What Causes the Discrepancy between Quantitative Computed Tomography and Dual Energy X-Ray Absorptiometry?

Yoon, Hong In; Kim, Jung Ho; Ryu, Dong Jin; Yoon, Seung-Hwan

doi:10.21129/nerve.2021.7.2.64

Cited by 4 publications

References 40 publications

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Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening

Oh,

Kang,

Park

et al. 2024

Sci Rep

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To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson’s correlation and intraclass correlation. Receiver operating characteristic curve (ROC) analysis identified the diagnostic ability of DL-BMD for low BMD and osteoporosis, determined by dual-energy X-ray absorptiometry (DXA) and m-BMD. Excellent concordance were seen between m-BMD and DL-BMD in total CT scans (r = 0.961/0.979). The ROC-derived AUC of DL-BMD compared to that of central DXA for the low-BMD and osteoporosis patients was 0.847 and 0.770 respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to central DXA for low BMD were 75.0%, 75.0%, and 75.0%, respectively, and those for osteoporosis were 68.0%, 80.5%, and 77.7%. The AUC of DL-BMD compared to the m-BMD for low BMD and osteoporosis diagnosis were 0.990 and 0.943, respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to m-BMD for low BMD were 95.5%, 93.5%, and 94.6%, and those for osteoporosis were 88.2%, 94.5%, and 92.9%, respectively. DL-BMD exhibited excellent agreement with m-BMD on L1 and L2 vertebrae in the various routine clinical CT scans and had comparable diagnostic performance for detecting the low-BMD and osteoporosis on conventional QCT.

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Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening

Oh,

Kang,

Park

et al. 2024

Sci Rep

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Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners

Park,

Kang,

Woo

et al. 2024

Sci Rep

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This retrospective study examined the diagnostic efficacy of automated deep learning-based bone mineral density (DL-BMD) measurements for osteoporosis screening using 422 CT datasets from four vendors in two medical centers, encompassing 159 chest, 156 abdominal, and 107 lumbar spine datasets. DL-BMD values on L1 and L2 vertebral bodies were compared with manual BMD (m-BMD) measurements using Pearson’s correlation and intraclass correlation coefficients. Strong agreement was found between m-BMD and DL-BMD in total CT scans ( r = 0.953, p < 0.001). The diagnostic performance of DL-BMD was assessed using receiver operating characteristic analysis for osteoporosis and low BMD by dual-energy x-ray absorptiometry (DXA) and m-BMD. Compared to DXA, DL-BMD demonstrated an AUC of 0.790 (95% CI 0.733–0.839) for low BMD and 0.769 (95% CI 0.710–0.820) for osteoporosis, with sensitivity, specificity, and accuracy of 80.8% (95% CI 74.2–86.3%), 56.3% (95% CI 43.4–68.6%), and 74.3% (95% CI 68.3–79.7%) for low BMD and 65.4% (95% CI 50.9–78.0%), 70.9% (95% CI 63.8–77.3%), and 69.7% (95% CI 63.5–75.4%) for osteoporosis, respectively. Compared to m-BMD, DL-BMD showed an AUC of 0.983 (95% CI 0.973–0.993) for low BMD and 0.972 (95% CI 0.958–0.987) for osteoporosis, with sensitivity, specificity, and accuracy of 97.3% (95% CI 94.5–98.9%), 85.2% (95% CI 78.8–90.3%), and 92.7% (95% CI 89.7–95.0%) for low BMD and 94.4% (95% CI 88.3–97.9%), 89.5% (95% CI 85.6–92.7%), and 90.8% (95% CI 87.6–93.4%) for osteoporosis, respectively. The DL-based method can provide accurate and reliable BMD assessments across diverse CT protocols and scanners. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-73709-w.

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Evaluation of Deep Learning-Based Quantitative Computed Tomography for Opportunistic Osteoporosis Screening

Kang

Park

et al. 2023

Preprint

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Background To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. Methods A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 92 routine clinical CT scans from 65 patients who underwent either chest (N:29), lumbar spine (N:34), or abdominal CT (N:29) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson’s correlation and intraclass correlation. Receiver operating characteristic curve (ROC) analysis identified the diagnostic ability of DL-BMD for low BMD and osteoporosis, determined by dual-energy x-ray absorptiometry (DXA) and m-BMD. Results Excellent concordance were seen between m-BMD and DL-BMD in total CT scans (r = 0.960/0.980). The ROC-derived AUC of DL-BMD compared to that of central DXA for the low-BMD and osteoporosis patients was 0.840 and 0.784 respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to central DXA for low BMD were 73.1%, 68.0%, and 71.7%, respectively, and those for osteoporosis were 78.9%, 83.6%, and 82.6%. The AUC of DL-BMD compared to the m-BMD for low BMD and osteoporosis diagnosis were 0.982 and 0.934, respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to m-BMD for low BMD were 94.8%, 94.1%, and 94.6%, and those for osteoporosis were 73.3%, 91.9%, and 85.9%, respectively. Conclusions DL-BMD exhibited excellent agreement with m-BMD on L1 and L2 vertebrae in the various routine clinical CT scans and had comparable diagnostic performance for detecting the low-BMD and osteoporosis on conventional QCT.

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What Causes the Discrepancy between Quantitative Computed Tomography and Dual Energy X-Ray Absorptiometry?

Cited by 4 publications

References 40 publications

Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening

Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening

Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners

Evaluation of Deep Learning-Based Quantitative Computed Tomography for Opportunistic Osteoporosis Screening

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