Abstract #1688

Radiomic Features Outperform Clinical Metrics in Distinguishing Femoroacetabular Impingement Patients from Healthy Subjects

Eros Montin^1,2, Richard Kijowski³, Thomas Youm⁴, 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, ³Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA, new york, NY, United States, ⁴Department of Orthopedic Surgery, New York University Grossman School of Medicine, New York, New York, USA, new york, NY, United States

Synopsis

Keywords: Whole Joint, Radiomics, femoroacetabular impingement, Radiomics, machine learning

Motivation: Radiomics could differentiate the symptomatic hip from the asymptomatic contralateral hip in patients with femoroacetabular impingement (FAI). This study investigates its potential in distinguishing FAI patients from healthy subjects.

Goal(s): To compare the diagnostic performance of radiomic features and clinical metrics in FAI diagnosis.

Approach: We used 3D Dixon MRI data (10 healthy subjects and 10 FAI patients). We trained machine learning models on radiomic features extracted from MRI to classify subjects as healthy or FAI. Models were trained also on clinical metrics for comparison.

Results: Radiomic features accurately identified FAI patients without errors (100% accuracy). Clinical metrics achieved 74% accuracy.

Impact: Radiomic features exhibited a remarkable diagnostic performance, accurately identifying all FAI patients and healthy subjects. This study shows the promise of radiomics to enable automated FAI diagnosis.

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