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Deep Radiomics-based Approach to the Diagnosis of Osteoporosis Using Hip Radiographs
Sangwook Kim1, Bo Ram Kim1, Hee-Dong Chae1
1Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (S.K., H.D.C., S.H.H., J.Y.C., H.J.Y.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (B.R.K.); Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (J.L.); Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea (S.J.Y.); Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea (D.H.K.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.J.Y., S.H.H., J.Y.C., H.J.Y.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.H.).
Purpose:
To develop and validate deep radiomics models for the diagnosis of osteoporosis using hip radiographs.
Materials And Methods:
A deep radiomics model was developed using 4924 hip radiographs from 4308 patients (3632 women; mean age, 62 years ± 13 [SD]) obtained between September 2009 and April 2020. Ten deep features, 16 texture features, and three clinical features were used to train the model. T score measured with dual-energy x-ray absorptiometry was used as a reference standard for osteoporosis. Seven deep radiomics models that combined different types of features were developed: clinical (model C); texture (model T); deep (model D); texture and clinical (model TC); deep and clinical (model DC); deep and texture (model DT); and deep, texture, and clinical features (model DTC). A total of 444 hip radiographs obtained between January 2019 and April 2020 from another institution were used for the external test. Six radiologists performed an observer performance test. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic performance.
Results:
For the external test set, model D (AUC, 0.92; 95% CI: 0.89, 0.95) demonstrated higher diagnostic performance than model T (AUC, 0.77; 95% CI: 0.70, 0.83; adjusted P < .001). Model DC (AUC, 0.95; 95% CI: 0.92, 0.97; adjusted P = .03) and model DTC (AUC, 0.95; 95% CI: 0.92, 0.97; adjusted P = .048) showed improved diagnostic performance compared with model D. When observer performance without and with the assistance of the model DTC prediction was compared, performance improved from a mean AUC of 0.77 to 0.87 (P = .002).
Conclusion:
Deep radiomics models using hip radiographs could be used to diagnose osteoporosis with high performance.Keywords: Skeletal-Appendicular, Hip, Absorptiometry/Bone Densitometry© RSNA, 2022.
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