Keywords: Diagnosis/Prediction, Radiomics
Motivation: At present, there is no application of radiomics in the diagnosis of the severity of primary lower limb lymphedema(PLE).
Goal(s): Develop machine learning models to predict severe PLE.
Approach: 119 patients were divided into non severe group (mild, moderate) and severe group. Used five commonly used machine learning models: Logistic Regression, Support Vector Machine, RandomForest, ExtraTrees, and Light Gradient Boosting Machine.
Results: The ExtraTree model performed the best in the test set, with an AUC of 0.938, sensitivity of 75%, and specificity of 100%. The net benefit of the ExtraTree model was greater than that of the two extreme curves.
Impact: All five models performed well in distinguishing between the nonsevere group and the severe group. NCMRI based machine learning radiomics signature can predict the severity of primary lower limb lymphedema.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords