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Purpose: Target delineation for radiation therapy is a time-consuming andcomplex task. Autocontouring gross tumor volumes (GTVs) has been shown toincrease efficiency. However, there is limited literature on post-operativetarget delineation, particularly for CT-based studies. To this end, we traineda CT-based autocontouring model to contour the post-operative GTV of pediatricpatients with medulloblastoma.  Methods: 104 retrospective pediatric CT scans were used to train a GTVauto-contouring model. 80 patients were then preselected for contourvisibility, continuity, and location to train an additional model. Each GTV wasmanually annotated with a visibility score based on the number of slices with avisible GTV (1 = <25%, 2 = 25%-50%, 3 = >50%-75%, and 4 = >75%-100%). Contrastand the contrast-to-noise ratio (CNR) were calculated for the GTV contour withrespect to a cropped background image. Both models were tested on the originaland pre-selected testing sets. The resulting surface and overlap metrics werecalculated comparing the clinical and autocontoured GTVs and the correspondingclinical target volumes (CTVs).  Results: 80 patients were pre-selected to have a continuous GTV within theposterior 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 20patients from the dataset, respectively. The Dice similarity coefficients (DSC)were 0.61 +/- 0.29 and 0.67 +/- 0.22 on the models without pre-selectedtraining data and 0.55 +/- 13.01 and 0.83 +/- 0.17 on the models withpre-selected data, respectively. The DSC on the CTV expansions were 0.90 +/-0.13. Conclusion: We automatically contoured continuous GTVs within theposterior fossa on scans that had contrast >=10 HU. CT-Based auto-contouringalgorithms have potential to positively impact centers with limited MRI access.

Resection cavity auto-contouring for patients with pediatric medulloblastoma using only CT information