario doses for direct plan robustness evaluations in IMPT for head-and-neck.

Physics in medicine and biology
. Intensity modulated proton therapy (IMPT) is susceptible to uncertainties in patient setup and proton range. Robust optimization is employed in IMPT treatment planning to ensure sufficient coverage of the clinical target volume (CTV) in predefined ...

Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.

Neuroradiology
INTRODUCTION: Head and neck cancers are the seventh most common globally, with lymph node metastasis (LNM) being a critical prognostic factor, significantly reducing survival rates. Traditional imaging methods have limitations in accurately diagnosin...

Clinical commissioning and introduction of an in-house artificial intelligence (AI) platform for automated head and neck intensity modulated radiation therapy (IMRT) treatment planning.

Journal of applied clinical medical physics
BACKGROUND AND PURPOSE: To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-neck (HN) Intensity Modulated Radiation Therapy (IMRT).

Exploring patient stratification in head and neck squamous cell carcinoma using machine learning techniques: Preliminary results.

Current problems in cancer
BACKGROUND: Head and Neck Squamous Cell Carcinoma (HNSCC) presents a significant challenge in oncology due to its inherent heterogeneity. Traditional staging systems, such as TNM (Tumor, Node, Metastasis), provide limited information regarding patien...

OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model for Radiotherapy Image Segmentation.

Clinical oncology (Royal College of Radiologists (Great Britain))
Several studies report the benefits and accuracy of using autosegmentation for organ at risk (OAR) outlining in radiotherapy treatment planning. Typically, evaluations focus on accuracy metrics, and other parameters such as perceived utility and safe...