Neuroimaging clinics of North America
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewPatient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging.
The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. ⋯ The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewNeck Imaging Reporting and Data System: Principles and Implementation.
Head and neck cancer surveillance imaging is diagnostically challenging, often with highly distorted anatomy after surgery and chemoradiation therapy. In the era of standardized reporting, the Neck Imaging Reporting and Data System (NI-RADS) was developed as a numerical classification system to provide clear and concise radiology reports and recommend next management step. There are 5 categories, each conveying a certain level of suspicion for the presence of persistent or recurrent disease. This article reviews the goals of NI-RADS, NI-RADS categories and lexicon, current research, and the future direction of NI-RADS in posttreatment head and neck cancer surveillance.
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewCommon Data Elements in Head and Neck Radiology Reporting.
Radiologists must convert the complex information in head and neck imaging into text reports that can be understood and used by clinicians, patients, and fellow radiologists for patient care, research, and quality initiatives. Common data elements in reporting, through use of defined questions with constrained answers and terminology, allow radiologists to incorporate best practice standards and improve communication of information regardless of individual reporting style. Use of common data elements for head and neck reporting has the potential to improve outcomes, reduce errors, and transition data consumption not only for humans but future machine learning systems.
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewArtificial Intelligence in Head and Neck Imaging: A Glimpse into the Future.
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 basic concepts in imaging artificial intelligence and briefly reviews the application of these techniques to general imaging as well as head and neck imaging. Artificial intelligence has the potential to contribute improvements to all areas of patient care, including image acquisition, processing, segmentation, automated detection of findings, integration of clinical information, quality improvement, and research. Numerous challenges remain, however, before widespread imaging clinical adoption and integration occur.