Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer

Xue Bai; Ze Liu; Jie Zhang; Shengye Wang; Qing Hou; Guoping Shan; Ming Chen; Binbing Wang

doi:10.1177/00368504211038162

Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer

Sci Prog. 2021 Jul-Sep;104(3):368504211038162. doi: 10.1177/00368504211038162.

Authors

Xue Bai^{1

2}, Ze Liu³, Jie Zhang², Shengye Wang², Qing Hou¹, Guoping Shan², Ming Chen², Binbing Wang²

Affiliations

¹ Key Lab of Radiation Physics and Technology, Ministry of Education, Institute of Nuclear Science and Technology, 12530Sichuan University, China.
² Department of Radiation Physics, 89680Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), China.
³ School of Electronic Information and Electronical Engineering, 74707Chengdu University, China.

Abstract

Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk (p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model (p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.

Keywords: Dose prediction; breast cancer; fully convolutional network; radiotherapy; treatment planning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Breast Neoplasms* / diagnostic imaging
Breast Neoplasms* / radiotherapy
Female
Humans
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted / methods
Radiotherapy, Intensity-Modulated* / methods
Unilateral Breast Neoplasms*