Artificial intelligence-generated targets and inter-observer variation in online adaptive radiotherapy of bladder cancer

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Background and purpose Daily target re-delineation in online adaptive radiotherapy (oART) introduces uncertainty. The aim of this study was to evaluate artificial intelligence (AI) generated contours and inter-observer target variation among radiotherapy technicians in cone-beam CT (CBCT) guided oART of bladder cancer. Materials and methods For each of 10 consecutive patients treated with oART for bladder cancer, one CBCT was randomly selected and retrospectively included. The bladder (CTV-T) was AI-segmented (CTV-T AI ). Seven radiotherapy technicians independently reviewed and edited CTV-T AI , generating CTV-T ADP . Contours were benchmarked against a ground truth contour (CTV-T GT ) delineated blindly from scratch. CTV-T ADP and CTV-T AI were compared to CTV-T GT using volume, dice similarity coefficient, and bidirectional local distance. Dose coverage (D 99% >95 %) of CTV-T GT was evaluated for treatment plans optimized for CTV-T AI and CTV-T ADP with clinical margins. Inter-observer variation among CTV-T ADP was assessed using coefficient of variation and generalized conformity index. Results CTV-T GT ranged from 48.7 cm 3 to 211.6 cm 3 . The median [range] volume difference was 4.5 [−17.8, 42.4] cm 3 for CTV-T ADP and −15.5 [−54.2, 4.3] cm 3 for CTV-T AI , compared to CTV-T GT . Corresponding dice similarity coefficients were 0.87 [0.71, 0.95] and 0.84 [0.64, 0.95]. CTV-T GT was adequately covered in 68/70 plans optimized on CTV-T ADP and in 6/10 plans optimized on CTV-T AI with clinical margins. The median [range] coefficient of variation was 0.08 [0.05, 0.11] and generalized conformity index was 0.78 [0.71, 0.88] among CTV-T ADP . Conclusions Target re-delineation in CBCT-guided oART of bladder cancer demonstrated non-isotropic inter-observer variation. Manual adjustment of AI-generated contours was necessary to cover ground truth targets.

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