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Title: SU-E-T-527: Prior Knowledge Guided TomoTherapy Treatment Planning

Abstract

Purpose: The quality and efficiency of radiotherapy treatment planning are highly planer dependent. Previously we have developed a statistical model to correlate anatomical features with dosimetry features of head and neck Tomotherapy treatment. The model enables us to predict the best achievable dosimetry for individual patient prior to treatment planning. The purpose of this work is to study if the prediction model can facilitate the treatment planning in both the efficiency and dosimetric quality. Methods: The anatomy-dosimetry correlation model was used to calculate the expected DVH for nine patients formerly treated. In Group A (3 patients), the model prediction agreed with the clinic plan; in Group B (3 patients), the model predicted lower larynx mean dose than the clinic plan; in Group C (3 patients), the model suggested the brainstem could be further spared. Guided by the prior knowledge, we re-planned all 9 cases. The number of interactions during the optimization process and dosimetric endpoints between the original clinical plan and model-guided re-plan were compared. Results: For Group A, the difference of target coverage and organs-at-risk sparing is insignificant (p>0.05) between the replan and the clinical plan. For Group B, the clinical plan larynx median dose is 49.4±4.7 Gy, whilemore » the prediction suggesting 40.0±6.2 Gy (p<0.05). The re-plan achieved 41.5±6.6 Gy, with similar dose on other structures as clinical plan. For Group C, the clinical plan brainstem maximum dose is 44.7±5.5 Gy. The model predicted lower value 32.2±3.8 Gy (p<0.05). The re-plans reduced brainstem maximum dose to 31.8±4.1 Gy without affecting the dosimetry of other structures. In the replanning of the 9 cases, the times operator interacted with TPS are reduced on average about 50% compared to the clinical plan. Conclusion: We have demonstrated that the prior expert knowledge embedded model improved the efficiency and quality of Tomotherapy treatment planning.« less

Authors:
 [1]; ;  [2];  [3];  [4];  [5]
  1. UniversityNorth Carolina, Chapel Hill, NC (United States)
  2. Duke University Medical Center, Durham, NC (United States)
  3. Univ. of North Carolina at Chapel Hill, Chapel Hill, NC (United States)
  4. University of North Carolina, Chapel Hill, NC (United States)
  5. UNC School of Medicine, Chapel Hill, NC (United States)
Publication Date:
OSTI Identifier:
22369654
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 41; Journal Issue: 6; Other Information: (c) 2014 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; 60 APPLIED LIFE SCIENCES; ANATOMY; COMPUTERIZED TOMOGRAPHY; CT-GUIDED RADIOTHERAPY; DOSIMETRY; HEAD; LARYNX; NECK; OPTIMIZATION; ORGANS; PATIENTS; PLANNING

Citation Formats

Lian, J, Yuan, L, Wu, Q, Zhu, X, Chera, B, and Chang, S. SU-E-T-527: Prior Knowledge Guided TomoTherapy Treatment Planning. United States: N. p., 2014. Web. doi:10.1118/1.4888861.
Lian, J, Yuan, L, Wu, Q, Zhu, X, Chera, B, & Chang, S. SU-E-T-527: Prior Knowledge Guided TomoTherapy Treatment Planning. United States. https://doi.org/10.1118/1.4888861
Lian, J, Yuan, L, Wu, Q, Zhu, X, Chera, B, and Chang, S. 2014. "SU-E-T-527: Prior Knowledge Guided TomoTherapy Treatment Planning". United States. https://doi.org/10.1118/1.4888861.
@article{osti_22369654,
title = {SU-E-T-527: Prior Knowledge Guided TomoTherapy Treatment Planning},
author = {Lian, J and Yuan, L and Wu, Q and Zhu, X and Chera, B and Chang, S},
abstractNote = {Purpose: The quality and efficiency of radiotherapy treatment planning are highly planer dependent. Previously we have developed a statistical model to correlate anatomical features with dosimetry features of head and neck Tomotherapy treatment. The model enables us to predict the best achievable dosimetry for individual patient prior to treatment planning. The purpose of this work is to study if the prediction model can facilitate the treatment planning in both the efficiency and dosimetric quality. Methods: The anatomy-dosimetry correlation model was used to calculate the expected DVH for nine patients formerly treated. In Group A (3 patients), the model prediction agreed with the clinic plan; in Group B (3 patients), the model predicted lower larynx mean dose than the clinic plan; in Group C (3 patients), the model suggested the brainstem could be further spared. Guided by the prior knowledge, we re-planned all 9 cases. The number of interactions during the optimization process and dosimetric endpoints between the original clinical plan and model-guided re-plan were compared. Results: For Group A, the difference of target coverage and organs-at-risk sparing is insignificant (p>0.05) between the replan and the clinical plan. For Group B, the clinical plan larynx median dose is 49.4±4.7 Gy, while the prediction suggesting 40.0±6.2 Gy (p<0.05). The re-plan achieved 41.5±6.6 Gy, with similar dose on other structures as clinical plan. For Group C, the clinical plan brainstem maximum dose is 44.7±5.5 Gy. The model predicted lower value 32.2±3.8 Gy (p<0.05). The re-plans reduced brainstem maximum dose to 31.8±4.1 Gy without affecting the dosimetry of other structures. In the replanning of the 9 cases, the times operator interacted with TPS are reduced on average about 50% compared to the clinical plan. Conclusion: We have demonstrated that the prior expert knowledge embedded model improved the efficiency and quality of Tomotherapy treatment planning.},
doi = {10.1118/1.4888861},
url = {https://www.osti.gov/biblio/22369654}, journal = {Medical Physics},
issn = {0094-2405},
number = 6,
volume = 41,
place = {United States},
year = {Sun Jun 01 00:00:00 EDT 2014},
month = {Sun Jun 01 00:00:00 EDT 2014}
}