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Title: WE-AB-209-05: Development of an Ultra-Fast High Quality Whole Breast Radiotherapy Treatment Planning System

Abstract

Purpose: To enable near-real-time (<20sec) and interactive planning without compromising quality for whole breast RT treatment planning using tangential fields. Methods: Whole breast RT plans from 20 patients treated with single energy (SE, 6MV, 10 patients) or mixed energy (ME, 6/15MV, 10 patients) were randomly selected for model training. Additional 20 cases were used as validation cohort. The planning process for a new case consists of three fully automated steps:1. Energy Selection. A classification model automatically selects energy level. To build the energy selection model, principle component analysis (PCA) was applied to the digital reconstructed radiographs (DRRs) of training cases to extract anatomy-energy relationship.2. Fluence Estimation. Once energy is selected, a random forest (RF) model generates the initial fluence. This model summarizes the relationship between patient anatomy’s shape based features and the output fluence. 3. Fluence Fine-tuning. This step balances the overall dose contribution throughout the whole breast tissue by automatically selecting reference points and applying centrality correction. Fine-tuning works at beamlet-level until the dose distribution meets clinical objectives. Prior to finalization, physicians can also make patient-specific trade-offs between target coverage and high-dose volumes.The proposed method was validated by comparing auto-plans with manually generated clinical-plans using Wilcoxon Signed-Rank test. Results:more » In 19/20 cases the model suggested the same energy combination as clinical-plans. The target volume coverage V100% was 78.1±4.7% for auto-plans, and 79.3±4.8% for clinical-plans (p=0.12). Volumes receiving 105% Rx were 69.2±78.0cc for auto-plans compared to 83.9±87.2cc for clinical-plans (p=0.13). The mean V10Gy, V20Gy of the ipsilateral lung was 24.4±6.7%, 18.6±6.0% for auto plans and 24.6±6.7%, 18.9±6.1% for clinical-plans (p=0.04, <0.001). Total computational time for auto-plans was < 20s. Conclusion: We developed an automated method that generates breast radiotherapy plans with accurate energy selection, similar target volume coverage, reduced hotspot volumes, and significant reduction in planning time, allowing for near-real-time planning.« less

Authors:
 [1];  [2]; ; ; ; ; ; ;  [3];  [4]
  1. Duke University, Durham, NC (United States)
  2. Thomas Jefferson University, Philadelphia, PA (United States)
  3. Duke University Medical Center, Durham, NC (United States)
  4. University of North Carolina at Charlotte, Charlotte, NC (United States)
Publication Date:
OSTI Identifier:
22669469
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 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:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; MAMMARY GLANDS; PATIENTS; PLANNING; RADIATION DOSE DISTRIBUTIONS; RADIOTHERAPY

Citation Formats

Sheng, Y, Li, T, Yoo, S, Yin, F, Blitzblau, R, Horton, J, Palta, M, Hahn, C, Wu, Q, and Ge, Y. WE-AB-209-05: Development of an Ultra-Fast High Quality Whole Breast Radiotherapy Treatment Planning System. United States: N. p., 2016. Web. doi:10.1118/1.4957774.
Sheng, Y, Li, T, Yoo, S, Yin, F, Blitzblau, R, Horton, J, Palta, M, Hahn, C, Wu, Q, & Ge, Y. WE-AB-209-05: Development of an Ultra-Fast High Quality Whole Breast Radiotherapy Treatment Planning System. United States. doi:10.1118/1.4957774.
Sheng, Y, Li, T, Yoo, S, Yin, F, Blitzblau, R, Horton, J, Palta, M, Hahn, C, Wu, Q, and Ge, Y. Wed . "WE-AB-209-05: Development of an Ultra-Fast High Quality Whole Breast Radiotherapy Treatment Planning System". United States. doi:10.1118/1.4957774.
@article{osti_22669469,
title = {WE-AB-209-05: Development of an Ultra-Fast High Quality Whole Breast Radiotherapy Treatment Planning System},
author = {Sheng, Y and Li, T and Yoo, S and Yin, F and Blitzblau, R and Horton, J and Palta, M and Hahn, C and Wu, Q and Ge, Y},
abstractNote = {Purpose: To enable near-real-time (<20sec) and interactive planning without compromising quality for whole breast RT treatment planning using tangential fields. Methods: Whole breast RT plans from 20 patients treated with single energy (SE, 6MV, 10 patients) or mixed energy (ME, 6/15MV, 10 patients) were randomly selected for model training. Additional 20 cases were used as validation cohort. The planning process for a new case consists of three fully automated steps:1. Energy Selection. A classification model automatically selects energy level. To build the energy selection model, principle component analysis (PCA) was applied to the digital reconstructed radiographs (DRRs) of training cases to extract anatomy-energy relationship.2. Fluence Estimation. Once energy is selected, a random forest (RF) model generates the initial fluence. This model summarizes the relationship between patient anatomy’s shape based features and the output fluence. 3. Fluence Fine-tuning. This step balances the overall dose contribution throughout the whole breast tissue by automatically selecting reference points and applying centrality correction. Fine-tuning works at beamlet-level until the dose distribution meets clinical objectives. Prior to finalization, physicians can also make patient-specific trade-offs between target coverage and high-dose volumes.The proposed method was validated by comparing auto-plans with manually generated clinical-plans using Wilcoxon Signed-Rank test. Results: In 19/20 cases the model suggested the same energy combination as clinical-plans. The target volume coverage V100% was 78.1±4.7% for auto-plans, and 79.3±4.8% for clinical-plans (p=0.12). Volumes receiving 105% Rx were 69.2±78.0cc for auto-plans compared to 83.9±87.2cc for clinical-plans (p=0.13). The mean V10Gy, V20Gy of the ipsilateral lung was 24.4±6.7%, 18.6±6.0% for auto plans and 24.6±6.7%, 18.9±6.1% for clinical-plans (p=0.04, <0.001). Total computational time for auto-plans was < 20s. Conclusion: We developed an automated method that generates breast radiotherapy plans with accurate energy selection, similar target volume coverage, reduced hotspot volumes, and significant reduction in planning time, allowing for near-real-time planning.},
doi = {10.1118/1.4957774},
journal = {Medical Physics},
issn = {0094-2405},
number = 6,
volume = 43,
place = {United States},
year = {2016},
month = {6}
}