AIMC Topic: Head and Neck Neoplasms

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TrDosePred: A deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy.

Journal of applied clinical medical physics Mar 3, 2023
BACKGROUND: Intensity-Modulated Radiation Therapy (IMRT) has been the standard of care for many types of tumors. However, treatment planning for IMRT is a time-consuming and labor-intensive process.
Algorithms Deep Learning Humans Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Head and Neck Neoplasms Organs at Risk
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Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images.

Physics in medicine and biology Feb 23, 2023
 Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC) each image volume is explored slice-by-slice from different orientati...
Tomography, X-Ray Computed Deep Learning Humans Positron Emission Tomography Computed Tomography Probability Fluorodeoxyglucose F18 Image Processing, Computer-Assisted Head and Neck Neoplasms Oropharyngeal Neoplasms
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Robot-assisted radial forearm free flap harvesting: a propensity score-matched case-control study.

Journal of robotic surgery Feb 5, 2023
Although some surgeons prefer anterolateral thigh and latissimus dorsi flap for soft tissue reconstruction in the head and neck area because it minimizes donor site complications, the radial forearm flap remains the workhorse for soft tissue reconstr...
Free Tissue Flaps Head and Neck Neoplasms Robotics Robotic Surgical Procedures Humans Reproducibility of Results Propensity Score Case-Control Studies
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A machine learning model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using multiple parameters.

The Journal of laryngology and otology Jan 23, 2023
OBJECTIVE: This study aimed to establish a model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using artificial intelligence algorithms.
Head and Neck Neoplasms Artificial Intelligence Machine Learning Retrospective Studies Squamous Cell Carcinoma of Head and Neck Humans
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Current Role of Delta Radiomics in Head and Neck Oncology.

International journal of molecular sciences Jan 22, 2023
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or ...
Head and Neck Neoplasms Artificial Intelligence Precision Medicine Humans
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A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma.

Academic radiology Nov 30, 2022
RATIONALE AND OBJECTIVES: Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced comp...
Retrospective Studies Tomography, X-Ray Computed Deep Learning Nomograms Humans Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms
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Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study.

American journal of otolaryngology Nov 24, 2022
OBJECTIVES: Video laryngoscopy is an important diagnostic tool for head and neck cancers. The artificial intelligence (AI) system has been shown to monitor blind spots during esophagogastroduodenoscopy. This study aimed to test the performance of AI-...
Humans Laryngoscopy Neural Networks, Computer Retrospective Studies Artificial Intelligence Head and Neck Neoplasms
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Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 1, 2022
BACKGROUND AND PURPOSE: To investigate the performance of head-and-neck (HN) organs-at-risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep learning (DL) solutions.
Deep Learning Radiotherapy Planning, Computer-Assisted Head and Neck Neoplasms Organs at Risk Humans Tomography, X-Ray Computed
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Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists Oct 21, 2022
Accurate clinical target volume (CTV) delineation is important for head and neck intensity-modulated radiation therapy. However, delineation is time-consuming and susceptible to interobserver variability (IOV). Based on a manual contouring process co...
Radiotherapy Planning, Computer-Assisted Deep Learning Humans Tumor Burden Head and Neck Neoplasms Tomography, X-Ray Computed
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Efficient dose-volume histogram-based pretreatment patient-specific quality assurance methodology with combined deep learning and machine learning models for volumetric modulated arc radiotherapy.

Medical physics Oct 17, 2022
BACKGROUND: Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose-volume histogram (DVH) and reconstructe...
Humans Organs at Risk Machine Learning Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Deep Learning Head and Neck Neoplasms Radiotherapy, Intensity-Modulated
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