Spectrum of qualitative and quantitative imaging of pilomyxoid, intermediate pilomyxoid and pilocytic astrocytomas in relation to their genetic alterations.

PURPOSE: Pilomyxoid astrocytomas (PMA) are pediatric brain tumors predominantly located in the suprasellar region, third ventricle and posterior fossa, which are considered to be more clinically aggressive than pilocytic astrocytomas (PA). Another entity, intermediate pilomyxoid tumors (IPT), exists within the spectrum of pilocytic/pilomyxoid astrocytomas. The 2021 WHO CNS classification refrained from assigning grade 1 or 2 status to PMA, thereby reflecting the need to further elucidate their clinical and imaging characteristics. METHODS: We included a total of 15 patients with PMA, IPT and suprasellar PA. We retrospectively evaluated immunohistochemistry, imaging findings and diffusion characteristics within these tumors as well as whole exome sequencing for three of the cases. RESULTS: 87% of the tumors were supratentorial with 11 cases suprasellar in location, 1 case located in the frontal white matter and 1 in the hippocampus. 6 cases demonstrated intraventricular extension. ADC values were higher in PMA and IPT than PA. 3 cases demonstrated KIAA1549-BRAF-fusion, 2 had BRAF[Formula: see text]-mutation and 6 were BRAF-wildtype. All cases had recurrence/progression on follow-up. CONCLUSION: PMA and IPT do not demonstrate aggressive imaging characteristics in respect to their diffusion imaging with ADC values being higher than PA. Lack of BRAF-alteration in PMA corresponded to atypical location of tumors with atypical driver mutations and mechanisms.

Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction.

Purpose: Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient's medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction. Materials and methods: An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) 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 the brain tumor segmentations. Results: UNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study. Conclusion: Integration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and 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 and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis.

Objectives: To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction. Methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection. Publications were screened in Covidence, and reporting quality was measured against the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Descriptive statistics were calculated using GraphPad Prism 9. Results: The search identified 11,727 candidate articles with 1,135 articles undergoing full text review and 85 included in analysis. 67 (79%) articles were published between 2018-2021. The mean prediction accuracy of the best performing model in each study was 0.89 ± 0.09. The most common algorithm for conventional machine learning studies was Support Vector Machine (mean accuracy: 0.90 ± 0.07) and for deep learning studies was Convolutional Neural Network (mean accuracy: 0.91 ± 0.10). Only one study used both a large training dataset (n>200) and external validation (accuracy: 0.72) for their model. The mean adherence rate to TRIPOD was 44.5% ± 11.1%, with poor reporting adherence for model performance (0%), abstracts (0%), and titles (0%). Conclusions: The application of ML to glioma grade prediction has grown substantially, with ML model studies reporting high predictive accuracies but lacking essential metrics and characteristics for assessing model performance. Several domains, including generalizability and reproducibility, warrant further attention to enable translation into clinical practice. Systematic Review Registration: PROSPERO, identifier CRD42020209938.

Topographic correlates of driver mutations and endogenous gene expression in pediatric diffuse midline gliomas and hemispheric high-grade gliomas.

We evaluate the topographic distribution of diffuse midline gliomas and hemispheric high-grade gliomas in children with respect to their normal gene expression patterns and pathologic driver mutation patterns. We identified 19 pediatric patients with diffuse midline or high-grade glioma with preoperative MRI from tumor board review. 7 of these had 500 gene panel mutation testing, 11 patients had 50 gene panel mutation testing and one 343 gene panel testing from a separate institution were included as validation set. Tumor imaging features and gene expression patterns were analyzed using Allen Brain Atlas. Twelve patients had diffuse midline gliomas and seven had hemispheric high-grade gliomas. Three diffuse midline gliomas had the K27M mutation in the tail of histone H3 protein. All patients undergoing 500 gene panel testing had additional mutations, the most common being in ACVR1, PPM1D, and p53. Hemispheric high-grade gliomas had either TP53 or IDH1 mutation and diffuse midline gliomas had H3 K27M-mutation. Gene expression analysis in normal brains demonstrated that genes mutated in diffuse midline gliomas had higher expression along midline structures as compared to the cerebral hemispheres. Our study suggests that topographic location of pediatric diffuse midline gliomas and hemispheric high-grade gliomas correlates with driver mutations of tumor to the endogenous gene expression in that location. This correlation suggests that cellular state that is required for increased gene expression predisposes that location to mutations and defines the driver mutations within tumors that arise from that region.