Abstract
Purpose: 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.Methods: Unlike breast conserving surgery, the radiotherapy CTV for modified radical mastectomy involves several regions, including CTV in the chest wall (CTVcw), supra- and infra-clavicular region (CTVsc), and internal mammary lymphatic region (CTVim). 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.Results: 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.Conclusion: 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.
Highlights
According to a report from the World Health Organization, breast cancer has overtaken lung cancer as the most prevalent cancer worldwide [1]
Modified radical mastectomy (MRM) is beneficial to patients, it presents a challenge to clinicians in contouring the clinical target volume (CTV) for postoperative radiotherapy because the corresponding CTVs involve several target areas with relatively complex anatomic structures compared with their counterparts in breastconserving surgery (BCS) and HS
Patients are segmented based on an atlas library, and the most anatomically similar will be selected as the target to be transformed into the same coordinate space as the input data
Summary
According to a report from the World Health Organization, breast cancer has overtaken lung cancer as the most prevalent cancer worldwide [1]. Modified radical mastectomy (MRM) is widely used in clinical practice for the treatment of breast cancer to ensure surgical efficacy while reducing surgical damage and improving the patient’s quality of life [2]. MRM has become a cornerstone of breast cancer treatment in China It involves excising only the mammary gland and clearing the axillary lymph nodes, while preserving the pectoralis major and minor muscles, thereby ensuring postoperative mobility and appearance. MRM is beneficial to patients, it presents a challenge to clinicians in contouring the clinical target volume (CTV) for postoperative radiotherapy because the corresponding CTVs involve several target areas with relatively complex anatomic structures compared with their counterparts in BCS and HS. There is an urgent need to develop an automatic CTV delineation method for radiotherapy of breast cancer after MRM to reduce the burden on clinicians while improving work efficiency and accuracy
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