Dataset Information

Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.

ABSTRACT: Our study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures.

Introduction

Many patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the performance of global volumetric bone mineral density (vBMD) with a random forest classifier based on regional vBMD and 3D texture features to separate patients with and without osteoporotic fractures.

Methods

In total, 154 patients (mean age 64 ± 8.5, male; n = 103) were included in this retrospective single-center analysis, who underwent contrast-enhanced CT for other reasons than osteoporosis screening. Patients were dichotomized regarding prevalent vertebral osteoporotic fractures (noFX, n = 101; FX, n = 53). Vertebral bodies were automatically segmented, and trabecular vBMD was calculated with a dedicated phantom. For 3D texture analysis, we extracted gray-level co-occurrence matrix Haralick features (HAR), histogram of gradients (HoG), local binary patterns (LBP), and wavelets (WL). Fractured vertebrae were excluded for texture-feature and vBMD data extraction. The performance to identify patients with prevalent osteoporotic vertebral fractures was evaluated in a fourfold cross-validation.

Results

The random forest classifier showed a high discriminatory power (AUC = 0.88). Parameters of all vertebral levels significantly contributed to this classification. Importantly, the AUC of the proposed algorithm was significantly higher than that of volumetric global BMD alone (AUC = 0.64).

Conclusion

The presented classifier combining 3D texture features and regional vBMD including the complete thoracolumbar spine showed high discriminatory power to identify patients with vertebral fractures and had a better diagnostic performance than vBMD alone.

SUBMITTER: Valentinitsch A

PROVIDER: S-EPMC6546649 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.

Valentinitsch A A Trebeschi S S Kaesmacher J J Lorenz C C Löffler M T MT Zimmer C C Baum T T Kirschke J S JS

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA 20190304 6

Our study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures.<h4>Introduction</h4>Many patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the p ...[more]

PMID: 30830261

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Project description:This study utilized deep learning to classify osteoporosis and predict bone density using opportunistic CT scans and independently tested the models on data from different hospitals and equipment. Results showed high accuracy and strong correlation with QCT results, showing promise for expanding osteoporosis screening and reducing unnecessary radiation and costs.PurposeTo explore the feasibility of using deep learning to establish a model for osteoporosis classification and bone density value prediction based on opportunistic CT scans and to verify its generalization and diagnostic ability using an independent test set.MethodsA total of 1219 cases of opportunistic CT scans were included in this study, with QCT results as the reference standard. The training set: test set: independent test set ratio was 703: 176: 340, and the independent test set data of 340 cases were from 3 different hospitals and 4 different CT scanners. The VB-Net structure automatic segmentation model was used to segment the trabecular bone, and DenseNet was used to establish a three-classification model and bone density value prediction regression model. The performance parameters of the models were calculated and evaluated.ResultsThe ROC curves showed that the mean AUCs of the three-category classification model for categorizing cases into "normal," "osteopenia," and "osteoporosis" for the training set, test set, and independent test set were 0.999, 0.970, and 0.933, respectively. The F1 score, accuracy, precision, recall, precision, and specificity of the test set were 0.903, 0.909, 0.899, 0.908, and 0.956, respectively, and those of the independent test set were 0.798, 0.815, 0.792, 0.81, and 0.899, respectively. The MAEs of the bone density prediction regression model in the training set, test set, and independent test set were 3.15, 6.303, and 10.257, respectively, and the RMSEs were 4.127, 8.561, and 13.507, respectively. The R-squared values were 0.991, 0.962, and 0.878, respectively. The Pearson correlation coefficients were 0.996, 0.981, and 0.94, respectively, and the p values were all < 0.001. The predicted values and bone density values were highly positively correlated, and there was a significant linear relationship.ConclusionUsing deep learning neural networks to process opportunistic CT scan images of the body can accurately predict bone density values and perform bone density three-classification diagnosis, which can reduce the radiation risk, economic consumption, and time consumption brought by specialized bone density measurement, expand the scope of osteoporosis screening, and have broad application prospects.

