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Abstract #0192

Predicting VETC in Hepatocellular Carcinoma Using Radiomic Features from Peritumoral Mechanical Strain Assessed by MR Elastography

Keni Zheng1, Mengsi Li2, Yi Sui1, Xiang Shan1, Emi Hojo1, Armando Manduca3, Richard Ehman1, Jin Wang2, and Ziying Yin1
1Radiology, Mayo Clinic, Rochester, MN, United States, 2The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China, 3Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States

Synopsis

Keywords: Liver, Liver, MRI, MR Elastography (MRE), HCC, VETC, Radiomics, texture features

Motivation: Vessels encapsulating tumor clusters (VETC) is a powerful indicator of aggressive Hepatocellular carcinoma (HCC) associated with recurrence, which is expected to be noninvasively identified using imaging techniques.

Goal(s): This study aims to develop a potential mechanical biomarker utilizing radiomics features extracted from MR Elastography (MRE)-assesed peritumoral shear strain to predict VETC in HCC.

Approach: Radiomics features were extracted from the tumor boundary on an octahedral shear strain (OSS) map. Feature selection techniques were used to identify relevant features to construct a radiomics score.

Results: Three radiomics features were utilized to construct a radiomics score, demonstrating potential for MRE-based strain analysis in predicting VETC.

Impact: A radiomics score constructed by radiomics features derived from MRE-based strain analysis in the peritumoral region show promise in predicting VETC in HCC patients. This study results show non-invasive VETC prediction potential in HCC, impacting personalized treatment and patient outcomes.

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Keywords