Contour‐based lung dose prediction for breast proton therapy

Abstract

PurposeThis study evaluates the feasibility of lung dose prediction based on target contour and patient anatomy for breast patients treated with proton therapy.MethodsFifty‐two randomly selected patients were included in the cohort, who were treated to 50.4–66.4 Gy(RBE) to the left (36), right (15), or bilateral (1) breast with uniform scanning (32) or pencil beam scanning (20). Anterior‐oblique beams were used for each patient. The prescription doses were all scaled to 50.4 Gy(RBE) for the current analysis. Isotropic expansions of the planning target volume of various margins m were retrospectively generated and compared with isodose volumes in the ipsilateral lung. The fractional volume V of each expansion contour within the ipsilateral lung was compared with dose–volume data of clinical plans to establish the relationship between the margin m and dose D for the ipsilateral lung such that V D = V(m). This relationship enables prediction of dose–volume VD from V(m), which could be derived from contours before any plan is generated, providing a goal of plan quality. Lung V 20 Gy( RBE ) and V 5 Gy( RBE ) were considered for this pilot study, while the results could be generalized to other dose levels and/or other organs.ResultsThe actual V 20 Gy( RBE ) ranged from 6% to 23%. No statistically significant difference in V 20 Gy( RBE ) was found between breast irradiation and chest wall irradiation (P = 0.8) or between left‐side and right‐side treatment (P = 0.9). It was found that V(1.1 cm) predicted V 20 Gy( RBE ) to within 5% root‐mean‐square deviation (RMSD) and V(2.2 cm) predicted V 5 Gy( RBE ) to within 6% RMSD.ConclusionA contour‐based model was established to predict dose to ipsilateral lung in breast treatment. Clinically relevant accuracy was demonstrated. This model facilitates dose prediction before treatment planning. It could serve as a guide toward realistic clinical goals in the planning stage.

Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network

“…By leveraging the power of deep CNN (LeCun et al 1989, Krizhevsky et al 2012, promising gains have also been witnessed in radiation therapy , Zhen et al 2017, Liu et al 2018, Meyer et al 2018, Tomori et al 2018, Ibragimov et al 2018a, 2018b. Recently, contours-based methods (Nguyen et al 2017, Zeng et al 2018 have been proposed to predict the dose distribution based on the correlation between organ contours and the dose values. The dose prediction method employed U-net to predict dose distribution for prostate IMRT patients (Nguyen et al 2017).…”