  Purpose: Target delineation for radiation therapy is a time-consuming and
complex task. Autocontouring gross tumor volumes (GTVs) has been shown to
increase efficiency. However, there is limited literature on post-operative
target delineation, particularly for CT-based studies. To this end, we trained
a CT-based autocontouring model to contour the post-operative GTV of pediatric
patients with medulloblastoma.
  Methods: 104 retrospective pediatric CT scans were used to train a GTV
auto-contouring model. 80 patients were then preselected for contour
visibility, continuity, and location to train an additional model. Each GTV was
manually annotated with a visibility score based on the number of slices with a
visible GTV (1 = <25%, 2 = 25%-50%, 3 = >50%-75%, and 4 = >75%-100%). Contrast
and the contrast-to-noise ratio (CNR) were calculated for the GTV contour with
respect to a cropped background image. Both models were tested on the original
and pre-selected testing sets. The resulting surface and overlap metrics were
calculated comparing the clinical and autocontoured GTVs and the corresponding
clinical target volumes (CTVs).
  Results: 80 patients were pre-selected to have a continuous GTV within the
posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2,
and 1, respectively. The contrast and CNR removed an additional 11 and 20
patients from the dataset, respectively. The Dice similarity coefficients (DSC)
were 0.61 +/- 0.29 and 0.67 +/- 0.22 on the models without pre-selected
training data and 0.55 +/- 13.01 and 0.83 +/- 0.17 on the models with
pre-selected data, respectively. The DSC on the CTV expansions were 0.90 +/-
0.13. Conclusion: We automatically contoured continuous GTVs within the
posterior fossa on scans that had contrast >=10 HU. CT-Based auto-contouring
algorithms have potential to positively impact centers with limited MRI access.
