AIMC Topic: Squamous Cell Carcinoma of Head and Neck

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Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI.

The Laryngoscope Jul 13, 2020
OBJECTIVES: To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status.
Female Aged Male Middle Aged Humans Machine Learning Retrospective Studies Magnetic Resonance Imaging Predictive Value of Tests Feasibility Studies Oropharyngeal Neoplasms Alphapapillomavirus ROC Curve Papillomavirus Infections Logistic Models Squamous Cell Carcinoma of Head and Neck
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Machine Learning Prediction of Extracapsular Extension in Human Papillomavirus-Associated Oropharyngeal Squamous Cell Carcinoma.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Jun 30, 2020
OBJECTIVE: To determine whether machine learning (ML) can predict the presence of extracapsular extension (ECE) prior to treatment, using common oncologic variables, in patients with human papillomavirus (HPV)-associated oropharyngeal squamous cell c...
Machine Learning Databases, Factual Prognosis Logistic Models Female Alphapapillomavirus Retrospective Studies Humans Squamous Cell Carcinoma of Head and Neck ROC Curve Aged Extranodal Extension Papillomavirus Infections Male Oropharyngeal Neoplasms Middle Aged
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Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.

European radiology Jun 26, 2020
OBJECTIVE: To compare the CT texture feature reproducibility of 2D and 3D segmentations and their machine learning (ML)-based classifications for predicting human papilloma virus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC).
Area Under Curve Observer Variation Alphapapillomavirus Reproducibility of Results Male Imaging, Three-Dimensional Image Processing, Computer-Assisted Head and Neck Neoplasms Regression Analysis Diagnosis, Differential Papillomavirus Infections Squamous Cell Carcinoma of Head and Neck Algorithms Retrospective Studies Tomography, X-Ray Computed Humans Female Machine Learning
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Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma.

European radiology Jun 10, 2020
OBJECTIVE: To assess the utility of deep learning analysis using F-fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET/CT) to predict disease-free survival (DFS) in patients with oral cavity squamous cell carcinoma (OCSCC).
Algorithms Retrospective Studies Female Reproducibility of Results Humans Male Middle Aged Diagnosis, Computer-Assisted Deep Learning Glycolysis Aged Aged, 80 and over Adult Sensitivity and Specificity Fluorodeoxyglucose F18 Kaplan-Meier Estimate Treatment Outcome Neoplasm Staging Squamous Cell Carcinoma of Head and Neck Mouth Neoplasms Predictive Value of Tests Disease-Free Survival Positron Emission Tomography Computed Tomography Tumor Burden
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Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study.

Oral radiology Jun 6, 2020
OBJECTIVE: To apply a deep learning object detection technique to CT images for detecting cervical lymph nodes metastasis in patients with oral cancers, and to clarify the detection performance.
Mouth Neoplasms Humans Carcinoma, Squamous Cell Deep Learning Squamous Cell Carcinoma of Head and Neck Lymph Nodes Head and Neck Neoplasms Lymphatic Metastasis
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Reply to A.B. Simon et al.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology Apr 9, 2020
Humans Deep Learning Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Extranodal Extension
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Challenge of Directly Comparing Imaging-Based Diagnoses Made by Machine Learning Algorithms With Those Made by Human Clinicians.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology Apr 9, 2020
Machine Learning Algorithms Deep Learning Humans Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Extranodal Extension
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Alpha Test of Intelligent Machine Learning in Staging Head and Neck Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology Mar 2, 2020
Deep Learning Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Extranodal Extension Humans Machine Learning
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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...
Radiometry Laryngeal Neoplasms Carcinoma, Squamous Cell Oropharyngeal Neoplasms Databases, Factual Aged Decision Support Systems, Clinical Male Carcinoma, Non-Small-Cell Lung Retrospective Studies Humans Lung Neoplasms Aged, 80 and over Middle Aged Tomography, X-Ray Computed Cluster Analysis Machine Learning Adult Squamous Cell Carcinoma of Head and Neck Female Regression Analysis Software Principal Component Analysis Algorithms
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Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice.

DNA and cell biology Feb 11, 2020
Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim ...
RNA, Untranslated Humans Pseudogenes Machine Learning Male Gene Expression Regulation, Neoplastic Prognosis Transcriptome Squamous Cell Carcinoma of Head and Neck Female Databases, Genetic Biomarkers, Tumor
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