Dataset Information

Opportunistic osteoporosis screening via the measurement of frontal skull Hounsfield units derived from brain computed tomography images.

ABSTRACT: BACKGROUND AND PURPOSE:Osteoporosis is one of the most common chronic metabolic diseases, but detection and treatment rates are low. The aim of the current study was to evaluate the correlation between frontal skull Hounsfield unit (HU) values from brain computed tomography (CT) scans and T-scores of the lumbar spine and femoral neck from dual-energy X-ray absorptiometry (DXA) scans. METHODS:Patients with < 1 year between brain CT and DXA scans were included in the study. The average frontal skull HU value used for analysis was defined as the average of four HU values of the frontal bone. A receiver operating characteristic curve was generated, and area under the curve (AUC) was used to determine the HU values of the frontal skull for predicting osteoporosis. The frontal skull HU value with the highest sensitivity and specificity was considered the optimal cutoff value. RESULTS:In total, 899 patients who underwent both brain CT and DXA scans at a single institution were enrolled. Average skull HU values differed significantly among patients in different bone mineral density categories (p < 0.001). There was a positive correlation between skull HU value and T-score (? = 105.06, p < 0.001, R2 = 0.343). The mean HU value in subjects with osteoporosis was 515, and the optimal cutoff value for the prediction of osteoporosis was 610 HU (AUC = 0.775, 95% CI 0.744-0.806, p < 0.001). CONCLUSIONS:Clinical brain CT scans can assist in the detection of osteoporosis, and patients with an HU value < 610 as determined via brain CT may be considered for further evaluation for possible osteoporosis.

SUBMITTER: Na MK

PROVIDER: S-EPMC5945032 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Opportunistic osteoporosis screening via the measurement of frontal skull Hounsfield units derived from brain computed tomography images.

Na Min Kyun MK Won Yu Deok YD Kim Choong Hyun CH Kim Jae Min JM Cheong Jin Hwan JH Ryu Je Il JI Han Myung-Hoon MH

PloS one 20180510 5

<h4>Background and purpose</h4>Osteoporosis is one of the most common chronic metabolic diseases, but detection and treatment rates are low. The aim of the current study was to evaluate the correlation between frontal skull Hounsfield unit (HU) values from brain computed tomography (CT) scans and T-scores of the lumbar spine and femoral neck from dual-energy X-ray absorptiometry (DXA) scans.<h4>Methods</h4>Patients with < 1 year between brain CT and DXA scans were included in the study. The aver ...[more]

PMID: 29746570

Similar Datasets

Project description:BackgroundMuscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans.MethodsAttenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours.ResultsWe trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%.ConclusionsThis work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer.

Project description: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.IntroductionMany 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.MethodsIn 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.ResultsThe 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).ConclusionThe 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.

Project description:The surgeon general of the USA defines osteoporosis as "a skeletal disorder characterized by compromised bone strength, predisposing to an increased risk of fracture." Measuring bone strength, Biomechanical Computed Tomography analysis (BCT), namely, finite element analysis of a patient's clinical-resolution computed tomography (CT) scan, is now available in the USA as a Medicare screening benefit for osteoporosis diagnostic testing. Helping to address under-diagnosis of osteoporosis, BCT can be applied "opportunistically" to most existing CT scans that include the spine or hip regions and were previously obtained for an unrelated medical indication. For the BCT test, no modifications are required to standard clinical CT imaging protocols. The analysis provides measurements of bone strength as well as a dual-energy X-ray absorptiometry (DXA)-equivalent bone mineral density (BMD) T-score at the hip and a volumetric BMD of trabecular bone at the spine. Based on both the bone strength and BMD measurements, a physician can identify osteoporosis and assess fracture risk (high, increased, not increased), without needing confirmation by DXA. To help introduce BCT to clinicians and health care professionals, we describe in this review the currently available clinical implementation of the test (VirtuOst), its application for managing patients, and the underlying supporting evidence; we also discuss its main limitations and how its results can be interpreted clinically. Together, this body of evidence supports BCT as an accurate and convenient diagnostic test for osteoporosis in both sexes, particularly when used opportunistically for patients already with CT. Biomechanical Computed Tomography analysis (BCT) uses a patient's CT scan to measure both bone strength and bone mineral density at the hip or spine. Performing at least as well as DXA for both diagnosing osteoporosis and assessing fracture risk, BCT is particularly well-suited to "opportunistic" use for the patient without a recent DXA who is undergoing or has previously undergone CT testing (including hip or spine regions) for an unrelated medical condition.

Dataset Information

Opportunistic osteoporosis screening via the measurement of frontal skull Hounsfield units derived from brain computed tomography images.

Publications

Opportunistic osteoporosis screening via the measurement of frontal skull Hounsfield units derived from brain computed tomography images.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets