AIMC Topic: Oropharyngeal Neoplasms

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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...
Humans Male Machine Learning Middle Aged Software Decision Support Systems, Clinical Algorithms Lung Neoplasms Databases, Factual Carcinoma, Squamous Cell Retrospective Studies Principal Component Analysis Aged Tomography, X-Ray Computed Radiometry Female Aged, 80 and over Laryngeal Neoplasms Adult Squamous Cell Carcinoma of Head and Neck Oropharyngeal Neoplasms Carcinoma, Non-Small-Cell Lung Cluster Analysis Regression Analysis
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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...
Humans Retrospective Studies Tomography, X-Ray Computed Area Under Curve Parotid Gland Radiotherapy Planning, Computer-Assisted Radiotherapy, Conformal Deep Learning ROC Curve Logistic Models Squamous Cell Carcinoma of Head and Neck Oropharyngeal Neoplasms Submandibular Gland Xerostomia Hypopharyngeal Neoplasms Radiotherapy, Image-Guided Laryngeal Neoplasms Pharyngeal Neoplasms
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Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks.

Physics in medicine and biology Nov 7, 2018
Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where detailed knowledge of the pathways of microscopic tumor...
Oropharyngeal Neoplasms Image Processing, Computer-Assisted Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Humans Neural Networks, Computer Retrospective Studies Tomography, X-Ray Computed
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Detecting oropharyngeal carcinoma using multispectral, narrow-band imaging and machine learning.

The Laryngoscope Mar 25, 2018
OBJECTIVE: To determine if multispectral narrow-band imaging (mNBI) can be used for automated, quantitative detection of oropharyngeal carcinoma (OPC).
Carcinoma Pattern Recognition, Automated Oropharyngeal Neoplasms Narrow Band Imaging Male Humans Middle Aged Diagnosis, Differential Machine Learning Bayes Theorem Prospective Studies Female Aged Aged, 80 and over Sensitivity and Specificity
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Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function.

International journal of radiation oncology, biology, physics Feb 7, 2018
PURPOSE: Automating and standardizing the contouring of clinical target volumes (CTVs) can reduce interphysician variability, which is one of the largest sources of uncertainty in head and neck radiation therapy. In addition to using uniform margin e...
Head and Neck Neoplasms Tumor Burden Humans Observer Variation Algorithms Oropharyngeal Neoplasms Deep Learning
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Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 20, 2017
BACKGROUND AND PURPOSE: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify wh...
Humans Oropharyngeal Neoplasms Machine Learning Decision Support Systems, Clinical Decision Making Radiotherapy Planning, Computer-Assisted
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Flex Robotic System in transoral robotic surgery: The first 40 patients.

Head & neck Oct 28, 2016
BACKGROUND: The Flex Robotic System is a new robotic device specifically developed for transoral robotic surgery (TORS).
Robotic Surgical Procedures Immunohistochemistry Biopsy, Needle Natural Orifice Endoscopic Surgery Oropharyngeal Neoplasms Equipment Safety Hypopharyngeal Neoplasms Treatment Outcome Operative Time Robotics Equipment Design Adult Male Patient Safety Middle Aged Humans Prospective Studies Female Aged
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Response to: "No evidence for improved TORS post-treatment feeding tube dependency rate relative to standard therapy in early stage oropharyngeal cancer".

Oral oncology Jun 26, 2015
Surgical Procedures, Operative Oropharyngeal Neoplasms Humans Carcinoma, Squamous Cell Robotics
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No evidence for improved TORS post-treatment feeding tube dependency rate relative to standard therapy in early stage oropharyngeal cancer.

Oral oncology Jun 20, 2015
Robotics Surgical Procedures, Operative Oropharyngeal Neoplasms Humans Carcinoma, Squamous Cell
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Feasibility of transoral robotic-assisted high-resolution microendoscopic imaging of oropharyngeal squamous cell carcinoma.

Head & neck Jun 18, 2015
BACKGROUND: Transoral robotic-assisted oncologic surgery of the head and neck offers promising functional results. Nonetheless, the efficacy of oncologic surgery remains critically dependent on obtaining negative margins. We aimed to integrate a mini...
Mouth Microscopy Image Enhancement Neck Dissection Fiber Optic Technology Robotics Feasibility Studies Prospective Studies Humans Carcinoma, Squamous Cell Natural Orifice Endoscopic Surgery Sensitivity and Specificity Oropharyngeal Neoplasms Treatment Outcome Endoscopes
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