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Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
Published on: August 8, 2019
Quantitative Analysis of Neural Foramina in the Lumbar Spine: An Imaging Informatics and Machine Learning Study
Bilwaj Gaonkar1, Joel Beckett1, Diane Villaroman1
1Departments of Neurosurgery (B.G., J.B., D.V., C. Ahn, M.E., M.A., D.B., L.M.), Radiological Sciences (J.P.V., N.S., A.B.), and Electrical Engineering (S.M.), University of California, Los Angeles, 300 Stein Plaza, Suite 554E, Los Angeles, CA 90095; and Department of Neurosurgery, University of California, San Francisco, San Francisco, Calif (C. Ames).
Purpose:
To use machine learning tools and leverage big data informatics to statistically model the variation in the area of lumbar neural foramina in a large asymptomatic population.
Materials And Methods:
By using an electronic health record and imaging archive, lumbar MRI studies in 645 male (mean age, 50.07 years) and 511 female (mean age, 48.23 years) patients between 20 and 80 years old were identified. Machine learning algorithms were used to delineate lumbar neural foramina autonomously and measure their areas. The relationship between neural foraminal area and patient age, sex, and height was studied by using multivariable linear regression.
Results:
Neural foraminal areas correlated directly with patient height and inversely with patient age. The associations involved were statistically significant (P < .01).
Conclusion:
By using machine learning and big data techniques, a linear model encoding variation in lumbar neural foraminal areas in asymptomatic individuals has been established. This model can be used to make quantitative assessments of neural foraminal areas in patients by comparing them to the age-, sex-, and height-adjusted population averages.© RSNA, 2019Supplemental material is available for this article.
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