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



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.

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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Alanine aminotransferase


Arterial phase


Aspartate aminotransferase


Clear cell renal carcinoma


Contrast-enhanced computed tomography


Confidence interval


Checklist for AI in Medical Imaging


Digital imaging and communications in medicine


Electronic health records




Gamma-glutamyl transpeptidase


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


Liver transplantation


Magnetic resonance imaging


Microwave ablation


Neighboring gray tone difference matrix


Radiofrequency ablation


Recursive feature elimination


Region of interest


Support vector machine


Venous phase


eXtreme Gradient Boosting


  1. 1.

    European Association for the Study of the Liver (2018) EASL clinical practice guidelines: management of hepatocellular carcinoma. J. Hepatol. 69(1):182–236

  2. 2.

    Njei B, Rotman Y, Ditah I, Lim JK (2015) Emerging trends in hepatocellular carcinoma incidence and mortality. Hepatology 61(1):191–199

    Article  PubMed  Google Scholar 

  3. 3.

    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424

    Article  Google Scholar 

  4. 4.

    Lencioni R, Crocetti L (2012) Local-regional treatment of hepatocellular carcinoma. Radiology 262(1):43–58

    Article  PubMed  Google Scholar 

  5. 5.

    Forner A, Reig M, Bruix J (2018) Hepatocellular carcinoma. Lancet 391(10127):1301–1314

    Article  PubMed  Google Scholar 

  6. 6.

    Wang Y-Y, Zhong J-H, Su Z-Y et al (2016) Albumin-bilirubin versus Child-Pugh score as a predictor of outcome after liver resection for hepatocellular carcinoma. Br J Surg 103(6):725–734

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Sasaki A, Kai S, Iwashita Y, Hirano S, Ohta M, Kitano S (2005) Microsatellite distribution and indication for locoregional therapy in small hepatocellular carcinoma. Cancer 103(2):299–306

    Article  PubMed  Google Scholar 

  8. 8.

    Weiss LM, Medeiros LJ, Vickery AL (1989) Pathologic features of prognostic significance in adrenocortical carcinoma. Am J Surg Pathol 13(3):202–206

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Martins-Filho SN, Paiva C, Azevedo RS, Alves VAF (2017) Histological grading of hepatocellular carcinoma-a systematic review of literature. Front Med (Lausanne) 4:193

    Article  Google Scholar 

  10. 10.

    Bruix J, Sherman M (2005) Management of hepatocellular carcinoma. Hepatology 42(5):1208–1236

    Article  PubMed  Google Scholar 

  11. 11.

    Llovet JM, Zucman-Rossi J, Pikarsky E et al (2016) Hepatocellular carcinoma. Nat Rev Dis Primers 2:16018

    Article  PubMed  Google Scholar 

  12. 12.

    Robert M, Sofair AN, Thomas A et al (2009) A comparison of hepatopathologists’ and community pathologists' review of liver biopsy specimens from patients with hepatitis C. Clin Gastroenterol Hepatol 7(3):335–338

    Article  Google Scholar 

  13. 13.

    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

    Article  Google Scholar 

  14. 14.

    Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281(3):947–957

    Article  Google Scholar 

  15. 15.

    Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D (2018) Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 28(11):4514–4523

    Article  Google Scholar 

  16. 16.

    Jin X, Zheng X, Chen D et al (2019) Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. Eur Radiol 29(11):6080–6088

    Article  Google Scholar 

  17. 17.

    Hennedige T, Venkatesh SK (2013) Imaging of hepatocellular carcinoma: diagnosis, staging and treatment monitoring. Cancer Imaging 12:530–547

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Oh J, Lee JM, Park J et al (2019) Hepatocellular carcinoma: texture analysis of preoperative computed tomography images can provide markers of tumor grade and disease-free survival. Korean J Radiol 20(4):569–579

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Wu M, Tan H, Gao F et al (2019) Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol 29(6):2802–2811

    Article  Google Scholar 

  20. 20.

    Willemink MJ, Koszek WA, Hardell C et al (2020) Preparing medical imaging data for machine learning. Radiology 295(1):4–15

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Edmondson HA, Steiner PE (1954) Primary carcinoma of the liver: a study of 100 cases among 48,900 necropsies. Cancer 7(3):462–503

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128

    Article  PubMed  Google Scholar 

  23. 23.

    van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21):e104–e107

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Leijenaar RTH, Nalbantov G, Carvalho S et al (2015) The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep 5:11075

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    E L, Lu L, Li L, Yang H, Schwartz LH, Zhao B (2019) Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography. Acad Radiol 26(9):1245–1252

    Article  PubMed  Google Scholar 

  26. 26.

    Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene Selection for Cancer Classification using Support Vector Machines. Mach Learn 46(1/3):389–422

  27. 27.

    Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15(2):155–163

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Leijenaar RTH, Carvalho S, Velazquez ER et al (2013) Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 52(7):1391–1397

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Bektas CT, Kocak B, Yardimci AH et al (2019) Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade. Eur Radiol 29(3):1153–1163

    Article  PubMed  Google Scholar 

  30. 30.

    Singh A (2019) Foundations of machine learning. SSRN J.

  31. 31.

    Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Krishnapuram B, Shah M, Smola A, Aggarwal C, Shen D, Rastogi R (eds) Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 785–794

    Chapter  Google Scholar 

  32. 32.

    Akinkunmi M (2019) Introduction to statistics using R. Morgan & Claypool, San Rafael California

    Book  Google Scholar 

  33. 33.

    Mongan J, Moy L, Kahn CE (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2(2):e200029

    Article  Google Scholar 

  34. 34.

    Zhou W, Zhang L, Wang K et al (2017) Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging 45(5):1476–1484

    Article  Google Scholar 

  35. 35.

    Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Sun P, Wang D, Mok VC, Shi L (2019) Comparison of feature selection methods and machine learning classifiers for radiomics analysis in glioma grading. IEEE Access 7:102010–102020

    Article  Google Scholar 

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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).

Author information



Corresponding author

Correspondence to Jingdong Ma.

Ethics declarations


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.


• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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


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)

High resolution image (TIF 5273 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).

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  • Hepatocellular carcinoma
  • Machine learning
  • Neoplasm grading
  • Multidetector computed tomography
  • Biomarkers