How to Discriminate Between Primary Hepatic Lymphoma and Cholangiocarcinoma

Yasri, Sora; Wiwanitkit, Viroj

doi:10.5001/omj.2018.83

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…[16][17][18] For example, a study tried to use serum alkaline phosphatase isoenzyme electrophoresis to differentiate the 2 diseases. 19 To improve the radiological diagnosis capacity, texture analysis combining with ML is the most potential method waiting to be explored.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography

Zou

Zhao

et al. 2021

Technol Cancer Res Treat

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Purpose: This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcinoma (ICCA) and hepatic lymphoma (HL). Method: A total of 28 patients with HL and 101 patients with ICCA were included. A total of 45 texture features were extracted by the software LifeX from contrast-enhanced computer tomography (CECT) images and 38 of them were eligible. A total of 5 feature selection methods and 9 feature classification methods were used to build the best diagnostic models, combining with the 10-fold cross-validation to assess the accuracy of these models. The discriminative ability of each model was evaluated by receiver operating characteristic analysis. Result: A total of 45 predictive models were built by the cross combination of each selection and classification method to differentiate ICCA from HL. According to the results of test group, most of the models performed well with a large area under the curve (AUC) (>0.85) and high accuracy (>0.85). Random Forest (RF)_Linear Discriminant Analysis (LDA) (AUC = 0.997, accuracy = 0.969) was the best model among all the 45 models. Conclusion: Combining texture parameters from CECT with multiple machine learning models can differentiate ICCA and HL effectively, and RF_LDA performed the best in this process.

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