AIMC Topic: Squamous Cell Carcinoma of Head and Neck

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One-slice CT image based kernelized radiomics model for the prediction of low/mid-grade and high-grade HNSCC.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Dec 23, 2019
An accurate grade prediction can help to appropriate treatment strategy and effective diagnosis to Head and neck squamous cell carcinoma (HNSCC). Radiomics has been studied for the prediction of carcinoma characteristics in medical images. The succes...
Radiographic Image Interpretation, Computer-Assisted Machine Learning Tomography, X-Ray Computed Squamous Cell Carcinoma of Head and Neck Neoplasm Grading Humans
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Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology Dec 9, 2019
PURPOSE: Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents...
Humans Tomography, X-Ray Computed Reproducibility of Results Head and Neck Neoplasms Lymphatic Metastasis Neoplasm Staging Extranodal Extension Deep Learning ROC Curve Lymph Nodes Squamous Cell Carcinoma of Head and Neck
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Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool.

Virchows Archiv : an international journal of pathology Aug 17, 2019
Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTS...
Sensitivity and Specificity Prognosis Male Female Middle Aged Internet Squamous Cell Carcinoma of Head and Neck Neoplasm Recurrence, Local Tongue Neoplasms Humans Neural Networks, Computer Machine Learning Aged Adult
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Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality.

BMC bioinformatics Jun 17, 2019
BACKGROUND: In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNA-variant and RNA expressio...
Algorithms Squamous Cell Carcinoma of Head and Neck Databases, Genetic Principal Component Analysis Area Under Curve Gene Ontology RNA, Neoplasm Humans Machine Learning
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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...
Logistic Models Squamous Cell Carcinoma of Head and Neck Area Under Curve Parotid Gland Retrospective Studies Tomography, X-Ray Computed Deep Learning ROC Curve Pharyngeal Neoplasms Oropharyngeal Neoplasms Submandibular Gland Xerostomia Radiotherapy Planning, Computer-Assisted Radiotherapy, Conformal Radiotherapy, Image-Guided Laryngeal Neoplasms Humans Hypopharyngeal Neoplasms
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Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.

European radiology Apr 12, 2019
OBJECTIVES: This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphad...
Head and Neck Neoplasms Neck Lymphatic Metastasis Neoplasm Staging Female Humans Multidetector Computed Tomography Machine Learning Male Lymph Nodes Squamous Cell Carcinoma of Head and Neck
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A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning.

Medical physics Apr 4, 2019
PURPOSE: This study suggests a lifelong learning-based convolutional neural network (LL-CNN) algorithm as a superior alternative to single-task learning approaches for automatic segmentation of head and neck (OARs) organs at risk.
Risk Humans Image Processing, Computer-Assisted Radiotherapy, Image-Guided Tomography, X-Ray Computed Squamous Cell Carcinoma of Head and Neck Deep Learning Organs at Risk Automation
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Prognostic value of tumor volume in patients with head and neck squamous cell carcinoma treated with primary surgery.

Head & neck Dec 22, 2017
BACKGROUND: Tumor volume in head and neck squamous cell carcinoma (HNSCC) was mainly measured in nonsurgically treated patients. We analyzed the influence of tumor volume on complete response (CR), overall survival (OS), and clear surgical margins al...
Statistics, Nonparametric Male Margins of Excision Middle Aged Survival Analysis Humans Squamous Cell Carcinoma of Head and Neck Tomography, X-Ray Computed Tumor Burden Risk Assessment Prognosis Head and Neck Neoplasms Female Immunohistochemistry Logistic Models Registries Retrospective Studies Aged Biopsy, Needle Disease-Free Survival Adult
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Construction of mass spectra database and diagnosis algorithm for head and neck squamous cell carcinoma.

Oral oncology Nov 10, 2017
OBJECTIVES: Intraoperative identification of tumor margins is essential to achieving complete tumor resection. However, the process of intraoperative pathological diagnosis involves cumbersome procedures, such as preparation of cryosections and micro...
Reproducibility of Results Aged Aged, 80 and over Male Middle Aged Machine Learning Intraoperative Period Humans Case-Control Studies Algorithms Spectrometry, Mass, Electrospray Ionization Squamous Cell Carcinoma of Head and Neck Databases, Factual Head and Neck Neoplasms Carcinoma, Squamous Cell
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TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

Scientific reports Jul 31, 2017
Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical n...
Natural Language Processing Supervised Machine Learning Data Mining Humans Prognosis Middle Aged Electronic Health Records Papillomavirus Infections Female Squamous Cell Carcinoma of Head and Neck Aged Head and Neck Neoplasms Male
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