P13.05.B INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET

Abstract

Abstract BACKGROUND Translation of AI algorithms into clinical practice is significantly limited by lack of large individual hospital-based datasets with expert annotations. Current methods for generation of annotated imaging data are significantly limited due to inefficient imaging data transfer, complicated annotation software, and time required for experts to generate ground truth information. We incorporated AI tools for auto-segmentation of gliomas into PACS that is used at our institution for reading clinical studies and developed a workflow for annotation of images and development of volumetric segmentations in neuroradiology clinical workflow. MATERIAL AND METHODS 1990 patients from Yale Radiation Oncology Registry (2012-2019) were screened. Segmentations were performed using a UNETR algorithm trained on BRaTS 2021 and an internal dataset of manually segmented tumors. AI generated segmentation can be revised and confirmed in PACS. Segmentations were validated by a board-certified neuro-radiologist, after which natively embedded PyRadiomics in PACS was used for direct feature extraction. RESULTS In 7 Months (05/2021 - 08/2021, 03/2022 - 05/2022) segmentations and annotations were performed in 1033 patients (429 female, 604 male, mean age 53 yrs). Dataset includes 595 Grade 4 Gliomas (96 Grade 3, 105 Grade 2, 45 Grade 1, 192 unknown). Molecular subtypes include IDH (129 mutated, 651 wildtype, 253 unknown), 1p/19q (94 deleted or co-deleted, 135 intact, 804 unknown), MGMT promotor (216 methylated, 110 partially methylated, 321 unmethylated, 383 unknown), EGFR (125 amplified, 248 not amplified, 660 unknown), ATRX (41 mutated, 238 retained, 754 unknown), Ki-67 (726 known, 307 unknown) and p53 (639 known, 394 unknown). Classification of gliomas between grade 3/4 and grade 1/2, yielded AUC of 0.85. CONCLUSION We developed a method for incorporation of volumetric segmentation, feature extraction, and classification that is easily incorporated into neuroradiology workflow. These tools allowed us to annotate over 100 gliomas per month, thus establishing a proof of concept for rapid development of annotated imaging database for AI applications.