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Abstract #3106

Differentiation between Intra-Cranial Mass Lesions using Machine Learning approach on Amide Proton Transfer-weighted (APT-w) CEST MRI

Ayan Debnath1, Manish Awasthi1, Neha Vats1, Rakesh Kumar Gupta2, and Anup Singh1,3
1Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, 2Department of Radiology, 2Fortis Memorial Research Institute, Gurgaon, Haryana, India, 3Department of Biomedical Engineering, All India Institute of Medical Science, New Delhi, India

It is challenging to differentiate between intra-cranial mass lesions (ICMLs) due to similar appearance using conventional MRIs. Amide-proton-transfer-weighted(APT-w) MRI provides differentiation among ICMLs with lower sensitivity ad specificity. The accuracy of classification between neo-plastic mass lesions and infective mass lesions as well as differentiation between low-grade-glioma and high-grade-glioma improves by implementing machine learning classifier based on features from APT-w CEST MRI. An optimized support-vector-machine with 10-fold cross-validation and optimal set of features extracted using Random forest based feature selection provided high accuracy of around 90%.

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