Contouring variation is one of the largest systematic uncertainties in radiotherapy, yet its effect on clinical outcome has never been analysed quantitatively. We propose a novel, robust methodology to locally quantify target contour variation in a large patient cohort and find where this variation correlates with treatment outcome. We demonstrate its use on biochemical recurrence for prostate cancer patients.
We propose to compare each patient’s target contours to a consistent and unbiased reference. This reference was created by auto‐contouring each patient’s target using an externally trained deep learning algorithm. Local contour deviation measured from the reference to the manual contour were projected to a common frame of reference, creating contour deviation maps for each patient. By stacking the contour deviation maps, time to event was modelled pixel‐wise using a multivariate Cox proportional hazards model (CPHM). Hazard ratio (HR) maps for each covariate were created, and regions of significance found using cluster‐based permutation testing on the z‐statistics.
This methodology was applied to Clinical Target Volume (CTV) contours, containing only the prostate gland, from 232 intermediate‐ and high‐risk prostate cancer patients. The reference contours were created using ADMIRE® v3.4 (Elekta AB, Sweden). Local contour deviations were computed in a spherical coordinate frame, where differences between reference and clinical contours were projected in a 2D map corresponding to sampling across the coronal and transverse angles every 3°. Time to biochemical recurrence was modelled using the pixel‐wise CPHM analysis accounting for contour deviation, patient age, Gleason score and treated CTV volume.
We successfully applied the proposed methodology to a large patient cohort containing data from 232 patients. In this patient cohort, our analysis highlighted regions where the contour variation was related to biochemical recurrence, producing expected and unexpected results: 1) the interface between prostate‐bladder and prostate‐seminal vesicle interfaces where increase of the manual contour relative to the reference was related to a reduction of risk of biochemical recurrence by 4‐8% per mm and 2) the prostate's right, anterior and posterior regions where an increase of the manual contour relative to the reference contours was related to an increase of risk of biochemical recurrence by 8‐24% per mm.
We proposed and successfully applied a novel methodology to explore the correlation between contour variation and treatment outcome. We analysed the effect of contour deviation of the prostate CTV on biochemical recurrence for a cohort of more than 200 prostate cancer patients while taking basic clinical variables into account. Applying this methodology to a larger dataset including additional clinically important covariates and externally validating it can more robustly identify regions where contour variation directly relates to treatment outcome. For example, in the prostate case we use to demonstrate our novel methodology, external validation will help confirm or reject the counter‐intuitive results (larger contours resulting in higher risk). Ultimately, the results of this methodology could inform contouring protocols based on actual patient outcomes.