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Abstract #3144

Deciphering predictive models for differentiating vertebral lesions using multiparametric MRI

Durgesh Kumar Dwivedi1, Anit Parihar1, Rashi Rathore1, Neera Kohli1, Alok Kumar Dwivedi2, and Anil Chandra3

1Radiodiagnosis, King George's Medical University, Lucknow, India, 2Division of Biostatistics & Epidemiology, Biomedical Sciences, Texas Tech University Health Sciences Center, El Paso, TX, United States, 3Neurosurgery, King George's Medical University, Lucknow, India

Conventional MR imaging has high sensitivity but limited specificity in differentiating various vertebral lesions. We aimed to assess the ability of multiparametric MR imaging in differentiating spinal vertebral lesions and to develop statistical models for predicting the probability of malignant vertebral lesions. On the basis of the mean ADC and signal intensity ratio, we established automated statistical models that would be helpful in differentiating vertebral lesions. Our study shows that multiparametric MRI differentiates various vertebral lesions, and we established prediction models for the same.

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