Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer

Abstract

Background and purpose: Deep learning-based models have been actively investigated for various aspects of radiotherapy. However, for cervical cancer, only a few studies dealing with the auto-segmentation of organs-at-risk (OARs) and clinical target volumes (CTVs) exist. This study aimed to train a deep learning-based auto-segmentation model for OAR/CTVs for patients with cervical cancer undergoing radiotherapy and to evaluate the model's feasibility and efficacy with not only geometric indices but also comprehensive clinical evaluation.

Materials and methods: A total of 180 abdominopelvic computed tomography images were included (training set, 165; validation set, 15). Geometric indices such as the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD) were analyzed. A Turing test was performed and physicians from other institutions were asked to delineate contours with and without using auto-segmented contours to assess inter-physician heterogeneity and contouring time.

Results: The correlation between the manual and auto-segmented contours was acceptable for the anorectum, bladder, spinal cord, cauda equina, right and left femoral heads, bowel bag, uterocervix, liver, and left and right kidneys (DSC greater than 0.80). The stomach and duodenum showed DSCs of 0.67 and 0.73, respectively. CTVs showed DSCs between 0.75 and 0.80. Turing test results were favorable for most OARs and CTVs. No auto-segmented contours had large, obvious errors. The median overall satisfaction score of the participating physicians was 7 out of 10. Auto-segmentation reduced heterogeneity and shortened contouring time by 30 min among radiation oncologists from different institutions. Most participants favored the auto-contouring system.

Conclusion: The proposed deep learning-based auto-segmentation model may be an efficient tool for patients with cervical cancer undergoing radiotherapy. Although the current model may not completely replace humans, it can serve as a useful and efficient tool in real-world clinics.