Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Image Acquisition and BMD Assessment
2.3. TVCB Auto-Segmentation Framework and VOI Delineation
2.4. Radiomics Model Construction
2.4.1. Radiomics Features Extraction
2.4.2. Features Selection and Model Construction
2.5. Deep Learning Network Construction
2.6. Statistical Analysis
3. Results
3.1. Participant Demographics
3.2. Automatic Segmentation Model
3.3. The Comparison of the Rad Model and DL Model
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristic | Development Set | Temporal Validation Set | p-Value |
---|---|---|---|
All (n) | 333 | 101 | |
Male (n) | 170 | 57 | |
Female (n) | 163 | 44 | 0.404 |
All (years) | 62.89 ± 11.55 | 60.76 ± 10.41 | 0.098 |
Male (years) | 65.37 ± 10.37 | 62.60 ± 9.14 | 0.073 |
Female (years) | 60.30 ± 12.17 | 58.39 ± 11.54 | 0.350 |
Osteoporosis (n) | 84 | 20 | |
Osteopenia (n) | 134 | 34 | |
Normal BMD (n) | 115 | 47 | 0.094 |
Category | DSC | VD (cm3) |
---|---|---|
All | 0.96 ± 0.02 | 0.50 (0.17, 0.69) |
Osteoporosis | 0.97 ± 0.01 | 0.44 (0.09, 0.68) |
Osteopenia | 0.96 ± 0.02 | 0.53 (0.19, 0.63) |
Normal BMD | 0.96 ± 0.02 | 0.50 (0.18, 0.80) |
Model | Set | Category | AUC | 95%CI | Sensitivity (%) | Specificity (%) | Precision (%) | Accuracy (%) |
---|---|---|---|---|---|---|---|---|
Rad Model | Internal training set | Osteoporosis | 0.959 | 0.927–0.979 | 88.1 | 92.0 | 88.9 | |
Osteopenia | 0.881 | 0.835–0.917 | 90.7 | 75.5 | 67.3 | |||
Normal BMD | 0.977 | 0.95–0.991 | 84.8 | 98.9 | 96.3 | |||
Overall | 79.0 | |||||||
Internal test set | Osteoporosis | 0.919 | 0.826–0.971 | 88.2 | 86.0 | 71.4 | ||
Osteopenia | 0.873 | 0.769–0.942 | 81.5 | 85.0 | 60.0 | |||
Normal BMD | 0.976 | 0.906–0.998 | 100.0 | 93.2 | 90.0 | |||
Overall | 70.2 | |||||||
Temporal validation set | Osteoporosis | 0.943 | 0.878–0.979 | 90.0 | 90.1 | 85.7 | ||
Osteopenia | 0.801 | 0.709–0.874 | 82.4 | 67.2 | 58.1 | |||
Normal BMD | 0.932 | 0.864–0.972 | 93.6 | 85.2 | 84.3 | |||
Overall | 73.3 | |||||||
DL Model | Internal training set | Osteoporosis | 0.975 | 0.948–0.990 | 95.5 | 96.5 | 87.7 | |
Osteopenia | 0.936 | 0.900–0.962 | 89.7 | 95.6 | 93.2 | |||
Normal BMD | 0.972 | 0.944–0.988 | 96.7 | 94.8 | 95.6 | |||
Overall | 92.5 | |||||||
Internal test set | Osteoporosis | 0.942 | 0.857–0.985 | 100.0 | 76.0 | 75.0 | ||
Osteopenia | 0.866 | 0.760–0.937 | 74.1 | 85.0 | 71.4 | |||
Normal BMD | 0.972 | 0.900–0.997 | 100.0 | 90.9 | 87.0 | |||
Overall | 77.6 | |||||||
Temporal validation set | Osteoporosis | 0.983 | 0.935–0.998 | 100.0 | 92.6 | 84.2 | ||
Osteopenia | 0.906 | 0.831–0.955 | 85.3 | 80.6 | 68.3 | |||
Normal BMD | 0.969 | 0.914–0.993 | 95.7 | 85.2 | 92.7 | |||
Overall | 81.2 |
Authors(methods) | Key Findings | AUC | Sensitivity (%) | Specificity (%) | Accuracy (%) |
---|---|---|---|---|---|
Xue et al. (Radiomics) [23] | Detecting abnormal BMD | 0.944 | 95.8 | - | - |
Detecting osteoporosis | 0.866 | 83.3 | - | - | |
Chen et al. (Radiomics) [24] | Detecting abnormal BMD | 0.960 | 93.0 | 89.0 | 91.0 |
Detecting osteoporosis | 0.980 | 95.0 | 93.0 | 94.0 | |
Wang et al. (Radiomics) [25] | Detecting osteoporosis | 0.914 | 90.7 | 75.0 | 89.8 |
Ours (Radiomics) | Detecting abnormal BMD | 0.932 | 93.6 | 85.2 | 73.3 |
Detecting osteopenia | 0.801 | 82.4 | 67.5 | ||
Detecting osteoporosis | 0.943 | 90.0 | 90.1 | ||
Yang et al. (Deep learning) [5] | Detecting osteopenia | 0.831 | 73.6 | 80.5 | - |
Detecting osteoporosis | 0.972 | 95.6 | 88.0 | ||
Ours (Deep learning) | Detecting abnormal BMD | 0.969 | 95.7 | 85.2 | 81.2 |
Detecting Osteopenia | 0.906 | 85.3 | 80.6 | ||
Detecting osteoporosis | 0.983 | 100 | 92.6 |
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Tong, X.; Wang, S.; Zhang, J.; Fan, Y.; Liu, Y.; Wei, W. Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images. Bioengineering 2024, 11, 50. https://doi.org/10.3390/bioengineering11010050
Tong X, Wang S, Zhang J, Fan Y, Liu Y, Wei W. Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images. Bioengineering. 2024; 11(1):50. https://doi.org/10.3390/bioengineering11010050
Chicago/Turabian StyleTong, Xiaoyu, Shigeng Wang, Jingyi Zhang, Yong Fan, Yijun Liu, and Wei Wei. 2024. "Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images" Bioengineering 11, no. 1: 50. https://doi.org/10.3390/bioengineering11010050
APA StyleTong, X., Wang, S., Zhang, J., Fan, Y., Liu, Y., & Wei, W. (2024). Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images. Bioengineering, 11(1), 50. https://doi.org/10.3390/bioengineering11010050