Cervical Cancer Treatment using AI
Kano, Yosuke Tokushima Prefecture Naruto Hospital
生島, 仁史 Tokushima University 徳島大学 教育研究者総覧 KAKEN研究者をさがす
佐々木, 幹治 Tokushima University 徳島大学 教育研究者総覧
芳賀, 昭弘 Tokushima University 徳島大学 教育研究者総覧
automatic tumor contour segmentation
diffusion-weighted imaging (DWI)
Dice similarity coefficient (DSC)
In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to delineate tumor contours. To reduce the burden on radiation oncologists, an automatic segmentation of the tumor contours would prove useful. To the best of our knowledge, automatic tumor contour segmentation has rarely been applied to cervical cancer treatment. In this study, diffusion-weighted images (DWI) of 98 patients with cervical cancer were acquired. We trained an automatic tumor contour segmentation model using 2D U-Net and 3D U-Net to investigate the possibility of applying such a model to clinical practice. A total of 98 cases were employed for the training, and they were then predicted by swapping the training and test images. To predict tumor contours, six prediction images were obtained after six training sessions for one case. The six images were then summed and binarized to output a final image through automatic contour segmentation. For the evaluation, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) was applied to analyze the difference between tumor contour delineation by radiation oncologists and the output image. The DSC ranged from 0.13 to 0.93 (median 0.83, mean 0.77). The cases with DSC <0.65 included tumors with a maximum diameter < 40 mm and heterogeneous intracavitary concentration due to necrosis. The HD ranged from 2.7 to 9.6 mm (median 4.7 mm). Thus, the study confirmed that the tumor contours of cervical cancer can be automatically segmented with high accuracy.
Journal of Radiation Research
Oxford University Press|The Japanese Radiation Research Society|Japanese Society for Radiation Oncology
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