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Noninvasive identification of SOX9 status using radiomics signatures may help construct personalized treatment strategy in hepatocellular carcinoma

  • Hepatobiliary
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop and validate a radiomics-based model for predicting SOX9-positive hepatocellular carcinoma (HCC) using preoperative contrast-enhanced computed tomography (CT) images.

Methods

From January 2013 to April 2017, patients with histologically proven HCC who received systemic sorafenib treatment after curative resection were retrospectively enrolled. Radiomic features were extracted from portal venous phase CT images and selected to build a radiomics score using logistic regression analysis. The factors associated with SOX9 expression were selected and combined by univariate and multivariate analyses to establish clinico-liver imaging (CL) model and clinico-liver imaging-radiomics (CLR) model. Diagnostic performance was measured by area under curve (AUC). Overall survival (OS) and recurrence-free survival (RFS) rates were compared using Kaplan-Meier method.

Results

A total of 108 patients (training cohort: n = 80; validation cohort: n = 28) were enrolled. Multivariate analyses revealed that the albumin-bilirubin grade and tumor size were significant independent factors for predicting SOX9-positive HCCs and were included in the CL model. The CLR model integrating the radiomics score with albumin-bilirubin grade and tumor size showed better discriminative performance than the CL model with AUCs of 0.912 and 0.790 in the training and validation cohorts. Survival curves for RFS and OS showed that SOX9 expression was closely related to the prognosis of HCC patients. RFS and OS rates were significantly lower in patients with SOX9-positive than SOX9-negative (51.02% vs. 75.00% at 1-year RFS rates; 76.92% vs. 94.94% at 2-year OS rates).

Conclusion

Radiomics signatures may serve as noninvasive predictors for SOX9 status evaluation in patients with HCC and may aid in constructing individualized treatment strategies.

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Data availability

The datasets of the current study would be available from the corresponding author on reasonable request.

Abbreviations

HCC:

Hepatocellular carcinoma

CT:

Computed tomography

CECT:

Contrast-enhanced computed tomography

OS:

Overall survival

RFS:

Recurrence-free survival

SOX:

Sex-determining region Y box

AUC:

Area under curve

ROC:

Receiver operating characteristics.

CSCs:

Cancer stem cells

MVI:

Microvascular invasion

ICIs:

Immune checkpoint inhibitors

CAR-T:

Chimeric antigen receptor-modified T cell

TACE:

Transcatheter arterial chemoembolization

MVI:

Microvascular invasion

VOI:

Volumes of interest

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

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Funding

This work was supported by the Science and Technology Support Program of Sichuan Province (Grant No. 2021YFS0021); China Postdoctoral Science Foundation (Grant No. 2021M692289); Post-Doctor Research Project, West China Hospital, Sichuan University (Grant No. 2020HXBH130); and National Natural Science Foundation of China (Grant No. 81971571).

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Author notes

  1. Feng Che and Yi Wei have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, West China Hospital, Sichuan University, No 37, Guoxue Alley, Chengdu, 610041, Sichuan, China

    Feng Che, Yi Wei, Qian Li, Tong Zhang, Fang Yuan & Bin Song

  2. Institute of Clinical Pathology, Key Laboratory of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Qing Xu

  3. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    Li-Ye Wang & Man Li

  4. Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China

    Bin Song

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Contributions

Conceptualization: FC and YW. Data curation: FC, YW, QX and ML. Formal analysis: FC, YW, QL, TZ and LW. Funding acquisition: YW and BS. Methodology: FC, YW and QL. Project administration: BS. Supervision: BS. Validation: FC, YW, TZ, LW and FY. Visualization: FC and QL. Writing-original draft: FC. Writing-review & editing: YW and QL.

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Correspondence to Fang Yuan or Bin Song.

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Che, F., Wei, Y., Xu, Q. et al. Noninvasive identification of SOX9 status using radiomics signatures may help construct personalized treatment strategy in hepatocellular carcinoma. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04190-2

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