Deep learning-based delineation of whole-body organs at risk empowering adaptive radiotherapy.

Abstract

Background: Accurate delineation of organs at risk (OARs) is crucial for precision radiotherapy. Most previous autosegmentation models were only constructed for single anatomical region without evaluation of dosimetric impact. We aimed to validate the clinical practicability of deep-learning (DL) models for autosegmentation of whole-body OARs with respect to delineation accuracy, clinical acceptance and dosimetric impact.

Methods: OARs in various anatomical regions, including the head and neck, thorax, abdomen, and pelvis, were automatedly delineated by DL models (DLD) and compared to manual delineations (MD) by an experienced radiation oncologist (RO). The geometric performance was evaluated using the Dice similarity coefficient (DSC) and average surface distance (ASD). RO A corrected DLD to create delineations approved in clinical practice (CPD). RO B graded the accuracy of DLD to assess clinical acceptance. The dosimetric impact was determined by assessing the difference in dosimetric parameters for each OAR in the DLD-based radiotherapy plan (Plan_DLD) and the CPD-based radiotherapy plan (Plan_CPD).

Results: The automatic delineation model has a high OAR delineation accuracy, and the median DSCs can reach 0.841 (IQR, 0.791-0.867) in the head and neck OAR, 0.903 (IQR, 0.777-0.932) in thoracic OAR, 0.847 (IQR, 0.834-0.931) in abdominal OAR, 0.916 (IQR, 0.906-0.964) in pelvic OAR. The majority of DL-generated OARs were graded as clinically acceptable with no editing or little editing needed. No significant differences in dosimetric parameters were found by comparing Plan_DLD with Plan_CPD.

Conclusions: For OARs of whole bodily regions, DL-based segmentation is fast; DL models perform sufficiently well for clinical practice with respect to delineation accuracy, clinical accepatance and dosimetric impact.