Dosimetric Impact of Deep Learning-Based CT Auto-Segmentation on IMRT Treatment Planning for Prostate Cancer

Abstract

Background The evaluation of the automatic segmentation algorithms is commonly performed using geometric metrics, yet an evaluation based on dosimetric parameters might be more relevant in clinical practice but is still lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in intensity-modulated radiation therapy (IMRT) for prostate patients for the first time. Methods A database of 69 computed tomography (CT) images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, gamma index and DVH parameters, has been calculated. Results 3D U-Net based segmentation achieved a DSC of 0.87(0.03) for prostate, 0.97(0.01) for bladder and 0.89(0.04) for rectum. The mean and 95% HD were below 1.6(0.4) and below 5(4) mm, respectively. The DVH parameters V 60/65/70 Gy for the bladder and V 50/65/70 Gy for the rectum showed agreement between dose distributions within ±5% and ±2%, respectively. The DVH parameters for prostate and prostate+3mm margin (surrogate clinical target volume) showed good target coverage for the 3D U-Net segmentation with the exception of one case. The average gamma pass-rate was 85\%. A comparison between geometric and dosimetric metrics showed no strong statistically significant correlation between these metrics. Conclusions The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. The study highlighted the importance of dosimetric evaluation on top of standard geometric parameters and concluded that the automatic segmentation is sufficiently accurate to assist the physicians in manually contouring organs in CT images of the male pelvic region, which is an important step towards a fully automated workflow in IMRT.

Route Planning of Health Care Tourism Based on Computer Deep Learning

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