A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer

Along Chen; Fei Chen; Xiaofang Li; Yazhi Zhang; Li Chen; Lixin Chen; Jinhan Zhu

doi:10.3389/fonc.2022.908903

A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer

Front Oncol. 2022 Jun 1:12:908903. doi: 10.3389/fonc.2022.908903. eCollection 2022.

Authors

Along Chen¹, Fei Chen², Xiaofang Li³, Yazhi Zhang⁴, Li Chen¹, Lixin Chen¹, Jinhan Zhu¹

Affiliations

¹ Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
² School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China.
³ Department of Radiation Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
⁴ Department of Oncology and Hematology, The Six People's Hospital of Huizhou City, Huiyang Hospital Affiliated to Southern Medical University, Huizhou, China.

Abstract

Purpose: To investigate the dosimetric impact on target volumes and organs at risk (OARs) when unmodified auto-segmented OAR contours are directly used in the design of treatment plans.

Materials and methods: A total of 127 patients with cervical cancer were collected for retrospective analysis, including 105 patients in the training set and 22 patients in the testing set. The 3D U-net architecture was used for model training and auto-segmentation of nine types of organs at risk. The auto-segmented and manually segmented organ contours were used for treatment plan optimization to obtain the AS-VMAT (automatic segmentations VMAT) plan and the MS-VMAT (manual segmentations VMAT) plan, respectively. Geometric accuracy between the manual and predicted contours were evaluated using the Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), and Hausdorff distance (HD). The dose volume histogram (DVH) and the gamma passing rate were used to identify the dose differences between the AS-VMAT plan and the MS-VMAT plan.

Results: Average DSC, MDA and HD₉₅ across all OARs were 0.82-0.96, 0.45-3.21 mm, and 2.30-17.31 mm on the testing set, respectively. The D_99% in the rectum and the Dmean in the spinal cord were 6.04 Gy (P = 0.037) and 0.54 Gy (P = 0.026) higher, respectively, in the AS-VMAT plans than in the MS-VMAT plans. The V₂₀, V₃₀, and V₄₀ in the rectum increased by 1.35% (P = 0.027), 1.73% (P = 0.021), and 1.96% (P = 0.008), respectively, whereas the V₁₀ in the spinal cord increased by 1.93% (P = 0.011). The differences in other dosimetry parameters were not statistically significant. The gamma passing rates in the clinical target volume (CTV) were 92.72% and 98.77%, respectively, using the 2%/2 mm and 3%/3 mm criteria, which satisfied the clinical requirements.

Conclusions: The dose distributions of target volumes were unaffected when auto-segmented organ contours were used in the design of treatment plans, whereas the impact of automated segmentation on the doses to OARs was complicated. We suggest that the auto-segmented contours of tissues in close proximity to the target volume need to be carefully checked and corrected when necessary.

Keywords: automatic segmentation; cervical cancer; deep learning; dosimetric differences; geometric accuracy.