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WE‐AB‐209‐05: Development of an Ultra‐Fast High Quality Whole Breast Radiotherapy Treatment Planning System
Author(s) -
Sheng Y,
Li T,
Yoo S,
Yin F,
Blitzblau R,
Horton J,
Palta M,
Hahn C,
Ge Y,
Wu Q
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4957774
Subject(s) - wilcoxon signed rank test , radiation treatment planning , computer science , medical physics , energy (signal processing) , quality assurance , fluence , radiation therapy , nuclear medicine , process (computing) , artificial intelligence , medicine , statistics , mathematics , radiology , physics , optics , laser , external quality assessment , pathology , operating system , mann–whitney u test
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.