Project description:The primary aim of this study was to evaluate computed tomography (CT)-based bone density analysis at the level of thoracic vertebra 12 (Th12) as a screening method for decreased bone density in patients admitted to the intensive care unit (ICU). Interobserver variability was analyzed. Secondary aims were to assess the prevalence of CT-based low bone density upon ICU admission in a cohort of COVID-19 patients and to assess the potential effect of long-term ICU stay on bone density in these patients. Retrospective single-center cohort study. ICU of the Leiden University Medical Center (LUMC), the Netherlands. Patients admitted to the ICU of the LUMC between March 1st, 2020 and February 1st, 2022 with a diagnosis of COVID-19, and a length of ICU stay of ≥ 21 days. In the included patients both baseline chest CT scans (obtained upon ICU admission) and follow-up chest CT scans (obtained ≥ 21 days after ICU admission) were available for analysis. A total of 118 CT scans in 38 patients were analyzed. There was a good interobserver variability, with an overall mean absolute difference (between measurements of three observers) of 9.7 Hounsfield Units (HU) and an intraclass correlation coefficient (ICC) of 0.93 (95% CI 0.88-0.96). The effect of intravenous contrast administration on bone density measurements was small (+ 7.5 HU (95% CI 3.4-11.5 HU)) higher in contrast enhanced CT images compared to non contrast enhanced CT images). Thirty-seven percent of patients had a bone density < 140 HU, suggestive of osteoporosis. No significant difference was found between bone density upon ICU admission and bone density at follow-up (≥ 21 days after ICU admission). Vertebral CT-based bone density analysis using routine CT scans is an easily applicable method to identify ICU patients with decreased bone density, which could enable enrollment in osteoporosis prevention programs. A high prevalence of low bone density was found in our cohort of ICU patients. There were no changes observed in bone density between baseline and follow-up measurements.

Project description:Opportunistic screening for osteoporosis can be performed using low-dose computed tomography (LDCT) imaging obtained for other clinical indications. In this study we explored the CT-derived bone mineral density (BMD) and prevalence of osteoporosis from thoracic LDCT in a large population cohort of Chinese men and women. A total of 69,095 adults (40,733 men and 28,362 women) received a thoracic LDCT scan for the purpose of lung cancer screening between 2018 and 2019, and data were obtained for analysis from the China Biobank Project, a prospective nationwide multicenter population study. Lumbar spine (L1 -L2 ) trabecular volumetric bone mineral density (vBMD) was derived from these scans using quantitative computed tomography (QCT) software and the American College of Radiology QCT diagnostic criteria for osteoporosis were applied. Geographic regional differences in the prevalence of osteoporosis were assessed and the age-standardized, population prevalence of osteoporosis in Chinese men and women was estimated from the 2010 China census. The prevalence of osteoporosis by QCT for the Chinese population aged >50 years was 29.0% for women and 13.5% for men, equating to 49.0 million and 22.8 million, respectively. In women, this rate is comparable to estimates from dual-energy X-ray absorptiometry (DXA), but in men, the prevalence is double. Prevalence varied geographically across China, with higher rates in the southwest and lower rates in the northeast. Trabecular vBMD decreased with age in both men and women. Women had higher peak trabecular vBMD (185.4 mg/cm3 ) than men (176.6 mg/cm3 ) at age 30 to 34 years, but older women had lower trabecular vBMD (62.4 mg/cm3 ) than men (92.1 mg/cm3 ) at age 80 years. We show that LDCT-based opportunistic screening could identify large numbers of patients with low lumbar vBMD, and that future cohort studies are now required to evaluate the clinical utility of such screening in terms of fracture prevention and supporting national health economic analyses. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)..

Project description:ObjectivesTo compare spinal bone measures derived from automatic and manual assessment in routine CT with dual energy X-ray absorptiometry (DXA) in their association with prevalent osteoporotic vertebral fractures using our fully automated framework ( https://anduin.bonescreen.de ) to assess various bone measures in clinical CT.MethodsWe included 192 patients (141 women, 51 men; age 70.2 ± 9.7 years) who had lumbar DXA and CT available (within 1 year). Automatic assessment of spinal bone measures in CT included segmentation of vertebrae using a convolutional neural network (CNN), reduction to the vertebral body, and extraction of bone mineral content (BMC), trabecular and integral volumetric bone mineral density (vBMD), and CT-based areal BMD (aBMD) using asynchronous calibration. Moreover, trabecular bone was manually sampled (manual vBMD).ResultsA total of 148 patients (77%) had vertebral fractures and significantly lower values in all bone measures compared to patients without fractures (p ≤ 0.001). Except for BMC, all CT-based measures performed significantly better as predictors for vertebral fractures compared to DXA (e.g., AUC = 0.885 for trabecular vBMD and AUC = 0.86 for integral vBMD vs. AUC = 0.668 for DXA aBMD, respectively; both p < 0.001). Age- and sex-adjusted associations with fracture status were strongest for manual vBMD (OR = 7.3, [95%] CI 3.8-14.3) followed by automatically assessed trabecular vBMD (OR = 6.9, CI 3.5-13.4) and integral vBMD (OR = 4.3, CI 2.5-7.6). Diagnostic cutoffs of integral vBMD for osteoporosis (< 160 mg/cm3) or low bone mass (160 ≤ BMD < 190 mg/cm3) had sensitivity (84%/41%) and specificity (78%/95%) similar to trabecular vBMD.ConclusionsFully automatic osteoporosis screening in routine CT of the spine is feasible. CT-based measures can better identify individuals with reduced bone mass who suffered from vertebral fractures than DXA.Key points• Opportunistic osteoporosis screening of spinal bone measures derived from clinical routine CT is feasible in a fully automatic fashion using a deep learning-driven framework ( https://anduin.bonescreen.de ). • Manually sampled volumetric BMD (vBMD) and automatically assessed trabecular and integral vBMD were the best predictors for prevalent vertebral fractures. • Except for bone mineral content, all CT-based bone measures performed significantly better than DXA-based measures. • We introduce diagnostic thresholds of integral vBMD for osteoporosis (< 160 mg/cm3) and low bone mass (160 ≤ BMD < 190 mg/cm3) with almost equal sensitivity and specificity compared to conventional thresholds of quantitative CT as proposed by the American College of Radiology (osteoporosis < 80 mg/cm3).

