Radiomics and molecular analysis: Bridging the gap for predicting hepatocellular carcinoma prognosis

This editorial examines a recent study that used radiomics based on computed tomography (CT) to predict the expression of the fibroblast-related gene enhancer of zeste homolog 2 (<i>EZH2</i>) and its correlation with the survival of patients with hepatocellular carcinoma (HCC). By integrating radiomics with molecular analysis, the study presented a strategy for accurately predicting the expression of <i>EZH2</i> from CT scans. The findings demonstrated a strong link between the radiomics model, <i>EZH2</i> expression, and patient prognosis. This noninvasive approach provides valuable insights into the therapeutic management of HCC.

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Publication Details

Journal
World Journal of Clinical Cases
Published
2024-11-13
DOI
https://doi.org/10.12998/wjcc.v13.i4.98550
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Radiomics and molecular analysis: Bridging the gap for predicting hepatocellular carcinoma prognosis

Chun‐Han Cheng, Wen‐Rui Hao, Tzu-Hurng Cheng
World Journal of Clinical Cases
Radiomics and Machine Learning in Medical Imaging
article

Radiomics and molecular analysis: Bridging the gap for predicting hepatocellular carcinoma prognosis

Chun‐Han Cheng, Wen‐Rui Hao, Tzu-Hurng Cheng
article en

Abstract

This editorial examines a recent study that used radiomics based on computed tomography (CT) to predict the expression of the fibroblast-related gene enhancer of zeste homolog 2 (<i>EZH2</i>) and its correlation with the survival of patients with hepatocellular carcinoma (HCC). By integrating radiomics with molecular analysis, the study presented a strategy for accurately predicting the expression of <i>EZH2</i> from CT scans. The findings demonstrated a strong link between the radiomics model, <i>EZH2</i> expression, and patient prognosis. This noninvasive approach provides valuable insights into the therapeutic management of HCC.

World Journal of Clinical CasesVol. 13(4)
Taipei Medical University (TW)
Good health and well-being
Openalex Percentile: Top 66%
Radiomics and Machine Learning in Medical Imaging
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