Effect of Positioning of the Region of Interest on Bone Density of the Hip

Feit, Avery; Levin, Natan; McNamara, Elizabeth; Sinha, Partha; Whittaker, LaTarsha G.; Malabanan, Alan O.; Rosen, Harold N.

doi:10.1016/j.jocd.2019.04.002

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Previous studies have reported the importance of defining the distal border of the ROI in the BMD measurement, because a 1 mm change results in a BMD change of 0.54% to 0.68%. 27,28 Further, the errors in angle measurements to account for the variance in patient positioning during CT were small (Figures 4c and 4d), especially for abduction and flexion, which independently lead to errors in BMD measurements. 29 As the distal border of the ROI was defined based on the tip of the lesser trochanter that had small errors in the landmark detection model (1.9 mm to 2.0 mm) and as hip abduction and flexion were accurately compensated, the CT-aBMD was likely quantified with less error.…”

Section: Discussionmentioning

confidence: 93%

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images

Uemura,

Otake,

Takashima

et al. 2023

Bone Joint Res

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AimsThis study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.MethodsThe study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.ResultsCT-aBMD was successfully measured in 976/978 hips (99.8%). A significant correlation was found between CT-aBMD and DXA-BMD (r = 0.941; p < 0.001). In the ROC analysis, the area under the curve to diagnose osteoporosis was 0.976. The diagnostic sensitivity and specificity were 88.9% and 96%, respectively, with the cutoff set at 0.625 g/cm2.ConclusionAccurate DXA-BMD measurements and diagnosis of osteoporosis were performed from CT images using the system developed herein. As the models are open-source, clinicians can use the proposed system to screen osteoporosis and determine the surgical strategy for hip surgery.Cite this article: Bone Joint Res 2023;12(9):590–597.

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Section: Discussionmentioning

confidence: 93%

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images

Uemura,

Otake,

Takashima

et al. 2023

Bone Joint Res

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“…It is important to note that manual efforts are still necessary to quantify BMD from CT images because the bone regions of interest must be selected manually. Because this process is time consuming and prone to error [21], in future studies, we aim to explore developing a convolutional neural network to isolate the region of interest (e.g., femoral neck and spine) to create a fully automated system, which would pave the way for conducting multicenter quantitative CT studies with large datasets.…”

Section: Discussionmentioning

confidence: 99%

Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network

et al. 2021

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To apply a convolutional neural network (CNN) to develop a system that segments intensity calibration phantom regions in computed tomography (CT) images, and to test the system in a large cohort to evaluate its robustness. MethodsA total of 1040 cases (520 cases each from two institutions), in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used, were included herein. A training dataset was created by manually segmenting the regions of the phantom for 40 cases (20 cases each). Segmentation accuracy of the CNN model was assessed with the Dice coefficient and the average symmetric surface distance (ASD) through the 4-fold cross validation. Further, absolute differences of radiodensity values (in Hounsfield units: HU) were compared between manually segmented regions and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate coefficients for the correlation between radiodensity and the densities of the phantom. ResultsAfter training, the median Dice coefficient was 0.977, and the median ASD was 0.116 mm. When segmented regions were compared between manual segmentation and automated segmentation, the median absolute difference was 0.114 HU. For the test cases, the median correlation coefficient was 0.9998 for one institution and was 0.9999 for the 4 other, with a minimum value of 0.9863. ConclusionsThe CNN model successfully segmented the calibration phantom's regions in the CT images with excellent accuracy, and the automated method was found to be at least equivalent to the conventional manual method. Future study should integrate the system by automatically segmenting the region of interest in bones such that the bone mineral density can be fully automatically quantified from CT images. The source code and the model used for segmenting the phantom are open and can be accessed via https://github.com/keisuke-

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“…We limited measurement bias on the BMD scans since all scans were performed by two trained operators with expertise in DXA scans [ 14 ]. Furthermore, all DXA scans were performed on the same machine (HOLOGIC Discovery) [ 15 ], and the two measurements were performed on the same day in each patient.…”

Section: Methodsmentioning

confidence: 99%

“The least significant change on bone mineral density scan increased in patients with higher degrees of obesity”

Dorilleau,

Kanagaratnam,

Charlot

et al. 2024

Aging Clin Exp Res

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Background: The least significant change (LSC) threshold of 0.03 g/cm² is used to interpret bone mineral density (BMD) scans in the general population. Our working hypothesis was that the current LSC threshold would not be applicable in obese populations. Aims: The aim of this study was to calculate the LSC in an obese population. Methods: We performed an interventional study among 120 obesity patients, in whom two measurements of BMD were performed at 3 sites. Pairs of measures were used to calculate the LSC, using the Bland and Altman method. Results: We calculated that the LSC was 0.046 g/cm² at the lumbar spine, 0.069 g/cm² at the femoral neck, and 0.06 g/cm² at the total hip. We also calculated the LSC for each class of obesity and observed an increase in LSC with increasing body mass index (BMI). We calculated a LSC of 0.05 g/cm² in patients with class 2 or class 3 obesity, whereas the LSC in patients with class 1 obesity is similar to the threshold used in the general population. Discussion: In obese population, like BMD, LSC is higher than the threshold value of the general population, and increases with increasing BMI.Conclusion: LSC of 0.05 g/cm² could be used in clinical practice in patients with class 2 or 3 obesity. These findings should help to improve the interpretation of BMD scans in these patients and optimize their management. Trial registration number: Comité de Protection des Personnes Ile-de France VII, France.

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Effect of Positioning of the Region of Interest on Bone Density of the Hip

Cited by 6 publications

References 12 publications

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images

Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images

Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network

“The least significant change on bone mineral density scan increased in patients with higher degrees of obesity”

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