AIMC Topic: Head and Neck Neoplasms

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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).
Diagnosis, Differential Area Under Curve Observer Variation Alphapapillomavirus Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Regression Analysis Tomography, X-Ray Computed Humans Female Machine Learning Reproducibility of Results Algorithms Male Retrospective Studies Imaging, Three-Dimensional Image Processing, Computer-Assisted Papillomavirus Infections
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Artificial Intelligence in Head and Neck Imaging: A Glimpse into the Future.

Neuroimaging clinics of North America Jun 10, 2020
Artificial intelligence, specifically machine learning and deep learning, is a rapidly developing field in imaging sciences with the potential to improve the efficiency and effectiveness of radiologists. This review covers common technical terms and ...
Humans Artificial Intelligence Tomography, X-Ray Computed Head and Neck Neoplasms Magnetic Resonance Imaging Image Interpretation, Computer-Assisted
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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.
Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Lymphatic Metastasis Mouth Neoplasms Humans Carcinoma, Squamous Cell Deep Learning Lymph Nodes
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Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy.

Physics in medicine and biology Apr 28, 2020
In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation d...
Middle Aged Cone-Beam Computed Tomography Image Processing, Computer-Assisted Head and Neck Neoplasms Aged, 80 and over Adult Male Radiometry Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Proton Therapy Humans Neural Networks, Computer Female Aged
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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
Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Extranodal Extension Humans Deep Learning
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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
Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Extranodal Extension Humans Machine Learning Algorithms Deep Learning
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A convolutional neural network-based system to classify patients using FDG PET/CT examinations.

BMC cancer Mar 17, 2020
BACKGROUND: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly ...
Adult Pelvic Neoplasms Male Abdominal Neoplasms Female Middle Aged Humans Thoracic Neoplasms Aged Neural Networks, Computer Head and Neck Neoplasms Aged, 80 and over Artificial Intelligence Young Adult Positron Emission Tomography Computed Tomography Fluorodeoxyglucose F18
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Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.

Scientific reports Mar 11, 2020
A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data lea...
Papillomaviridae Prognosis Retrospective Studies Tomography, X-Ray Computed Survival Rate Humans Head and Neck Neoplasms Deep Learning Privacy ROC Curve Data Accuracy Papillomavirus Infections Image Interpretation, Computer-Assisted
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Artificial neural networks allow response prediction in squamous cell carcinoma of the scalp treated with radiotherapy.

Journal of the European Academy of Dermatology and Venereology : JEADV Mar 4, 2020
BACKGROUND: Epithelial neoplasms of the scalp account for approximately 2% of all skin cancers and for about 10-20% of the tumours affecting the head and neck area. Radiotherapy is suggested for localized cutaneous squamous cell carcinomas (cSCC) wit...
Humans Neural Networks, Computer Forecasting Phenotype Retrospective Studies Female Skin Neoplasms Male Middle Aged Carcinoma, Squamous Cell Treatment Outcome Age Factors Aged Head and Neck Neoplasms Neoplasm Staging Radiotherapy Dosage Aged, 80 and over Scalp Neoplasm Recurrence, Local
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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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