Evaluation of auto-segmentation for brachytherapy of postoperative cervical cancer using deep learning-based workflow

Abstract

Objective: The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures derived from Deep learning (DL) based auto-segmentation compared with standard manual delineation for postoperative cervical cancer. Methods: We introduced a convolutional neural networks (CNN) which was developed and presented for auto-segmentation in cervical cancer radiotherapy. The dataset of 60 patients received BT of postoperative cervical cancer was used to train and test this model for delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs). Dice similarity coefficient (DSC), 95% hausdorff distance (95%HD), jaccard coefficient (JC) and dose-volume index (DVI) were used to evaluate the accuracy. The correlation between geometric metrics and dosimetric difference was performed by Spearman’s correlation analysis. The radiation oncologists scored the auto-segmented contours by rating the lever of satisfaction (no edits, minor edits, major edits). Results: The mean DSC values of DL based model were 0.87, 0.94, 0.86, 0.79 and 0.92 for HRCTV, bladder, rectum, sigmoid and small intestine, respectively. The Bland-Altman test obtained dose agreement for HRCTV_D90%, HRCTV_Dmean, bladder_D2cc, sigmoid_D2cc and small intestine_D2cc. Wilcoxon’s signed-rank test indicated significant dosimetric differences in bladder_D0.1cc, rectum_D0.1cc and rectum_D2cc (P<0.05). A strong correlation between HRCTV_D90% with its DSC (R=-0.842, P=0.002) and JC (R=-0.818, P=0.004) were found in Spearman’s correlation analysis. From the physician review, 80% of HRCTVs and 72.5% of OARs in the test dataset were shown satisfaction (no edits). Conclusion: The proposed DL based model achieved a satisfied agreement between the auto-segmented and manually defined contours of HRCTV and OARs, although the clinical acceptance of small volume dose of OARs around the target was a concern. DL based auto-segmentation was an essential component in cervical cancer workflow which would generate the accurate contouring.