Managing tumor changes during radiotherapy using a deep learning model.

Journal: Medical physics

Published Date: Aug 11, 2021

Abstract

PURPOSE: We propose a treatment planning framework that accounts for weekly lung tumor shrinkage using cone beam computed tomography (CBCT) images with a deep learning-based model.

Authors

Ruiqi Li

Department of Radiation Oncology, UT Health San Antonio, San Antonio, TX, USA.
Arkajyoti Roy

Department of Management Science and Statistics, University of Texas at San Antonio, San Antonio, Texas, USA.
Noah Bice

Department of Radiological Sciences, UT Health San Antonio, San Antonio, TX, 78229, USA.
Neil Kirby

Department of Radiological Sciences, UT Health San Antonio, San Antonio, TX, 78229, USA.
Mohamad Fakhreddine

Department of Radiological Sciences, UT Health San Antonio, San Antonio, TX, 78229, USA.
Niko Papanikolaou

Department of Radiological Sciences, UT Health San Antonio, San Antonio, TX, 78229, USA.

Keywords

Carcinoma, Non-Small-Cell Lung Cone-Beam Computed Tomography Deep Learning Humans Lung Neoplasms Male Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated

External Resources

View on PubMed Access via DOI PubMed (33959978)

Managing tumor changes during radiotherapy using a deep learning model.

Abstract

Authors

Keywords

External Resources

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