Radiomics models for diagnosing microvascular invasion in hepatocellular carcinoma: which model is the best model?

Cancer Imaging. 2019 Aug 28;19(1):60. doi: 10.1186/s40644-019-0249-x.


Objectives: To explore the feasibility of diagnosing microvascular invasion (MVI) with radiomics, to compare the diagnostic performance of different models established by each method, and to determine the best diagnostic model based on radiomics.

Methods: A retrospective analysis was conducted with 206 cases of hepatocellular carcinoma (HCC) confirmed through surgery and pathology in our hospital from June 2015 to September 2018. Among the samples, 88 were MVI-positive, and 118 were MVI-negative. The radiomics analysis process included tumor segmentation, feature extraction, data preprocessing, dimensionality reduction, modeling and model evaluation.

Results: A total of 1044 sets of texture feature parameters were extracted, and 21 methods were used for the radiomics analysis. All research methods could be used to diagnose MVI. Of all the methods, the LASSO+GBDT method had the highest accuracy, the LASSO+RF method had the highest sensitivity, the LASSO+BPNet method had the highest specificity, and the LASSO+GBDT method had the highest AUC. Through Z-tests of the AUCs, LASSO+GBDT, LASSO+K-NN, LASSO+RF, PCA + DT, and PCA + RF had Z-values greater than 1.96 (p<0.05). The DCA results showed that the LASSO + GBDT method was better than the other methods when the threshold probability was greater than 0.22.

Conclusions: Radiomics can be used for the preoperative, noninvasive diagnosis of MVI, but different dimensionality reduction and modeling methods will affect the diagnostic performance of the final model. The model established with the LASSO+GBDT method had the optimal diagnostic performance and the greatest diagnostic value for MVI.

Keywords: Hepatocellular carcinoma; Microvessel; Neoplasm invasiveness; Radiomics.

Publication types

  • Comparative Study

MeSH terms

  • Carcinoma, Hepatocellular / diagnostic imaging*
  • Carcinoma, Hepatocellular / pathology
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Liver Neoplasms / diagnostic imaging*
  • Liver Neoplasms / pathology
  • Male
  • Middle Aged
  • Neoplasm Invasiveness
  • Positron-Emission Tomography / methods*