Do AI-based contouring algorithms influence physicians in the online adaptive radiotherapy of patients with bladder cancer?

Résumé fourni par la source

Purpose In this prospective cross-over study, the precision of manual correction of the clinical target volume (CTV) during online-adaptive radiotherapy (ART) of patients with bladder cancer was investigated in dependence on the input of applied computational algorithms. Methods and materials Online-adaptive-radiation therapy (ART) fractions were analysed from a prospective registry as a component of a trimodality treatment for bladder cancer. The CBCT-guided adaptive radiation therapy (CBCTgART) workflow uses a CBCT acquired at the beginning of a treatment session (CBCT1) to update the target volume contours of the planning CT (pCT) on the current anatomy of the day. The study comprises radiotherapy fractions with target (CTV) segments showing clinically relevant deviations (>5 mm) when mapped between pre-treatment (CBCT1) and post-adaptation (CBCT2) images. CTV algo1-2 were obtained from two deformable-image-registration algorithms (algo 1, algo 2 ) based on AI-generated normal-tissue contours and a rigid copy of the CTV original termed CTV algo3 (algo 3 ). Verification cone-beam-computed-tomography (CBCT2) acquired during ART showed CTV algo1-3 on new anatomical scenarios. The task of 13 physicians was to adjust CTV algo1-3 on CBCT2 to match the CTV original on CBCT1. Results In 151/169 pairwise comparisons, mean-distance-of-segments-to-ground-truth (MDA GT ) after adjustment by a physician was larger for the poorer algorithms than for the better ones. The binomial proportion was 0.893, significantly different from 0.5 as null proportion (p < 0.0001, exact-binomial-test), and not dependent on the physician or on the respective patient. In a quantitative analysis by a mixed-model, the MDA GT of the manually adjusted segments between 16% and 50% of the errors were on average corrected by the physicians, with interindividual differences (p < 0.0001,F-test). Conclusions The present study results reveal that the quality of AI-generated contours is of crucial importance for a proper online onboard ART, as errors may bias clinical workflow and contouring. The accuracy of supervised structures strongly depends on the accuracy of the AI generated contours before supervision.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.