Clinical application and evaluation of automatic segmentation model based on deep learning for breast cancer radiotherapy

Abstract

Objective In this study, the deep learning algorithm and the commercial planning system were integrated to establish and validate an automatic segmentation platform for clinical target volume (CTV) and organs at risk (OARs) in breast cancer patients. Methods A total of 400 patients with left and right breast cancer receiving radiotherapy after breast-conserving surgery in Cancer Hospital CAMS were enrolled in this study. A deep residual convolutional neural network was used to train CTV and OARs segmentation models. An end-to-end deep learning-based automatic segmentation platform (DLAS) was established. The accuracy of the DLAS platform delineation was verified using 42 left breast cancer and 40 right breast cancer patients. The overall Dice Similarity Coefficient (DSC) and the average Hausdorff Distance (AHD) were calculated. The relationship between the relative layer position and the DSC value of each layer (DSC_s) was calculated and analyzed layer-by-layer. Results The mean overall DSC and AHD of global CTV in left/right breast cancer patients were 0.87/0.88 and 9.38/8.71 mm. The average overall DSC and AHD range for all OARs in left/right breast cancer patients were ranged from 0.86 to 0.97 and 0.89 to 9.38 mm. The layer-by-layer analysis of CTV and OARs reached 0.90 or above, indicating that the doctors were only required to make slight or no modification, and the DSC_s ≥ 0.9 of CTV automatic delineation accounted for approximately 44.7% of the layers. The automatic delineation range for OARs was 50.9%-89.6%. For DSC_s< 0.7, the DSC_s values of CTV and the regions of interest other than the spinal cord were significantly decreased in the boundary regions on both sides (layer positions 0-0.2, and 0.8-1.0), and the level of decrease toward the edge was more pronounced. The spinal cord was delineated in a full-scale manner, and no significant decrease in DSC_s was observed in a particular area. Conclusions The end-to-end automatic segmentation platform based on deep learning can integrate the breast cancer segmentation model and achieve excellent automatic segmentation effect. In the boundary areas on both sides of the superior and inferior directions, the consistency of the delineation decreases more obviously, which needs to be further improved. Key words: Automatic segmentation; Deep learning; Breast neoplasm/radiotherapy