Incorporating patient-specific prior clinical knowledge to improve clinical target volume auto-segmentation generalisability for online adaptive radiotherapy of rectal cancer: A multicenter validation

Background & purpose: Deep learning (DL) based auto-segmentation has shown to be beneficial for online adaptive radiotherapy (OART). However, auto-segmentation of clinical target volumes (CTV) is complex, as clinical interpretations are crucial in their definition. The resulting variation between clinicians and institutes hampers the generalizability of DL networks. In OART the CTV is delineated during treatment preparation which makes the clinician intent explicitly available during treatment. We studied whether multicenter generalisability improves when using this prior clinical knowledge, the pre-treatment delineation, as a patient-specific prior for DL models for online auto-segmentation of the mesorectal CTV. Material & methods: We included intermediate risk or locally advanced rectal cancer patients from three centers. Patient-specific weight maps were created by combining the patient-specific CTV delineation on the pre-treatment scan with population-based variation of likely inter-fraction mesorectal CTV deformations. We trained two models to auto-segment the mesorectal CTV on in-house data, one with (MRI + prior) and one without (MRI-only) priors. Both models were applied to two external datasets. An external baseline model was trained without priors from scratch for one external center. Performance was evaluated on the DSC, surface Dice, 95HD and MSD. Results: For both external centers, the MRI + prior model outperformed the MRI-only model significantly on the segmentation metrics (p-values < 0.01). There was no significant difference between the external baseline model and the MRI + prior model. Conclusion: Adding patient-specific weight maps makes the CTV segmentation model more robust to institutional preferences. Performance was comparable to a model trained locally from scratch. This makes this approach suitable for generalization to multiple centers.