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Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To investigate the efficacy of contrast-enhanced computed tomography (CECT)–based radiomics signatures for preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning.

Methods

In this single-center retrospective study, data collected from 297 consecutive subjects with HCC were allocated to training dataset (n = 237) and test dataset (n = 60). Manual segmentation of lesion sites was performed with ITK-SNAP, the radiomics features were extracted by the Pyradiomics, and radiomics signatures were synthesized using recursive feature elimination (RFE) method. The prediction models for pathological grading of HCC were established by using eXtreme Gradient Boosting (XGBoost). The performance of the models was evaluated using the AUC along with 95% confidence intervals (CIs) and standard deviation, sensitivity, specificity, and accuracy.

Results

The radiomics signatures were found highly efficient for machine learning to differentiate high-grade HCC from low-grade HCC. For the clinical factors, when they were merely applied to train a machine learning model, the model achieved an AUC of 0.6698, along with 95% CI and standard deviation of 0.5307–0.8089 and 0.0710, respectively (sensitivity, 0.6522; specificity, 0.4595; accuracy, 0.5333). Meanwhile, when the radiomics signatures were applied in association with clinical factors to train a machine learning model, the performance of the model remarkably increased with AUC of 0.8014, along with 95% CI and standard deviation of 0.6899–0.9129 and 0.0569, respectively (sensitivity, 0.6522; specificity, 0.7297; accuracy, 0.7000).

Conclusions

The radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC.

Key Points

• The radiomics signatures may non-invasively explore the underlying association between CECT images and pathological grades of HCC via machine learning.

• The radiomics signatures of CECT images may enhance the prediction performance of pathological grading of HCC, and further validation is required.

• The features extracted from arterial phase CECT images may be more reliable than venous phase CECT images for predicting pathological grades of HCC.

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Abbreviations

3D:

Three-dimensional

AFP:

Alpha-fetoprotein

ALT:

Alanine aminotransferase

AP:

Arterial phase

AST:

Aspartate aminotransferase

cc-RCC:

Clear cell renal carcinoma

CECT:

Contrast-enhanced computed tomography

CI:

Confidence interval

CLAIM:

Checklist for AI in Medical Imaging

DICOM:

Digital imaging and communications in medicine

EHR:

Electronic health records

ES:

Edmondson-Steiner

GGT:

Gamma-glutamyl transpeptidase

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

HBsAg:

Hepatitis B surface antigen

HCVab:

Hepatitis C antibody

ICC:

Intraclass correlation coefficient

LT:

Liver transplantation

MRI:

Magnetic resonance imaging

MWA:

Microwave ablation

NGTDM:

Neighboring gray tone difference matrix

RFA:

Radiofrequency ablation

RFE:

Recursive feature elimination

ROI:

Region of interest

SVM:

Support vector machine

VP:

Venous phase

XGBoost:

eXtreme Gradient Boosting

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Funding

The study was funded by the National Natural Science Foundation of China (NO. 71974065) and Key R & D and promotion projects in Henan Province (NO. 182400410172).

Author information

Authors and Affiliations

  1. School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Qiaokou District, Wuhan, 430030, Hubei, China

    Bing Mao, Feng Ding & Jingdong Ma

  2. Zhengzhou University People’s Hospital, Zhengzhou, Henan, China

    Lianzhong Zhang & Peigang Ning

  3. DHC Mediway Technology Co., Ltd, Beijing, China

    Fatian Wu

  4. Dassault Systems, 175 Wyman St., Waltham, MA, USA

    Gary Lu

  5. Huiying Medical Technology (Beijing) Co., Ltd, Beijing, China

    Yayuan Geng

Authors
  1. Bing Mao
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  2. Lianzhong Zhang
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  4. Feng Ding
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  5. Fatian Wu
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  8. Jingdong Ma
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Corresponding author

Correspondence to Jingdong Ma.

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Guarantor

The scientific guarantor of this publication is Jingdong Ma.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Electronic supplementary material

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Details of the various features and filters (DOCX 13 kb)

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The Checklist for AI in Medical Imaging (CLAIM) (DOCX 24 kb)

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The list of the extracted features in our study (CSV 127 kb)

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The results of feature selection and feature importance. The results of feature selection of AP images, VP images, clinical factors, AP-VP images, combined clinical factors and AP images, combined clinical factors and VP images, combined clinical factors and AP-VP images are shown in an order from A to G, respectively.5.The ROC curves with confidence intervalof AP images, VP images, clinical factors, AP-VP images, combined clinical factors and AP images, combined clinical factors and VP images are shown in an order from A to F, respectively. (PNG 6385 kb)

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Mao, B., Zhang, L., Ning, P. et al. Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics. Eur Radiol 30, 6924–6932 (2020). https://doi.org/10.1007/s00330-020-07056-5

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  • DOI: https://doi.org/10.1007/s00330-020-07056-5

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