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.
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.
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).
The radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC.
• 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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Clear cell renal carcinoma
Contrast-enhanced computed tomography
Checklist for AI in Medical Imaging
Digital imaging and communications in medicine
Electronic health records
Gray-level co-occurrence matrix
Gray-level dependence matrix
Gray-level run length matrix
Gray-level size zone matrix
Hepatitis B surface antigen
Hepatitis C antibody
Intraclass correlation coefficient
Magnetic resonance imaging
Neighboring gray tone difference matrix
Recursive feature elimination
Region of interest
Support vector machine
eXtreme Gradient Boosting
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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).
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.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
• Diagnostic or prognostic study
• Performed at one institution
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Electronic supplementary material
Details of the various features and filters (DOCX 13 kb)
The Checklist for AI in Medical Imaging (CLAIM) (DOCX 24 kb)
The list of the extracted features in our study (CSV 127 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
- Hepatocellular carcinoma
- Machine learning
- Neoplasm grading
- Multidetector computed tomography