Program Information
Development of An Ultra-Fast High Quality Whole Breast Radiotherapy Treatment Planning System
Y Sheng1*, T Li2 , S Yoo3 , F Yin3 , R Blitzblau3 , J Horton3 , M Palta3 , C Hahn3 , Y Ge4 , Q Wu3 , (1) Duke University, Durham, NC, (2) Thomas Jefferson University, Philadelphia, PA, (3) Duke University Medical Center, Durham, NC, (4) University of North Carolina at Charlotte, Charlotte, NC
Presentations
WE-AB-209-5 (Wednesday, August 3, 2016) 7:30 AM - 9:30 AM Room: 209
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.
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