To design a clinically applicable workflow for application of AI tools in Neuro-oncology.

Deep learning artificial intelligence is a powerful tool for automated segmentation although integrating this tool into the clinical workflow has been challenging. In this work, we implemented a deep learning-based algorithm for automated brain tumor segmentation and embedded it into PACS to accelerate a supervised, end-to-end workflow for radiomic feature extraction.

An algorithm was trained to segment primary brain tumors on MRI FLAIR images from the multi-institutional BRATS 2021 dataset which includes segmentations of glioblastoma and lower grade glioma. Afterwards the algorithm was validated using an internal dataset from Yale New Haven Health (YHHH).  A UNETR deep-learning network architecture was employed and embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from segmentations in form of JSON files. Titan Xp, NVIDIA , Santa Clara, CA was used for tumor segmentation.

Integration of advanced image-based algorithms and extraction of imaging biomarkers into PACS systems can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations on the diagnostic workstation reduce the time needed to generate ground-truth data.