Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI.

European Radiology
Stefanie J HectorsBachir Taouli

Abstract

To (1) develop a fully automated deep learning (DL) algorithm based on gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and (2) compare the diagnostic performance of DL vs. MR elastography (MRE) for noninvasive staging of liver fibrosis. This single-center retrospective study included 355 patients (M/F 238/117, mean age 60 years; training, n = 178; validation, n = 123; test, n = 54) who underwent gadoxetic acid-enhanced abdominal MRI, including HBP and MRE, and pathological evaluation of the liver within 1 year of MRI. Cropped liver HBP images from a custom-written fully automated liver segmentation were used as input for DL. A transfer learning approach based on the ImageNet VGG16 model was used. Different DL models were built for the prediction of fibrosis stages F1-4, F2-4, F3-4, and F4. ROC analysis was performed to evaluate the performance of DL in training, validation, and test sets and of MRE liver stiffness in the test set. AUC values of DL were 0.99/0.70/0.77 (F1-4), 0.92/0.71/0.91 (F2-4), 0.91/0.78/0.90 (F3-4), and 0.98/0.83/0.85 (F4) for training/validation/test sets, respectively. The AUCs of MRE liver stiffness in the test set were 0.86 (F1-4), 0.87 (F2-4), 0.92 (F3-4), and 0.86 (F4). AUCs of MRE and DL were n...Continue Reading

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Citations

May 11, 2021·World Journal of Gastroenterology : WJG·Jia-Sheng CaoXiu-Jun Cai
May 20, 2021·Liver International : Official Journal of the International Association for the Study of the Liver·Roi AntebyShelly Soffer
Jun 17, 2021·Seminars in Ultrasound, CT, and MR·Mayur VirarkarPriya Bhosale

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