Abstract #1690

Predicting Total Knee Replacement Surgery Using Radiomic Features Extracted from MRI Scans

Eros Montin^1,2, Ozkan Cigdem^1,2, Haresh Rajamohan³, Kyunghyun Cho³, Richard Kijowski⁴, Cem Deniz^1,2, and Riccardo Lattanzi^1,2

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology,, New York University Grossman School of Medicine, New York, New York, USA, new york, NY, United States, ²Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA, new york, NY, United States, ³Center of Data Science, New York University, New York, NY, United States, new york, NY, United States, ⁴Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA, new york, NY, United States

Synopsis

Keywords: Cartilage, Joints, knee, joints, mri, radiomic

Motivation: Predicting total knee replacement surgery can help patients and healthcare providers make informed treatment decisions.

Goal(s): To develop a machine learning model for predicting patients that will undergo a total knee replacement using radiomics.

Approach: To extract radiomic features from images of patients and healthy subjects and train different machine learning models to predict patients' outcomes.

Results: The best model achieved an accuracy of 87.2%. Three out of the four most significant radiomic features selected in the All subset were derived from the meniscus areas, suggesting that the meniscus may play a crucial role in predicting patient outcomes.

Impact: Radiomic features from MRI scans effectively classify TKR-positive patients, particularly those incorporating meniscus features. These models potentially can predict patient outcomes and guide treatment decisions, but further research is needed to enhance performance and validate findings in broader patient populations.

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.

Click here for more information on becoming a member.