AUTHOR=You Jinqiang , Wang Qingxin , Wang Ruoxi , An Qin , Wang Jing , Yuan Zhiyong , Wang Jun , Chen Haibin , Yan Ziye , Wei Jun , Wang Wei 

TITLE=Deep Learning-Aided Automatic Contouring of Clinical Target Volumes for Radiotherapy in Breast Cancer After Modified Radical Mastectomy

JOURNAL=Frontiers in Physics

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.754248

DOI=10.3389/fphy.2021.754248

ISSN=2296-424X

ABSTRACT=<p><bold>Purpose:</bold> The aim of this study is to develop a practicable automatic clinical target volume (CTV) delineation method for radiotherapy of breast cancer after modified radical mastectomy.</p><p><bold>Methods:</bold> Unlike breast conserving surgery, the radiotherapy CTV for modified radical mastectomy involves several regions, including CTV in the chest wall (<italic>CTV</italic><sub><italic>cw</italic></sub>), supra- and infra-clavicular region (<italic>CTV</italic><sub><italic>sc</italic></sub>), and internal mammary lymphatic region (<italic>CTV</italic><sub><italic>im</italic></sub>). For accurate and efficient segmentation of the CTVs in radiotherapy of breast cancer after modified radical mastectomy, a multi-scale convolutional neural network with an orientation attention mechanism is proposed to capture the corresponding features in different perception fields. A channel-specific local Dice loss, alongside several data augmentation methods, is also designed specifically to stabilize the model training and improve the generalization performance of the model. The segmentation performance is quantitatively evaluated by statistical metrics and qualitatively evaluated by clinicians in terms of consistency and time efficiency.</p><p><bold>Results:</bold> The proposed method is trained and evaluated on the self-collected dataset, which contains 110 computed tomography scans from patients with breast cancer who underwent modified mastectomy. The experimental results show that the proposed segmentation method achieved superior performance in terms of Dice similarity coefficient (DSC), Hausdorff distance (HD) and Average symmetric surface distance (ASSD) compared with baseline approaches.</p><p><bold>Conclusion:</bold> Both quantitative and qualitative evaluation results demonstrated that the specifically designed method is practical and effective in automatic contouring of CTVs for radiotherapy of breast cancer after modified radical mastectomy. Clinicians can significantly save time on manual delineation while obtaining contouring results with high consistency by employing this method.</p>