Diagnosing growth in low-grade gliomas with and without artificial intelligence-measured longitudinal volume measurements: A retrospective observational study.

Hassan M Fathallah-Shaykh¹, Houman Sotoudeh², Markus Bredel³, Alex Whitley⁴, Jinsuh Kim⁵, Fanny E Morón⁶, Fabio Raman⁷, Nidhal Bouaynaya⁸, Hayat Rahal⁹

¹Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL (H.M.F.-S.).

²Department of Radiology, University of Texas Southwestern, Dallas, TX.

³Department of Radiation Oncology, University of Miami, Miami, FL.

⁴-Central Alabama Radiation Oncology, Montgomery, AL.

⁵Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.

⁶Department of Radiology, Baylor College of Medicine, Houston, TX (F.E.M.).

⁷Department of Radiology, Johns Hopkins School of Medicine, Baltimore, MD.

⁸Department of Computer and Electrical Engineering, Rowans University, -Glassboro, NJ.

⁹MRIMath, Birmingham, AL.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Low-grade or grade 2 diffuse gliomas (LGG) infiltrate the brains leading to significant neurological morbidity. This retrospective observational study evaluates the ability of AI-assisted volumetric analysis to correctly detect tumor growth in longitudinal studies of LGG as compared to the standard clinical method. METHODS: A total of 56 gliomas and 7 stable FLAIR lesions were include…

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