Low-dose CT radiomics features-based neural networks predict lymphoma types

Abstract

Abstract Background Fluorodeoxyglucose positron emission tomography (PET)–computed tomography (CT) is preferred for pretreatment staging and treatment planning in patients with lymphoma. This study aims to train and validate the neural networks (NN) for predicting lymphoma types using low-dose CT radiomics. Results Few radiomics features were stable in intraclass correlation coefficient and coefficient of variation analysis (n = 119). High collinear ones with variance inflation factor were eliminated (n = 56). Twenty-four features were selected with the least absolute shrinkage and selection operator regression for network training. NN had 75.76% predictive accuracy in the validation set and has 0.73 (95% CI 0.55–0.91) area under the curve (AUC) to differentiate Hodgkin lymphoma from non-Hodgkin lymphoma. NN which was used to differentiate B-cell lymphoma from T-cell lymphoma had 78.79% predictive accuracy and has 0.81 (95% CI 0.63–0.99) AUC. Conclusions In this study, in which we used low-dose CT images of PET–CT scans, predictions of the neural network were near acceptable lower bound for Hodgkin and non-Hodgkin lymphoma discrimination, and B-cell and T-cell lymphoma differentiation.

Keywords

• Hodgkin lymphoma
• Non-Hodgkin lymphoma
• CT
• Radiomics analysis
• Neural network