Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence
Motivation: Fluid and fat accumulation can be observed in MRI images of patients with PLEL; however, the microscopic characteristics of the different components of PLEL are currently unknown.
Goal(s): This study aimed to explore the MRI radiomics features of different components of subcutaneous soft tissues in patients with PLEL, such as simple fat, mixed fat and water, fat interstitial edema, and effusion
Approach: We propose a machine learning model to analyze the radiomics characteristics of different tissue components of lower extremity lymphedema in MRI.
Results: he four-class model, using 15 selected radiomics features, shows outstanding performance with an overall accuracy of 0.866.
Impact: The different components of subcutaneous soft tissues of PLEL patients, such as simple fat, mixed fat and water, adipose interstitial edema and effusion, have unique radiomic features.
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