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Deep learning-based auto-contouring for organs at risk in three-dimensional image-guided brachytherapy for cervical
Kirika Takahashi1, Ken Takeda1,2, Hisamichi Takagi1
1Department of Therapeutic Radiology, Health Sciences, Tohoku University Graduate School of Medicine, Sendai, Japan.
Background:
Automatic contouring can reduce the time required for delineating organs at risk (OARs) in brachytherapy planning and minimize interobserver variability.
Purpose:
This study aimed to develop and evaluate a deep learning-based automatic contouring model for OARs in three-dimensional image-guided brachytherapy (3D-IGBT) for cervical and endometrial cancer, including cases with interstitial needles.
Methods:
The dataset comprised 100 patients (140 cases) with cervical or endometrial cancer who underwent 3D-IGBT. Interstitial needles were used in 74 cases. The nnU-Net model was trained (80 patients, 80 cases) and tested (20 patients, 60 cases). The OARs considered were the bladder, small bowel, rectum, and sigmoid. Ground truth (GT) contours were manually delineated by radiation oncologists and medical physicists. Evaluation included measuring inference time and assessing geometric agreement using dice similarity coefficient (DSC), surface DSC (sDSC), Hausdorff distance (HD), and 95th percentile HD (95HD). These metrics were also calculated for a combined structure of the rectum and sigmoid (Rec+Sig). Furthermore, D2cc was calculated based on both the GT and predicted contours using the clinical dose distribution, and the difference between them (ΔD2cc) was evaluated. Differences in accuracy with or without interstitial needles were compared using Welch's t-test (significance level: p < 0.05).
Results:
Mean processing time was 30.3 s per case. Mean DSC values for the bladder, small bowel, rectum, sigmoid, and Rec+Sig were 0.96, 0.79, 0.83, 0.76, and 0.87, respectively. Mean 95HD values (mm) were 4.01, 18.8, 13.6, 25.5, and 17.8, respectively; ΔD2cc values (Gy) were 0.17, 0.53, 0.014, -0.073, and -0.045, respectively. No significant accuracy differences related to interstitial needles were observed for any of the OARs.
Conclusions:
The proposed deep learning model demonstrated potential for application in cases involving interstitial needles and may contribute to improving the efficiency of the treatment planning workflow.
