A newly developed machine learning model developed by Microsoft Azure Machine Learning platform can aid in patient selection for prostate multiparametric MRI (mpMRI) to optimize resource utilization and reduce unnecessary costs.
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‘According to this study, a newly developed machine learning model developed by Microsoft Azure Machine Learning can accurately predict which patients are most likely to benefit from prostate multiparametric MRI (mpMRI).’
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A total of 811 prostate mpMRI examinations from four tertiary care centers with mpMRI expertise were used to develop a support vector machine model for predicting PI-RADS category 4 or 5 lesions on the basis of patient age, prostate specific antigen, and prostate volume. Patients either had no prior prostate biopsy or had a negative prior prostate biopsy. The model was developed on the Microsoft Azure Machine Learning platform and can be accessed at birch.azurewebsites.net. The model was then tested prospectively on 42 patients. 
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The model showed 73 percent accuracy for predicting PI-RADS category 4 or 5 lesions on the basis of 10-fold cross validation. Prospective validation of the model demonstrates a sensitivity of 75 percent and specificity of 82 percent for a cutoff threshold of 43 percent for predicting PI-RADS category 4 or 5 lesions.
With educational activities representing the entire spectrum of radiology, ARRS will host leading radiologists from around the world at the ARRS 2018 Annual Meeting, April 22-27, at the Marriott Wardman Park Hotel in Washington, DC.
Source-Eurekalert