AIMC Topic: Laryngeal Neoplasms

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Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network.

Biomedical engineering online May 25, 2021
BACKGROUND: Early detection of laryngeal masses without periodic visits to hospitals is essential for improving the possibility of full recovery and the long-term survival ratio after prompt treatment, as well as reducing the risk of clinical infecti...
Image Processing, Computer-Assisted Mass Screening Early Detection of Cancer Automation Laryngeal Neoplasms Humans Neural Networks, Computer Algorithms
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Limitations of Transoral Robotic Total Laryngectomy.

Journal of voice : official journal of the Voice Foundation Dec 15, 2020
Humans Robotics Robotic Surgical Procedures Laryngeal Neoplasms Laryngectomy
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Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer.

JAMA network open Nov 2, 2020
IMPORTANCE: Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.
Humans Neural Networks, Computer Machine Learning Laryngeal Neoplasms Otorhinolaryngologic Surgical Procedures Oropharyngeal Neoplasms Hypopharyngeal Neoplasms Squamous Cell Carcinoma of Head and Neck Deep Learning Chemoradiotherapy, Adjuvant Neoplasm Staging Retrospective Studies Cohort Studies Proportional Hazards Models Neoplasm Grading Patient Selection Female Logistic Models Radiotherapy, Adjuvant Mouth Neoplasms Tumor Burden Aged Lymph Nodes Male
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Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.

Clinical radiology Oct 6, 2020
AIM: To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally a...
Male Middle Aged Predictive Value of Tests Positron Emission Tomography Computed Tomography Female Carcinoma, Squamous Cell Aged Adult Laryngeal Neoplasms Hypopharynx Neoplasm Recurrence, Local Radiopharmaceuticals Fluorodeoxyglucose F18 Chemoradiotherapy Humans Machine Learning
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Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.

The Laryngoscope Apr 28, 2020
OBJECTIVES/HYPOTHESIS: Create an autonomous computational system to classify endoscopy findings.
Machine Learning Biopsy Image Interpretation, Computer-Assisted Laryngoscopy Larynx Humans Neural Networks, Computer Laryngeal Neoplasms Artificial Intelligence Laryngeal Diseases
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Machine learning helps identifying volume-confounding effects in radiomics.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Feb 20, 2020
Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding...
Retrospective Studies Tomography, X-Ray Computed Female Carcinoma, Squamous Cell Adult Male Middle Aged Aged Aged, 80 and over Machine Learning Laryngeal Neoplasms Cluster Analysis Decision Support Systems, Clinical Software Oropharyngeal Neoplasms Humans Regression Analysis Lung Neoplasms Algorithms Principal Component Analysis Databases, Factual Squamous Cell Carcinoma of Head and Neck Radiometry Carcinoma, Non-Small-Cell Lung
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Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.

The Laryngoscope Feb 18, 2020
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngosco...
Otolaryngologists Humans Deep Learning Retrospective Studies Image Interpretation, Computer-Assisted Adult Laryngoscopy Sensitivity and Specificity Female Laryngeal Neoplasms Male Reproducibility of Results
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Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images.

EBioMedicine Oct 5, 2019
OBJECTIVE: To develop a deep convolutional neural network (DCNN) that can automatically detect laryngeal cancer (LCA) in laryngoscopic images.
Models, Theoretical Laryngoscopy Reproducibility of Results Laryngeal Neoplasms Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Humans
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A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

International journal of radiation oncology, biology, physics Jun 13, 2019
PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensio...
Radiotherapy, Image-Guided Laryngeal Neoplasms Submandibular Gland Humans Deep Learning Xerostomia Area Under Curve Pharyngeal Neoplasms Hypopharyngeal Neoplasms Retrospective Studies ROC Curve Parotid Gland Tomography, X-Ray Computed Oropharyngeal Neoplasms Logistic Models Radiotherapy Planning, Computer-Assisted Squamous Cell Carcinoma of Head and Neck Radiotherapy, Conformal
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Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy.

Theranostics Apr 13, 2019
Maximal resection of tumor while preserving the adjacent healthy tissue is particularly important for larynx surgery, hence precise and rapid intraoperative histology of laryngeal tissue is crucial for providing optimal surgical outcomes. We hypothes...
Nonlinear Optical Microscopy Humans China Automation, Laboratory Histological Techniques Carcinoma, Squamous Cell Laryngeal Neoplasms Deep Learning Pathology, Surgical
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