Pediatric central nervous system CNS tumors, the second most common childhood malignancy and the most common solid tumor in children, comprise tumors of the brain, the spinal cord, and the meninges. The management of brain tumors depends on patient age, histology, tumor location and extent but typically involves a radiation therapy RT planning based on computed tomography CT and magnetic resonance imaging MRI. MRI is acquired for detailed tumor localization and delineations of the target and OARs thanks to its excellent soft-tissue contrast.
In this study, a commercial solution for automatic OAR delineation, previously trained and validated on adult patients, was tested on an unseen cohort of pediatric patients to evaluate its clinical acceptability. ART-Net, a CE-marked, FDA-cleared anatomically preserving deep-learning ensemble architecture for automatic annotation of OAR was evaluated using pediatric patients from 2 centers selected across 4 different age groups y 9 , y 10 , y 10 and y For each patient, automatic contours AC of 24 OARs were performed and submitted to 5 experts across 4 centers for evaluation.
To avoid any bias, experts were blind to the fact that all contours were generated automatically. AC were generated in a mean time of 0. Table 1 shows how the performance of the software increases with age group: age groups of y and y show higher clinical acceptability Only 2. We show first results for the evaluation of MR-brain AI-based auto-contouring for pediatric cases.
The results show very good acceptance, highlighting the high usability of the commercial tool for MR annotation for pediatric brain cases and its clinical implementation feasibility.
In the future, a comparison between AI and human expert and a dosimetric evaluation will be performed. Purpose or Objective Pediatric central nervous system CNS tumors, the second most common childhood malignancy and the most common solid tumor in children, comprise tumors of the brain, the spinal cord, and the meninges. Materials and Methods ART-Net, a CE-marked, FDA-cleared anatomically preserving deep-learning ensemble architecture for automatic annotation of OAR was evaluated using pediatric patients from 2 centers selected across 4 different age groups y 9 , y 10 , y 10 and y Results AC were generated in a mean time of 0.
