Novel dosimetric validation of a commercial CT scanner based deep learning automated contour solution for prostate radiotherapy.

PURPOSE: OAR delineation accuracy influences: (i) a patient's optimised dose distribution (PD), (ii) the reported doses (RD) presented at approval, which represent plan quality. This study utilised a novel dosimetric validation methodology, comprehensively evaluating a new CT-scanner-based AI contouring solution in terms of PD and RD within an automated planning workflow.

METHODS: 20 prostate patients were selected to evaluate AI contouring for rectum, bladder, and proximal femurs. Five planning 'pipelines' were considered; three using AI contours with differing levels of manual editing (nominally none (AIStd ), minor editing in specific regions (AIMinEd ), and fully corrected (AIFullEd )). Remaining pipelines were manual delineations from two observers (MDOb1 , MDOb2 ). Automated radiotherapy plans were generated for each pipeline. Geometric and dosimetric agreement of contour sets AIStd , AIMinEd , AIFullEd and MDOb2 were evaluated against the reference set MDOb1 . Non-inferiority of AI pipelines was assessed, hypothesising that compared to MDOb1 , absolute deviations in metrics for AI contouring were no greater than that from MDOb2 .

RESULTS: Compared to MDOb1 , organ delineation time was reduced by 24.9 min (96 %), 21.4 min (79 %) and 12.2 min (45 %) for AIStd , AIMinEd and AIFullEd respectively. All pipelines exhibited generally good dosimetric agreement with MDOb1 . For RD, median deviations were within ± 1.8 cm3 , ± 1.7 % and ± 0.6 Gy for absolute volume, relative volume and mean dose metrics respectively. For PD, respective values were within ± 0.4 cm3 , ± 0.5 % and ± 0.2 Gy. Statistically (p < 0.05), AIMinEd and AIFullEd were dosimetrically non-inferior to MDOb2 .

CONCLUSIONS: This novel dosimetric validation demonstrated that following targeted minor editing (AIMinEd ), AI contours were dosimetrically non-inferior to manual delineations, reducing delineation time by 79 %.