Project description:Background Abdominal and thoracic CT provide a valuable opportunity for osteoporosis screening regardless of the clinical indication for imaging. Purpose To establish reference normative ranges for first lumbar vertebra (L1) trabecular attenuation values across all adult ages to measure bone mineral density (BMD) at routine CT. Materials and Methods Reference data were constructed from 20 374 abdominal and/or thoracic CT examinations performed at 120 kV. Data were derived from adults (mean age, 60 years ± 12 [standard deviation]; 56.1% [11 428 of 20 374] women). CT examinations were performed with (n = 4263) or without (n = 16 111) intravenous contrast agent administration for a variety of unrelated clinical indications between 2000 and 2018. L1 Hounsfield unit measurement was obtained either with a customized automated tool (n = 11 270) or manually by individual readers (n = 9104). The effects of patient age, sex, contrast agent, and manual region-of-interest versus fully automated L1 Hounsfield unit measurement were assessed using multivariable logistic regression analysis. Results Mean L1 attenuation decreased linearly with age at a rate of 2.5 HU per year, averaging 226 HU ± 44 for patients younger than 30 years and 89 HU ± 38 for patients 90 years or older. Women had a higher mean L1 attenuation compared with men (P < .008) until menopause, after which both groups had similar values. Administration of intravenous contrast agent resulted in negligible differences in mean L1 attenuation values except in patients younger than 40 years. The fully automated method resulted in measurements that were average 21 HU higher compared with manual measurement (P < .004); at intrapatient subanalysis, this difference was related to the level of transverse measurement used (midvertebra vs off-midline level). Conclusion Normative ranges of L1 vertebra trabecular attenuation were established across all adult ages, and these can serve as a quick reference at routine CT to identify adults with low bone mineral density who are at risk for osteoporosis. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Smith in this issue.

Project description:BackgroundMany patients with malignant tumors require chemotherapy and radiation therapy, which can result in a decline in physical function and potentially influence bone mineral density (BMD). Furthermore, these treatments necessitate enhanced computed tomography (CT) scans for determining disease staging or treatment outcomes, and opportunistic screening with available imaging data is beneficial for patients at high risk for osteoporosis if existing imaging data can be used. The study aimed to investigate the feasibility of opportunistic screening for osteoporosis using enhanced CT based on a dual-energy CT (DECT) material decomposition technique.MethodsWe prospectively enrolled 346 consecutive patients who underwent abdominal unenhanced and triphasic contrast-enhanced CT (arterial, portal venous, and delayed phases) between June 2021 and June 2022. The BMD, and the density of hydroxyapatite (HAP) on HAP-iodine images and calcium (Ca) on Ca-iodine images were measured on the L1-L3 vertebral bodies. The iodine intake was recorded. Pearson analysis was conducted to assess the correlation between iodine intake and the density values in three phases and the correlation between BMD and the densities of HAP and Ca. Furthermore, linear regression was employed for quantitative evaluation. Bland-Altman analysis was used to evaluate the agreement between calculated BMD derived from DECT (BMD-DECT) and reference BMD derived from quantitative CT (BMD-QCT). Receiver operating characteristic (ROC) analysis was applied to assess the diagnostic efficacy.ResultsThe HAP and Ca density of the L1-L3 vertebral bodies did not differ significantly among the three phases of contrast-enhanced CT (F=0.001-0.049; P>0.05). Significant positive correlations were found between HAP, Ca densities, and BMD (HAP-BMD: r=0.9472, R2=0.8973; Ca-BMD: r=0.9470, R2=0.8968; all P<0.001). Bland-Altman plots showed high agreement between BMD-DECT and BMD-QCT. The area under the curve (AUC) using HAP and Ca measurements was 0.963 [95% confidence interval (CI): 0.937-0.980] and 0.964 (95% CI: 0.939-0.981), respectively, for diagnosing osteoporosis and was 0.951 (95% CI: 0.917-0.973) and 0.950 (95% CI: 0.916-0.973), respectively, for diagnosing osteopenia.ConclusionsThe HAP and Ca density measurements generated through the material decomposition technique in DECT have good diagnostic performances in assessing BMD, which offers a new perspective for opportunistic screening of osteoporosis on contrast-enhanced CT.

Dataset Information

Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.

Introduction

Methods

Results

Conclusion

Publications

Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.

Similar Datasets

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