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Radiomics Based on Contrast-Enhanced CT for Recognizing c-Met-Positive Hepatocellular Carcinoma: a Noninvasive Approach to Predict the Outcome of Sorafenib Resistance

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Abstract

Objectives

The purpose of our project was to investigate the effectiveness of radiomic features based on contrast-enhanced computed tomography (CT) that can detect the expression of c-Met in hepatocellular carcinoma (HCC) and to validate its efficacy in predicting the outcome of sorafenib resistance.

Materials and Methods

In total, 130 patients (median age, 60 years) with pathologically confirmed HCC who underwent contrast material–enhanced CT from October 2012 to July 2020 were randomly divided into a training set (n = 91) and a test set (n = 39). Radiomic features were extracted from arterial phase (AP), portal venous phase (VP) and delayed phase (DP) images of every participant’s enhanced CT images.

Results

The entire group comprised 39 Met-positive and 91 Met-negative patients. The combined model, which included the clinical factors and the radiomic features, performed well in the training (area under the curve [AUC] = 0.878) and validation (AUC = 0.851) cohorts. The nomogram, which relied on the combined model, fits well in the calibration curves. Decision curve analysis (DCA) further confirmed that the clinical valuation of the nomogram achieved comparable accuracy in c-Met prediction. Among another 20 patients with HCC who had received sorafenib, the predicted high-risk group had shorter overall survival (OS) than the predicted low-risk group (p < 0.05).

Conclusion

A multivariate model acquired from three phases (AP, VP and DP) of enhanced CT, HBV-DNA and γ glutamyl transpeptidase isoenzyme II (GGT-II) could be considered a satisfactory preoperative marker of the expression of c-Met in patients with HCC. This approach may help in overcoming sorafenib resistance in advanced HCC.

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Abbreviations

AFP:

a-Fetoprotein

AP:

Arterial phase

VP:

Portal venous phase

DP:

Delayed phase

AUC:

Area under curve

CT:

Computed tomography

DCA:

Decision curve analysis

HCC:

Hepatocellular carcinoma

ICC:

Intraclass correlation coefficients

HGF:

Hepatocyte growth factor

GGT-II:

γ glutamyl transpeptidase isoenzyme II

VEGF:

Vascular endothelial growth factor

MRI:

Magnetic resonance imaging

TACE:

Transcatheter arterial chemoembolization

DICOM:

Digital imaging and communications in medicine

ROI:

Regions of interest

Lasso:

Shrinkage and selection operator

ROC:

Receiver operating characteristic curve

CI:

Confidence interval

OR:

Odds ratio

OS:

Overall survival

GLDM:

Grey level dependence matrix

GLSZM:

Grey level size zone matrix

GLRLM:

Grey level run length matrix

GLCM:

Grey level co-occurrence matrix

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Acknowledgements

This study was supported by the Natural Science Foundation of China (82170514).

Author information

Authors and Affiliations

  1. Department of Vascular Surgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, the, People’s Republic of China

    Jingxiao Gu & Chen Huang

  2. Department of Radiology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China

    Jingxiao Gu & Zhongzheng Jia

  3. Department of Nuclear Medicine, Affiliated Hospital of Nantong University, Nantong, China

    Shanlei Bao

  4. Medical School of Nantong University, Nantong, China

    Reaoxian Akemuhan

  5. Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Nantong University, Nantong, China

    Yu Zhang

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  1. Jingxiao Gu
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  4. Zhongzheng Jia
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Correspondence to Zhongzheng Jia, Yu Zhang or Chen Huang.

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Gu, J., Bao, S., Akemuhan, R. et al. Radiomics Based on Contrast-Enhanced CT for Recognizing c-Met-Positive Hepatocellular Carcinoma: a Noninvasive Approach to Predict the Outcome of Sorafenib Resistance. Mol Imaging Biol 25, 1073–1083 (2023). https://doi.org/10.1007/s11307-023-01870-1

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  • DOI: https://doi.org/10.1007/s11307-023-01870-1

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