Radiomics for classification of bone mineral loss: A machine learning study

Rastegar, S. and Vaziri, M. and Qasempour, Y. and Akhash, M.R. and Abdalvand, N. and Shiri, I. and Abdollahi, H. and Zaidi, H. (2020) Radiomics for classification of bone mineral loss: A machine learning study. Diagnostic and Interventional Imaging, 101 (9). pp. 599-610.

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Purpose: The purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches. Materials and methods: A total of 147 patients were included in this retrospective single-center study. There were 12 men and 135 women with a mean age of 56.88 ± 10.6 (SD) years (range: 28�87 years). For each patient, seven regions including four lumbar and three femoral including trochanteric, intertrochanteric and neck were segmented on bone mineral densitometry images and 54 texture features were extracted from the regions. The performance of four feature selection methods, including classifier attribute evaluation (CLAE), one rule attribute evaluation (ORAE), gain ratio attribute evaluation (GRAE) and principal components analysis (PRCA) along with four classification methods, including random forest (RF), random committee (RC), K-nearest neighbor (KN) and logit-boost (LB) were evaluated. Four classification categories, including osteopenia vs. normal, osteoporosis vs. normal, osteopenia vs. osteoporosis and osteoporosis + osteopenia vs. osteoporosis were examined for the defined seven regions. The classification model performances were evaluated using the area under the receiver operator characteristic curve (AUC). Results: The AUC values ranged from 0.50 to 0.78. The combination of methods RF + CLAE, RF + ORAE and RC + ORAE yielded highest performance (AUC = 0.78) in discriminating between osteoporosis and normal state in the trochanteric region. The combinations of RF + PRCA and LB + PRCA had the highest performance (AUC = 0.76) in discriminating between osteoporosis and normal state in the neck region. Conclusion: The machine learning radiomic approach can be considered as a new method for bone mineral deficiency disease classification using bone mineral densitometry image features. © 2020 Société française de radiologie

Item Type: Article
Additional Information: cited By 12
Uncontrolled Keywords: adult; aged; Article; bone densitometry; bone density; bone mineral; controlled study; disease classification; feature extraction; feature selection; female; femoral neck; femur; hip radiography; human; image segmentation; k nearest neighbor; lumbar region; machine learning; major clinical study; male; osteopenia; osteoporosis; principal component analysis; radiomics; random forest; retrospective study
Subjects: WE Musculoskeletal System
Depositing User: eprints admin
Date Deposited: 02 Jun 2021 05:13
Last Modified: 02 Jun 2021 05:13

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