Abstract
Aim: The study aimed to explore an approach for accurately assembling high-quality lymph node clinical target volumes (CTV) on CT images in cervical cancer radiotherapy with the encoder-decoder 3D network.
Methods: 216 cases of CT images treated at our center between 2017 and 2020 were included as a sample, which were divided into two cohorts, including 152 cases and 64 cases, respectively. Para-aortic lymph node, common iliac, external iliac, internal iliac, obturator, presacral, and groin nodal regions were delineated as sub-CTV manually in the cohort including 152 cases. Then, the 152 cases were randomly divided into training (96 cases), validation (36 cases), and test (20 cases) groups for the 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 Hausdorff distance (HD) metrics were both used for quantitative evaluation.
Results: Comparing auto-segmentation results to ground truth, the mean DSC value/HD was 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 six 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.
Conclusion: We have developed a deep learning-based approach to segmenting lymph node sub-regions automatically and assembling high-quality CTVs according to clinical needs in cervical cancer radiotherapy. This work can increase the efficiency of the process of cervical cancer detection and treatment.
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