AIMC Topic: Oropharyngeal Neoplasms
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Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Jan 14, 2025
PURPOSE: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM ris...
Clinical implementation of deep learning robust IMPT planning in oropharyngeal cancer patients: A blinded clinical study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Sep 6, 2024
BACKGROUND AND PURPOSE: This study aimed to evaluate the plan quality of our deep learning-based automated treatment planning method for robustly optimized intensity-modulated proton therapy (IMPT) plans in patients with oropharyngeal carcinoma (OPC)...
MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.
International journal of surgery (London, England)
Aug 1, 2024
INTRODUCTION: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to...
Machine Learning Methods in Classification of Prolonged Radiation Therapy in Oropharyngeal Cancer: National Cancer Database.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Jul 31, 2024
OBJECTIVE: To investigate the accuracy of machine learning (ML) algorithms in stratifying risk of prolonged radiation treatment duration (RTD), defined as greater than 50 days, for patients with oropharyngeal squamous cell carcinoma (OPSCC).
Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images.
Scientific reports
Jun 20, 2024
Several studies have emphasised how positive and negative human papillomavirus (HPV+ and HPV-, respectively) oropharyngeal squamous cell carcinoma (OPSCC) has distinct molecular profiles, tumor characteristics, and disease outcomes. Different radiom...
Impact of F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2024
We aimed to investigate the effects of F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. We extracted 1,037 F-FDG PET radiomic features q...
Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.
American journal of otolaryngology
Apr 29, 2024
BACKGROUND: Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV statu...
In Reference to Evaluation of Oropharyngeal Cancer Information from Revolutionary Artificial Intelligence Chatbot.
The Laryngoscope
Feb 1, 2024
In Response to Evaluation of Oropharyngeal Cancer Information from Revolutionary Artificial Intelligence Chatbot.
The Laryngoscope
Feb 1, 2024
Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.
European radiology
Jan 20, 2024
PURPOSE: To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net.
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