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 Singh^1,3

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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