Comparison of the diagnostic performance of CT Hounsfield unit histogram analysis and dual-energy X-ray absorptiometry in predicting osteoporosis of the femur

Value of different preoperative bone evaluation methods in predicting intraoperative screw insertion torque-a prospective clinical comparative trial

BACKGROUND CONTEXT

Establishing good screw-bone structural stability is conducive to reducing the risk of postoperative screw loosening. Screw insertion torque is an objective index for evaluating screw-bone structural stability. Therefore, accurate prediction of screw insertion torque can improve the preoperative evaluation of patients, optimize the surgical plan, and improve the surgical effect. At present, the correlation between different bone assessment methods and screw insertion torque is unclear.

PURPOSE

The aim of this study was to evaluate the correlation between different bone assessment methods and screw insertion torque and to optimize the predictive performance of screw insertion torque through mathematical modeling combined with different radiology methods.

DESIGN

Prospective cross-sectional study.

PATIENT SAMPLES

77 patients with preoperatively available DXA, CT and MRI data who underwent spinal fixation surgeries between October 2022 and September 2023 and 357 sets of screw data were included in this analysis.

OUTCOME MEASURES

Spinal, vertebrae-specific and screw trajectory's BMD were measured preoperatively by different imaging modalities. Intraoperative screw insertion torque was measured using an electronic torque wrench.

METHODS

Pearson linear correlation, scatter plots and univariate linear regression were used to evaluate the correlation between different bone evaluation methods and screw insertion torque. Different bone evaluation methods were fitted into the prediction model of screw torque and the related equations were obtained.

RESULTS

Screw insertion torque had the strongest positive correlation with the volumetric bone mineral density (vBMD) of the screw trajectory (Pedicle screw insertion torque (PSIT): R = 0.618, p<.001; Terminal screw insertion torque (TSIT): R = 0.735, p<.001). A weak negative correlation was found between the screw insertion torque and level specific vertebral bone quality (VBQ) (PSIT: R = -0.178, p=.001; TSIT: R = -0.147, p=.006). We also found that the PSIT was strongly correlated with the TSIT (R = 0.812, p<.001).

CONCLUSIONS

Compared to other bone quality assessment methods, screw trajectory vBMD may be better predict the magnitude of screw insertion torque. In addition, we further optimized preoperative assessments by constructing a mathematical model to better predict screw insertion torque. In conclusion, clinicians should select appropriate preoperative bone quality assessment methods, identify potential low-torque patients, optimize surgical plans, and ultimately improve screw insertion accuracy and reduce postoperative screw loosening rate.

Comparative evaluation of multiparametric lumbar MRI radiomic models for detecting osteoporosis

BACKGROUND

Osteoporosis is a serious global public health issue. Currently, there are few studies that explore the use of multiparametric MRI radiomics for osteoporosis detection. The purpose of this study was to compare the performance of radiomics features from multiple MRI sequences (T1WI, T2WI and T1WI combined with T2WI) for detecting osteoporosis in patients.

METHODS

A retrospective analysis was performed on 160 patients who had undergone dual-energy X-ray absorptiometry(DXA) and lumbar magnetic resonance imaging (MRI) at our hospital. Among them, 86 patients were diagnosed with abnormal bone mass (osteoporosis or low bone mass), and 74 patients were diagnosed with normal bone mass based on the DXA results. Sagittal T1-and T2-weighted images of all patients were imported into the uAI Research Portal (United Imaging Intelligence) for image delineation and radiomics analysis, where a series of radiomic features were obtained. A radiomic model that included T1WI, T2WI, and T1WI+T2WI was established using features selected by LASSO regression. We used ROC curve analysis to evaluate the predictive efficacy of each model for identifying bone abnormalities and conducted decision curve analysis (DCA) to evaluate the net benefit of each model. Finally, we validated the model in a sample of 35 patients from different health care institution.

