Abstract #2034

Machine learning-based texture analysis of IVIM-DKI for classification of benign and malignant pancreatic masses

Archana Vadiraj Malagi¹, Sivachander Shivaji², Devasenathipathy Kandasamy², Pramod Garg³, Siddhartha Datta Gupta⁴, Shivanand Gamanagatti ², Raju Sharma², and Amit Mehndiratta^1,5

¹Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, ²Department of Radiodiagnosis, All India Institute of Medical Sciences Delhi, New Delhi, India, ³Department of Gastroenterology, All India Institute of Medical Sciences Delhi, New Delhi, India, ⁴Department of Pathology, All India Institute of Medical Sciences Delhi, New Delhi, India, ⁵Department of Biomedical Engineering, All India Institute of Medical Sciences Delhi, New Delhi, India

Synopsis

Tumor heterogeneity could be detected non-invasively utilizing textural indicators from IVIM-DKI, which have a high potential for early prognosis of pancreatic lesions. A novel technique was investigated for tumor prediction model utilizing IVIM-DKI with total variation penalty function, in which we employed combinations of texture characteristics from IVIM-DKI parameters. In this study, texture characteristics of the kurtosis(k) parameter had the high accuracy:93% and AUC:1, and combinations of all IVIM-DKI parameters' textural features after feature reduction had accuracy:84% and AUC:0.91 for classifying benign and malignant pancreatic lesions. Whole-volumetric texture analysis of IVIM-DKI can be employed for characterization of pancreatic lesions.

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