Staging of Primary Lymphedema Based on Radiomics Features from Subcutaneous Tissues in Lower Extremity MRI

Motivation: Traditional imaging-based staging methods for PLEL rely on subjective assessments by medical professionals and often struggle to capture the micro-level changes associated with lymphedema, which limits the accuracy and granularity of staging. Goal(s): To explore the value of radiomicss features based on different components extracted from MRI for assessing the staging of PLEL. Approach: We proposed a machine learning model to integrate multi-region radiomics for automated PLEL staging and employed deep learning for automated subcutaneous tissue segmentation in the lower extremity MRI. Results: The Dice coefficient for subcutaneous tissue segmentation scored 0.92, and the AUC for lymphedema staging was 0.821. Impact: The machine learning model based on radiomics in this study shows promising potential and value in lymphedema staging, which is expected to reduce subjective variability and potentially eliminate the need for clinical assays, thus enhancing its clinical applicability.