Clinical Validation and Treatment Plan Evaluation Based on Autodelineation of the Clinical Target Volume for Prostate Cancer Radiotherapy

Abstract

Purpose Clinical target volumes (CTVs) and organs at risk (OARs) could be autocontoured to save workload. This study aimed to assess a convolutional neural network for automatic and accurate CTV and OARs in prostate cancer, while comparing possible treatment plans based on autocontouring CTV to clinical treatment plans. Methods Computer tomography (CT) scans of 217 patients with locally advanced prostate cancer treated at our hospital were retrospectively collected and analyzed from January 2013 to January 2019. A deep learning-based method, CUNet, was used to delineate CTV and OARs. A training set of 195 CT scans and a test set of 28 CT scans were randomly chosen from the dataset. The mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD), and subjective evaluation were used to evaluate the performance of this strategy. Predetermined evaluation criteria were used to grade treatment plans, and percentage errors for clinical doses to the planned target volume (PTV) and OARs were calculated. Results The mean DSC and 95HD values of the defined CTVs were (0.84 ± 0.05) and (5.04 ± 2.15) mm, respectively. The average delineation time was < 15 s for each patient's CT scan. The overall positive rates for clinicians A and B were 53.15% versus 46.85%, and 54.05% versus 45.95%, respectively ( P > .05) when CTV outlines from CUNet were blindly chosen and compared with the ground truth (GT). Furthermore, 8 test patients were randomly chosen to design the predicted plan based on the autocontouring CTVs and OARs, demonstrating acceptable agreement with the clinical plan: average absolute dose differences in mean value of D2, D50, D98, Dmax, and Dmean for PTV were within 0.74%, and average absolute volume differences in mean value of V45 and V50 for OARs were within 3.4%. Conclusion Our results revealed that the CTVs and OARs for prostate cancer defined by CUNet were close to the GT. CUNet could halve the time spent by radiation oncologists in contouring, demonstrating the potential of the novel autocontouring method.