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Updated: Aug 9, 2026
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
Differentiating HCC from ICC and prediction of ICC grade based on MRI deep-radiomics: Using lesions and their
Shuping Wang1, Xuehu Wang2, Xiaoping Yin3
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.
Purpose:
This study aimed to evaluate the ability of MRI-based intratumoral and peritumoral radiomics features of liver tumors to differentiate between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) and to predict ICC differentiation.
Methods:
This study retrospectively collected 87 HCC patients and 75 ICC patients who were confirmed pathologically. The standard region of interest (ROI) of the lesion drawn by the radiologist manually shrank inward and expanded outward to form multiple ROI extended regions. A three-step feature selection method was used to select important radiomics features and convolution features from extended regions. The predictive performance of several machine learning classifiers on dominant feature sets was compared. The extended region performance was assessed by area under the curve (AUC), specificity, sensitivity, F1-score and accuracy.
Results:
The performance of the model is further improved by incorporating convolution features. Compared with the standard ROI, the extended region obtained better prediction performance, among which 6 mm extended region had the best prediction ability (Classification: AUC = 0.96, F1-score = 0.94, Accuracy: 0.94; Grading: AUC = 0.94, F1-score = 0.93, Accuracy = 0.89).
Conclusion:
Larger extended region and fusion features can improve tumor predictive performance and have potential value in tumor radiology.
