Dataset Information

Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density.

ABSTRACT:

Objective

This study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images.

Methods

A total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31-94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1-L4 vertebrae on the CT images were automatically segmented using SenseCare and defined as regions of interest (ROIs). In total, 1,197 radiomic features were extracted from these ROIs using PyRadiomics. The most significant features were selected using logistic regression and Pearson correlation coefficient matrices. Using these features, we constructed three linear classification models based on the random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms, respectively. The training and test sets were repeatedly selected using fivefold cross-validation. The model performance was evaluated using the area under the receiver operator characteristic curve (AUC) and confusion matrix.

Results

The classification model based on RF had the highest performance, with an AUC of 0.994 (95% confidence interval [CI]: 0.979-1.00) for differentiating normal BMD and osteoporosis, 0.866 (95% CI: 0.779-0.954) for osteopenia versus osteoporosis, and 0.940 (95% CI: 0.891-0.989) for normal BMD versus osteopenia.

Conclusions

The excellent performance of this radiomic model indicates that lumbar spine CT images can effectively be used to identify osteoporosis and as a tool for opportunistic osteoporosis screening.

SUBMITTER: Xue Z

PROVIDER: S-EPMC8991484 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density.

Xue Zhihao Z Huo Jiayu J Sun Xiaojiang X Sun Xuzhou X Ai Song Tao ST LichiZhang Liu Chenglei C

BMC musculoskeletal disorders 20220408 1

<h4>Objective</h4>This study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images.<h4>Methods</h4>A total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31-94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1-L4 vertebrae on the CT images were automatically segmented using SenseCare an ...[more]

PMID: 35395769

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Dataset Information

Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density.

Objective

Methods

Results

Conclusions

Publications

Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density.

Similar Datasets

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