Deformable Image Registration and Image Segmentation for Radiation Therapy

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 1280

Special Issue Editors

Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Interests: deformable image registration; image segmentation; quantitative imaging biomarkers for MR-guided adaptive radiation therapy
Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Interests: interaction of human modeling and radiation therapy; biomechanical model-based deformable registration; dose reconstruction; correlative pathology
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Special Issue Information

Dear Colleagues,

Deformable image registration and image segmentation are two imperative techniques that enable current state-of-the-art radiation therapy with improved accuracy and precision in treatment planning. In radiation therapy, deformable image registration is commonly used for contour propagation and dose accumulation to power up efficient adaptive radiation therapy. This allows for more precise targeting of the tumor and better sparing of normal tissue in radiation treatment. On the other hand, image segmentation plays a crucial role in treatment planning by automatically identifying and outlining the tumor and surrounding organs at risk for modern radiation treatment planning techniques. Automatic image segmentation also facilitates the management of radiation-induced toxicity and the evaluation of potential risks and benefits of different treatment options. Recent advances in machine learning and deep learning techniques have significantly improved the accuracy and speed of deformable image registration and segmentation in radiation oncology. These methods have been shown to be more robust and consistent than traditional methods and have the potential to improve radiation treatment outcomes. This Special Issue aims to disseminate recent state-of-the-art artificial-intelligence-based deformable image registration and image segmentation techniques in the application of radiation therapy. 

Dr. Jinzhong Yang
Dr. Kristy K. Brock
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • deep learning
  • deformable image registration
  • image segmentation
  • image-guided radiation therapy
  • adaptive radiation therapy

Published Papers (1 paper)

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Research

16 pages, 1939 KiB  
Article
Evaluating the Necessity of Adaptive RT and the Role of Deformable Image Registration in Lung Cancer with Different Pathologic Classifications
by Woo Chul Kim, Yong Kyun Won, Sang Mi Lee, Nam Hun Heo, Seung-Gu Yeo, Ah Ram Chang, Sun Hyun Bae, Jae Sik Kim, Ik Dong Yoo, Sun-pyo Hong, Chul Kee Min, In Young Jo and Eun Seog Kim
Diagnostics 2023, 13(18), 2956; https://doi.org/10.3390/diagnostics13182956 - 15 Sep 2023
Viewed by 681
Abstract
Background: This study aimed to analyze differential radiotherapy (RT) responses according to the pathological type of lung cancer to see the possibility of applying adaptive radiotherapy (ART). Methods: ART planning with resampled-computed tomography was conducted for a total of 30 patients (20 non-small-cell [...] Read more.
Background: This study aimed to analyze differential radiotherapy (RT) responses according to the pathological type of lung cancer to see the possibility of applying adaptive radiotherapy (ART). Methods: ART planning with resampled-computed tomography was conducted for a total of 30 patients (20 non-small-cell lung cancer patients and 10 small-cell lung cancer patients) using a deformable image registration technique to reveal gross tumor volume (GTV) changes according to the duration of RT. Results: The small-cell lung cancer group demonstrated an average GTV reduction of 20.95% after the first week of initial treatment (p = 0.001), whereas the adenocarcinoma and squamous cell carcinoma groups showed an average volume reduction of 20.47% (p = 0.015) and 12.68% in the second week. The application of ART according to the timing of GTV reduction has been shown to affect changes in radiation dose irradiated to normal tissues. This suggests that ART applications may have to be different depending on pathological differences in lung cancer. Conclusion: Through these results, the present study proposes the possibility of personalized treatment options for individual patients by individualizing ART based on specific radiation responses by pathologic types of lung cancer. Full article
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