Qualitative and Quantitative Analysis of a Deep Learning Auto Contouring Model for Radiotherapy in Localized Prostate Cancer

Abstract

Purpose/Objective(s)To evaluate a convolutional neural network auto-contouring (AC) model created for treatment planning in patients receiving radiotherapy for localized prostate cancer after insertion of a radiopaque rectal spacer hydrogel.Materials/MethodsThe deep learning model, trained by 125 patients, auto contours target volumes (prostate and proximal seminal vesicles), OARs (bladder, rectum, femoral heads and penile bulb) and radiopaque rectal spacers. ACs were evaluated against MD manual contours (MCs) submitted for treatment planning. ACs were not available for MD review while creating MCs. Individual volumes as well as composite volumes (overall performance) were qualitatively evaluated by a radiation oncologist using a 1 (minor discrepancy, little to no dose-volume impact), 2 (moderate discrepancy or editable with substantial efficiency gain), 3 (significant discrepancy or editable with meaningful efficiency gain), and 4 (rejected due to gross error or editable without efficiency gain) scoring scale. Quantitative evaluation of geometric differences was performed with mean distance to agreement (MDA) and Dice Similarity Coefficient (DSC) for each volume.ResultsA total of 68 cases were evaluated by quantitative and qualitative metrics (Table 1). MCs were performed by 4 experienced MDs. Average composite score of 2.22 (SD 0.73) indicates substantial to meaningful efficiency gain in contouring process. Composite scores for 100% of cases evaluated indicate a meaningful efficiency gain in the contouring process, with 60% of composite scores indicating only minor to moderate discrepancy between contours. Prostate and SV AC average scores were above 2. Mean prostate MDA and DSC were 1.71 and 0.85, respectively, both within tolerance range recommended by AAPM TG 132.ConclusionThe model can accurately auto contour target volumes, OARs and spacer with meaningful to substantial efficiency gain, with a majority of target volumes qualitatively evaluated by a physician as having only mild to moderate discrepancies. Quantitative analysis shows contours for most OARs have only minor geometric differences unlikely to have significant dose-volume impacts. Further analysis with comparison of treatment plans generated from ACs and MCs is warranted. To evaluate a convolutional neural network auto-contouring (AC) model created for treatment planning in patients receiving radiotherapy for localized prostate cancer after insertion of a radiopaque rectal spacer hydrogel. The deep learning model, trained by 125 patients, auto contours target volumes (prostate and proximal seminal vesicles), OARs (bladder, rectum, femoral heads and penile bulb) and radiopaque rectal spacers. ACs were evaluated against MD manual contours (MCs) submitted for treatment planning. ACs were not available for MD review while creating MCs. Individual volumes as well as composite volumes (overall performance) were qualitatively evaluated by a radiation oncologist using a 1 (minor discrepancy, little to no dose-volume impact), 2 (moderate discrepancy or editable with substantial efficiency gain), 3 (significant discrepancy or editable with meaningful efficiency gain), and 4 (rejected due to gross error or editable without efficiency gain) scoring scale. Quantitative evaluation of geometric differences was performed with mean distance to agreement (MDA) and Dice Similarity Coefficient (DSC) for each volume. A total of 68 cases were evaluated by quantitative and qualitative metrics (Table 1). MCs were performed by 4 experienced MDs. Average composite score of 2.22 (SD 0.73) indicates substantial to meaningful efficiency gain in contouring process. Composite scores for 100% of cases evaluated indicate a meaningful efficiency gain in the contouring process, with 60% of composite scores indicating only minor to moderate discrepancy between contours. Prostate and SV AC average scores were above 2. Mean prostate MDA and DSC were 1.71 and 0.85, respectively, both within tolerance range recommended by AAPM TG 132. The model can accurately auto contour target volumes, OARs and spacer with meaningful to substantial efficiency gain, with a majority of target volumes qualitatively evaluated by a physician as having only mild to moderate discrepancies. Quantitative analysis shows contours for most OARs have only minor geometric differences unlikely to have significant dose-volume impacts. Further analysis with comparison of treatment plans generated from ACs and MCs is warranted.