A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation

In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus making active motion management an essential step in thoracic and abdominal tumor treatment. In current practice, patients with tumor motion may be re...

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Veröffentlicht in:	Scientific reports 2019-10, Vol.9 (1), p.14868-11, Article 14868
Hauptverfasser:	Lin, Hui, Zou, Wei, Li, Taoran, Feigenberg, Steven J., Teo, Boon-Keng K., Dong, Lei
Format:	Artikel
Sprache:	eng
Schlagworte:	639/705/531 692/4028/67/1612 Artificial intelligence Compression Computed tomography Disease management Electronic medical records Gamma Rays - therapeutic use Humanities and Social Sciences Humans Image Interpretation, Computer-Assisted Learning algorithms Lung - diagnostic imaging Lung - pathology Lung - radiation effects Lung cancer Lung Neoplasms - diagnostic imaging Lung Neoplasms - pathology Lung Neoplasms - radiotherapy Machine learning Machine Learning - statistics & numerical data Movement - physiology multidisciplinary Patients Prediction models Radiation Dosage Radiation therapy Radiotherapy, Computer-Assisted - methods Respiration Science Science (multidisciplinary) Thorax Tumors
Online-Zugang:	Volltext
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