A machine learning based on CT radiomics signature and change value features for predicting the risk classification of thymoma

Abstract

Abstract Objective: The aim of this study is to propose a medical imaging and comprehensive stacking learning based method for predicting high and low risk categories of thymoma. Methods: This retrospective study collected 126 patients with thymoma and 5 patients with thymic carcinoma treated at our institution, including 65 low-risk cases and 66 high-risk cases. Among them 78 cases were the training cohort. The rest formed the validation cohort (53 cases). Radiomicsfeatures and variation features are extracted from collected medical imaging data. Mann-Whitney U-test was used to identify and determine potential differences between categories and features with p<0.05 were retained. Feature selection was first performed using LASSO regression, and then the top ten features with the highest potential for differentiation were selected using the SelectKBest method. By applying stacked ensemble learning, we combine three machine learning algorithms to provide an efficient and reliable solution for risk prediction of thymoma. Results: A total of 54 features were identified as the most discriminative features for low-risk and high-risk thymoma, and were used to develop radiomics features. Our model successfully identified patients with low-risk and high-risk thymoma. For the imaging omics model, the AUC in the training and validation cohorts were 0.999 (95%CI,0.988-1.000) and 0.967(95%CI,0.916-1.000). For the nomogram, the values were 0.999 (95%CI,0.996-1.000) and 0.983 (95%CI,0.990-1.000). Conclusion: This study describes the application of CT based radiomics in thymoma patients and proposes a clinical decision nomogram that can be used to predict the risk of thymoma. This nomogram is advantageous for clinical decision-making concerning thymoma patients.