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Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purposes

To develop and externally validate a multiphase computed tomography (CT)-based machine learning (ML) model for staging liver fibrosis (LF) by using whole liver slices.

Materials and methods

The development dataset comprised 232 patients with pathological analysis for LF, and the test dataset comprised 100 patients from an independent outside institution. Feature extraction was performed based on the precontrast (PCP), arterial (AP), portal vein (PVP) phase, and three-phase CT images. CatBoost was utilized for ML model investigation by using the features with good reproducibility. The diagnostic performance of ML models based on each single- and three-phase CT image was compared with that of radiologists’ interpretations, the aminotransferase-to-platelet ratio index, and the fibrosis index based on four factors (FIB-4) by using the receiver operating characteristic curve with the area under the curve (AUC) value.

Results

Although the ML model based on three-phase CT image (AUC = 0.65–0.80) achieved higher AUC value than that based on PCP (AUC = 0.56–0.69) and PVP (AUC = 0.51–0.74) in predicting various stage of LF, significant difference was not found. The best CT-based ML model (AUC = 0.65–0.80) outperformed the FIB-4 in differentiating advanced LF and cirrhosis and radiologists’ interpretation (AUC = 0.50–0.76) in the diagnosis of significant and advanced LF.

Conclusion

All PCP, PVP, and three-phase CT-based ML models can be an acceptable in assessing LF, and the performance of the PCP-based ML model is comparable to that of the enhanced CT image-based ML model.

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Funding

This work supported by the Opening Research Fund of Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation (Grant Number: 201905010003).

Author information

Author notes

  1. Enming Cui and Wansheng Long have contributed equally to this study.

Authors and Affiliations

  1. Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, Jiangmen Clinical Medical School of Guangdong Medical University, 23 Beijie Haibang Street, Jiangmen, 529030, People’s Republic of China

    Enming Cui, Wansheng Long & Changyi Ma

  2. Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation, Guangzhou, People’s Republic of China

    Enming Cui

  3. Department of Hepatobiliary Surgery, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, People’s Republic of China

    Juanhua Wu

  4. Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, People’s Republic of China

    Qing Li

  5. Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, 3002 SunGangXi Road, Shenzhen, 518035, People’s Republic of China

    Yi Lei & Fan Lin

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  1. Enming Cui
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Correspondence to Fan Lin.

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Cui, E., Long, W., Wu, J. et al. Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features. Abdom Radiol 46, 3866–3876 (2021). https://doi.org/10.1007/s00261-021-03051-6

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  • DOI: https://doi.org/10.1007/s00261-021-03051-6

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