Related Experiment Video
Updated: Jan 3, 2026

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
Published on: July 21, 2023
Immunotherapy response evaluation with magnetic resonance elastography (MRE) in advanced HCC
Aliya Qayyum1, Ken-Pin Hwang2, Jason Stafford2
1Department of Abdominal Imaging, UT MD Anderson Cancer Center, 1400 Pressler Street, Houston, Texas, USA. aqayyum@mdanderson.org.
Changes in liver stiffness measured by magnetic resonance elastography (MRE) may predict immunotherapy response in advanced hepatocellular carcinoma (HCC). Early MRE stiffness changes correlate with survival and T-cell infiltration, offering a potential imaging biomarker.
Area of Science:
- Hepatobiliary imaging
- Oncology
- Immunotherapy
Background:
- Hepatocellular carcinoma (HCC) lacks established imaging predictors for immunotherapy outcomes.
- Magnetic resonance elastography (MRE) measures tissue stiffness, a potential indicator of tumor response.
Purpose of the Study:
- To investigate if changes in liver stiffness, assessed by MRE, can predict immunotherapy response in advanced HCC patients.
- To correlate MRE-derived stiffness changes with overall survival (OS), time to disease progression (TTP), and T-cell infiltration.
Main Methods:
- Prospective study of 15 advanced HCC patients treated with Pembrolizumab.
- Liver MRE and biopsy performed at baseline and 6 weeks.
- Spearman correlation analyzed the relationship between stiffness changes, OS, TTP, and intratumoral CD3+ T lymphocytes.
Main Results:
- Early changes in HCC stiffness at 6 weeks significantly correlated with OS (R=0.81) and TTP (R=0.88, p<0.01).
- HCC stiffness changes also correlated significantly with intratumoral T lymphocyte abundance (R=0.79, p=0.007).
- Non-tumor liver stiffness showed no significant change.
Conclusions:
- Pilot MRE data suggests early changes in tumor stiffness may serve as an indicator of immunotherapy response in advanced HCC.
- MRE-derived stiffness changes show promise as a non-invasive imaging biomarker for predicting treatment efficacy.
More Related Videos
12:18Magnetic Resonance Elastography Methodology for the Evaluation of Tissue Engineered Construct Growth
Published on: February 9, 2012
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018