Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models.

Yang Sheng,Janet K Horton,Sua Yoo,Taoran Li,Fang-Fang Yin,Rachel Blitzblau,Q Jackie Wu,Yaorong Ge

doi:10.3389/fonc.2019.00750

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.

Highlights

Breast cancer is the most common non-cutaneous cancer type among females
Many patients opt for a breast conserving surgery and the whole breast radiation therapy (WBRT) is routinely delivered in the post-operative setting to reduce the risk of locoregional recurrence
We aim to develop an automatic planning system starting with the energy selection, followed by the fluence estimation model using random forest and concluding with a fluence fine tuning module that would enable near-real-time and interactive planning while providing similar plan quality as experienced human planners

Summary

Introduction

Breast cancer is the most common non-cutaneous cancer type among females. There are 268,670 estimated new female breast cancer cases in 2018 [1] with additional yearly estimated breast ductal carcinoma in situ (DCIS) occurrence of 60,290 [2]. WBRT refers to multiple treatment techniques [3,4,5,6] including the traditional 3D treatment [7,8,9,10] using the physical wedge, the field-in-field (FiF) delivery [11,12,13], the intensity modulated radiation therapy (IMRT) [14, 15], and the volumetric modulated arc therapy (VMAT) [16,17,18,19,20,21]. Since the tunable parameter (wedge angle) for the 3D technique is limited, inhomogeneous dose distribution is often observed within the irradiated volume. The FiF approach offers the additional benefit of multiple static segments to control the delivered fluence as compared to 3D treatment with 2–4 segments. The FiF treatment planning does not invoke the inverse IMRT optimizer and is a forward planning process

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