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Abstract #1751

Machine Learning Model for Non-invasive Liver Fibrosis Staging on MRI and its Added Value to Ultrasound Liver Stiffness Measurements

Yiyang Sun1, Junhao Zha2, Chengxiu Zhang1, Chenglong Wang1, Yang Song3, Yinqiao Yi1, Shenghong Ju2, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China, 3Siemens Healthineers Ltd., Shanghai, China

Synopsis

Keywords: Liver, Liver

Motivation: Early non-invasive assessment of liver fibrosis is crucial for timely intervention and treatment.

Goal(s): This study aims to explore the added value of T1-delay MR to liver stiffness measurement (LSM).

Approach: We retrospectively collected of 655 chronic hepatitis B patients and constructed machine learning models based on T1-delay MR images and ultrasound LSM for liver fibrosis staging.

Results: The MR-LSM model achieved an AUC of 0.824 for identification of clinically significant liver fibrosis (F≥2), outperforming the standalone MR and LSM models. For identification of advanced fibrosis (F≥3), the MR-LSM model reached an AUC of 0.855, significantly surpassing other models.

Impact: T1-delay MR can be combined with LSM for non-invasive liver fibrosis classification, enabling early detection of liver fibrosis and timely intervention.

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Keywords