Implementation and clinical evaluation of an in-house thoracic auto-segmentation model for 0.35 T magnetic resonance imaging guided radiotherapy

Background and Purpose:

Magnetic resonance imaging-guided radiotherapy (MRgRT) facilitates high accuracy, small margins treatments at the cost of time-consuming and labor-intensive manual delineation of organs-at-risk (OARs). Auto-segmentation models show promise in streamlining this workflow. This study investigates the clinical applicability of a set of thoracic OAR segmentation models for baseline treatment planning in lung tumor patients. We investigate the use of the models for treatment at a 0.35 T MR-linac, assess their potential to reduce physician workload in terms of time savings and quantify the extent of required manual corrections, providing insights into the value of their integration into clinical practice.

Materials and Methods:

Deep-learning based auto-segmentation models for 9 thoracic OARs were integrated into the MRgRT workflow. Two groups of 11 lung cancer cases each were prospectively considered. For Group 1 auto-segmentation contours were corrected by physicians, for Group 2 manual contouring according to standard clinical workflows was performed. Contouring times were recorded for both. Time savings between the groups as well as correlations of the extent of corrections to correction times for Group 1 patients were analyzed.

Results:

The model performed consistently well across all Group 1 cases. Median contouring times were reduced for six out of nine OARs leading to a reduction of 50.3 % or 12.6 min in median total contouring time.

Conclusion:

Feasibility of auto-segmentation for baseline treatment planning at the 0.35 T MR-linac was shown with significant time savings demonstrated. Time saving potential could not be estimated from model geometric performance metrics.