Assembling High-Quality Lymph Node Clinical Target Volumes for Cervical Cancer Radiotherapy Using a Deep Learning-Based Approach

Background: To explore an approach for accurate assembling high-quality lymph node clinical target volumes (CTV) on CT images in cervical cancer radiotherapy with the encoder-decoder 3D network. Methods: : CT images from 216 cases were involved from 2017-2020 in our center. 216 patients were divided into two cohorts, including 152 cases and 64 cases respectively. The first cohort with 152 cases whose para-aortic lymph node, common iliac, external iliac, internal iliac, obturator, presacral and groin nodal regions as sub-CTV were delineated manually. Then the 152 cases were randomly divided into training ( n=96 ), validation ( n=36 ) and test ( n=20 ) groups for training process. Each structure was individually trained and optimized through a deep learning model. An additional 64 cases with 6 different clinical conditions were taken as examples to verify the feasibility of CTV generation based on our model. Dice similarity coefficient(DSC) and hausdurff distance(HD) metrics were both used for quantitative evaluation. Results: : Comparing auto-segmentation results to ground truth, the mean DSC value/HD were 0.838/7.7mm, 0.853/4.7mm, 0.855/4.7mm, 0.844/4.7mm, 0.784/5.2mm, 0.826/4.8mm and 0.874/4.8mm for CTV_PAN, CTV_common iliac, CTV_internal iliac, CTV_external iliac, CTV_obturator, CTV_presacral and CTV_groin, respectively. The similarity comparison results of 6 different clinical situations were 0.877/4.4mm, 0.879/4.6mm, 0.881/4.2mm, 0.882/4.3mm, 0.872/6.0mm and 0.875/4.9mm for DSC value/ HD respectively. Conclusions: : We developed a deep learning-based approach to segmenting lymph node sub-regions automatically and assembling CTVs according to clinical needs with these sub-regions in cervical cancer radiotherapy. This work can be applied to improve the consistency and flexibility of high-quality CTV delineation, increase the efficiency of cervical cancer work process.