Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy.

ABSTRACT

PURPOSE/OBJECTIVE(S) Precise segmentation of clinical target volumes (CTV) in breast cancer is indispensable for state-of-the art radiotherapy. Despite international guidelines, significant intra- and interobserver variability exists, negatively impacting treatment outcomes. The aim of this study is to evaluate the performance and efficiency of segmentation of CTVs in planning CT images of breast cancer patients using a 3D convolutional neural network (CNN) compared to the manual process. MATERIALS/METHODS: An expert radiation oncologist (RO) segmented all CTVs separately according to international guidelines in 150 breast cancer patients. This data was used to create, train and validate a 3D CNN. The network's performance was additionally evaluated in a test set of 20 patients. Primary endpoints are quantitative and qualitative analysis of the segmentation data generated by the CNN for each level specifically as well as for the total PTV to be irradiated. The secondary endpoint is the evaluation of time efficiency. RESULTS: In the test set, segmentation performance was best for the contralateral breast and the breast CTV and worst for Rotter's space and the internal mammary nodal (IMN) level. Analysis of impact on PTV resulted in non-significant over-segmentation of the primary PTV and significant under-segmentation of the nodal PTV, resulting in slight variations of overlap with OARs. Guideline consistency improved from 77.14% to 90.71% in favor of CNN segmentation while saving on average 24 minutes per patient with a median time of 35 minutes for pure manual segmentation. CONCLUSION: 3D CNN based delineation for breast cancer radiotherapy is feasible and performant, as scored by quantitative and qualitative metrics.