Prospective Analysis of a Deep Learning Auto-Contouring Model for Definitive Radiation of Localized Prostate Cancer

Abstract

<h3>Purpose/Objective(s)</h3> To analyze the prospective use of a deep learning auto contouring model for patients with localized prostate cancer with or without insertion of a radiopaque hydrogel spacer. <h3>Materials/Methods</h3> The model was initially trained with patients from an academic center. It was implemented for prospective clinical use at a satellite center by three experienced radiation oncologists. The model auto contours prostate, proximal seminal vesicles (SVs), bladder, rectum, femurs, penile bulb and spacer (when existing). The auto contours (ACs) were reviewed and edited by treating physician. The ACs were compared to the approved planning contours (PCs) for the initial 23 patients to evaluate the extent of physician editing required. Each AC was compared to the final PC by one physician using a qualitative 4-point system: 1 (minor discrepancy with significant efficiency gain), 2 (moderate discrepancy with substantial gain), 3 (significant discrepancy with meaningful gain), and 4 (rejected for gross error or no efficiency gain). A composite score was calculated by averaging the score of each structure. A Turing test was performed to see if the physician can differentiate AC vs. PC. Quantitatively, the geometric differences between each AC and PC were analyzed using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). <h3>Results</h3> As shown in Table 1, the mean score was ≤1.4 for all structures except for SVs, and 1.26 for the composite, indicating overall significant efficiency gain. In 97.8% of cases, the ACs led to an efficiency gain. While the passing rate of the Turing test was not high, the failure was frequently due to very minor differences. The DSC was close to 1 for femurs, bladder and spacer, and >0.9 for prostate and rectum. The MDA was <2 mm for all structures except SVs. All quantitative measures except for the SVs were within the tolerance recommended by AAPM TG 132 (DSC ≥ 0.8, MDA ≤ 3 mm). On average, treating physicians estimated 17 minutes were saved for each patient. <h3>Conclusion</h3> Overall, the model can accurately auto contour targets, organs, and spacer with significant efficiency gain. A majority of ACs qualitatively evaluated by a physician had only mild discrepancies. Quantitative analysis demonstrated ACs for most organs had only minor geometric differences, unlikely to result in any significant dose-volume impact.