Abstract #1942
Radiomics assessment of liver fibrosis in patients with chronic liver disease using MRI: a machine learning approach
Farzin Mobayyyen1,2, Behrooz Taghvainia3, Hassan Homayoun2, Ali Abbasian Ardakani4, Anahita Fathi Kazerooni2, Hanieh Mobarak Salari2, Nasser Rakhshani5, Hamidreza Salighehrad1,2, and Hamid Reza haghighatkhah6
1Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), Tehran, Iran (Islamic Republic of), 2Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), 3Shahid Beheshti University of Medical Science, Tehran, Iran (Islamic Republic of), 4Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Islamic Republic of), 5Gastrointestinal and Liver Disease Research Center, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (Islamic Republic of), 6Department of Diagnostic Imaging, Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Science, Tehran, Iran (Islamic Republic of)
Synopsis
Neither biochemical markers nor a qualitative assessment of medical images are reliable to differentiate mild from moderate stages of liver fibrosis. The main purpose of this study is to develop a machine learning model to classify mild and moderate liver fibrosis based on radiomic features extracted from MRI images (T1-w, T2*-map & ADC-map). Nu-SVC classifier was employed as the classification technique, trained by the extracted data from image series of 29 patients with histopathology-confirmed mild and moderate liver fibrosis. Results demonstrate that radiomic analysis of T2*-map and ADC-map has high potential in classifying different stages of liver fibrosis.
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