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 Mehndiratta1,5
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
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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