  Background and purpose: The delineation of the gross tumor volume (GTV) is a
critical step for radiation therapy treatment planning. The delineation
procedure is typically performed manually which exposes two major issues: cost
and reproducibility. Delineation is a time-consuming process that is subject to
inter- and intra-observer variability. While methods have been proposed to
predict GTV contours, typical approaches ignore variability and therefore fail
to utilize the valuable confidence information offered by multiple contours.
Materials and methods: In this work we propose an automatic GTV contouring
method for soft-tissue sarcomas from X-ray computed tomography (CT) images,
using deep learning by integrating inter- and intra-observer variability in the
learned model. Sixty-eight patients with soft tissue and bone sarcomas were
considered in this evaluation, all underwent pre-operative CT imaging used to
perform GTV delineation. Four radiation oncologists and radiologists performed
three contouring trials each for all patients. We quantify variability by
defining confidence levels based on the frequency of inclusion of a given voxel
into the GTV and use a deep convolutional neural network to learn GTV
confidence maps. Results: Results were compared to confidence maps from the
four readers as well as ground-truth consensus contours established jointly by
all readers. The resulting continuous Dice score between predicted and true
confidence maps was 87% and the Hausdorff distance was 14 mm. Conclusion:
Results demonstrate the ability of the proposed method to predict accurate
contours while utilizing variability and as such it can be used to improve
clinical workflow.
