Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models

Abstract

Purpose: To develop an automatic treatment planning system for whole breast radiation therapy (WBRT) based on two intensity-modulated tangential fields, enabling near-real-time planning. Methods and Materials: A total of 40 WBRT plans from a single institution were included in this study under IRB approval. Twenty WBRT plans, 10 with single energy (SE, 6MV) and 10 with mixed energy (ME, 6/15MV), were randomly selected as training dataset to develop the methodology for automatic planning. The rest 10 SE cases and 10 ME cases served as validation. The auto-planning process consists of three steps. First, an energy prediction model was developed to automate energy selection. This model establishes an anatomy-energy relationship based on principle component analysis (PCA) of the gray level histograms from training cases' digitally reconstructed radiographs (DRRs). Second, a random forest (RF) model generates an initial fluence map using the selected energies. Third, the balance of overall dose contribution throughout the breast tissue is realized by automatically selecting anchor points and applying centrality correction. The proposed method was tested on the validation dataset. Non-parametric equivalence test was performed for plan quality metrics using one-sided Wilcoxon Signed-Rank test. Results: For validation, the auto-planning system suggested same energy choices as clinical-plans in 19 out of 20 cases. The mean (standard deviation, SD) of percent target volume covered by 100% prescription dose was 82.5% (4.2%) for auto-plans, and 79.3% (4.8%) for clinical-plans (p > 0.999). Mean (SD) volume receiving 105% Rx were 95.2 cc (90.7 cc) for auto-plans and 83.9 cc (87.2 cc) for clinical-plans (p = 0.108). Optimization time for auto-plan was <20 s while clinical manual planning takes between 30 min and 4 h. Conclusions: We developed an automatic treatment planning system that generates WBRT plans with optimal energy selection, clinically comparable plan quality, and significant reduction in planning time, allowing for near-real-time planning.

Keywords: auto planning; breast cancer; electronic compensation; machine learning; random forest; whole breast radiation therapy.

Figure 1

Flowchart of the proposed automatic planning workflow (Left) and the current clinical workflow (Right). The automatic planning workflow mimics the workflow of manual planning while providing automation tools to streamline the process.

Figure 2

(A) DRR intensity histogram for single energy cases (red) and mixed energy cases (green); (B) PC1 and PC2 score of single energy cases (red) and mixed energy cases (green) for training dataset (solid square) and validation dataset (circle), PC1 = 0 is shown as black line.

Figure 3

Isodose comparison between ECOMP clinical-plan (top row) and ECOMP auto-plan (bottom row) for one large breast patient (left three columns) and one small breast patient (right three columns). Yellow isodose line (IDL) denotes 100% and pink IDL denotes 105%.

Figure 4

Boxplot side-by-side comparison of dose metrics between auto-plan (Left) and clinical-plan (Right). Rectangular box denotes interquartile range. Thick line in the box denotes median. Circular dot denotes outlier data point, which is 1.5 times interquartile range above upper quartile.

Figure 5

(a) DVH comparison between clinical-plan (blue) and auto-plan (orange). It shows breast target coverage distribution for bins in dose range of 95–113%. (b) DVH difference (auto-plan minus clinical-plan) in target coverage for bins in dose range of 95–113%. Positive difference in dose range of 95–100% indicates better breast target coverage. Negative difference in dose range of 100–113% indicates better hot spot volume control.