Classification of Anterior Mediastinal Tumors with Dynamic Contrast Enhanced Magnetic Resonance Imaging and Machine Learning Methods

Abstract

In current clinical and imaging exams of anterior mediastinal tumors one existing challenge exists in the differentiation of thymic tumors and lymphoma Our aim is to quantitatively classify anterior mediastinal tumor with image-derived parameters and classification models to assist doctors in clinical diagnosis In this study we used the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) as the main imaging modality Moreover we used three different compartment models to analyze the perfusion parameters from DCE-MRI data to evaluate the anterior mediastinal tumors Method: In this study we collected data from fifty patients including imaging and clinical history from the National Cheng Kung University Hospital Biopsy or surgical pathology found 13 of them with lymphoma and 37 of them with the thymic tumor We drew the volume of interest (VOI) and the arterial input function (AIF) with our in-house software and converted the image intensity into the concentration of the contrast agent (CA) in the tissue Moreover we use three different compartment models Tofts Kermode (TK) model Extended Tofts Kermode (ETK) model and two-compartment exchange (2CXM) model to calculate the perfusion parameters We used the receiver operating characteristic (ROC) curve and three different machine learning methods including decision trees support vector machine (SVM) and K-nearest neighbor (KNN) Finally we evaluated the differences in the perfusion parameters of three compartment models and compared the classification results of different machine learning algorithms Result: We make the process of DCE-MRI data analysis transparent with our in-house software Moreover we classified lymphomas and thymic tumors by our decision tree models and eventually reached 80% on accuracy Furthermore the accuracy sensitivity and specificity of the result of classifying thymomas and thymic carcinomas can all surpass 92% Conclusion: Our software can analyze the DCE-MRI data with the two-compartments model and calculate the perfusion parameters We used the perfusion parameters to train the decision tree classification model which can classify the anterior mediastinal tumors We will add more tumor data in the future to optimize the classification model provide a reliable computer-assisted diagnosis in the clinical setting

Date of Award	2020
Original language	English
Supervisor	Jia-Jin Chen (Supervisor)