RESULTS

The T1WI + T2WI radiomics model showed better screening performance for patients with abnormal bone mass. In the training group, the sensitivity was 0.758, the specificity was 0.78, and the accuracy was 0.768 (AUC =0.839, 95% CI=0.757-0.901). In the validation group, the sensitivity was 0.792, the specificity was 0.875, and the accuracy was 0.833 (AUC =0.86, 95% CI=0.73-0.943).The DCA also showed that the combined model had better net benefits. In the external validation group, the sensitivity was 0.764, the specificity was 0.833, and the accuracy was 0.8 (AUC =0.824, 95% CI 0.678-0.969).

CONCLUSIONS

Radiomics-based multiparametric MRI can be used for the quantitative analysis of lumbar MRI and for accurately screening patients with abnormal bone mass.

Measurement of Hounsfield units on proximal femur computed tomography for predicting regional osteoporosis

OBJECTIVE

This study was designed to investigate the use of proximal femoral Hounsfield units (HU) in conventional abdominal and pelvic computed tomography (CT) to predict hip osteoporosis by coupling with data from quantitative CT (QCT).

METHODS

In this study, 315 patients who underwent routine abdominal and pelvic CT with the proximal femur included in the scanning range were also subjected to QCT of the proximal femur. Pearson correlation test was performed to analyze the correlations of the femoral head, femoral neck, proximal femur, and femoral trochanter CT HU with the femoral neck, femoral trochanter, and intertrochanteric femur bone mineral density (BMD) values from QCT. The diagnostic performance of CT HU measurement of the proximal femur for osteoporosis was analyzed using receiver operating characteristic (ROC) curves.

RESULTS

The CT HU of the proximal femur showed the highest correlation with the BMD value of the hip (r = 0.826; p < 0.01). The mean CT HU of the proximal femur differed significantly (all p < 0.01) for the three QCT-defined BMD categories of osteoporosis (192.23 HU vs. 188.71), of osteopenia (247.86 HU vs. 248.36 HU), and of normal individuals (308.13 HU vs. 310.41 HU) in left and right sides, respectively. In the ROC curve analysis, the area under the ROC curve values to predict osteoporosis in the left and right proximal femurs were 0.942 and 0.941, respectively.

CONCLUSION

The CT HU of the proximal femur was significantly associated with the BMD value of the hip measured by QCT. The CT HU of the proximal femur is highly effective in diagnosing osteoporosis and could be used for hip osteoporosis screening.

Fat quantification of the rotator cuff muscles using CT histogram analysis in comparison with Dixon MRI sequence

OBJECTIVES

To quantify the proportion of fat within the rotator cuff muscles using CT histogram analysis (HA) and to compare fat fraction (FF) values obtained from CT and T2*-corrected 6-echo three-dimensional gradient-echo Dixon sequences.

METHODS

Twenty-six patients who underwent both shoulder CT and MRI were enrolled. The FF of rotator cuff muscles was measured on FF maps of the Dixon technique. Using CT, the FF values were obtained in two ways: 1) CT mean Hounsfield Unit (HU) measurement-based fat quantification using subcutaneous fat and the teres major muscle as internal standards; and 2) CT HA-based fat quantification in which pixels from -190 to -30 HU were arbitrarily classified as fat, and those from -30 to 150 HU as muscle. Afterward, the relationship between FF values from MRI (FF-MR), CT mean HU-based analysis (FF-HU), and CT HA (FF-HA) was assessed.

RESULTS

There were strong positive correlations (p < 0.001) between FF-MR and FF-HA, which were higher when compared with FF-HU (r = 0.90 vs. 0.77 for total). For a total of 104 rotator cuff muscles, the intraclass correlation between FF-MR and FF-HA was excellent (ICC, 0.90), which was higher than that between FF-MR and FF-HU (ICC, 0.76). The ICCs showed excellent interobserver agreement between FF-MR, FF-HU, and FF-HA (ICCs, 0.93-0.96).

CONCLUSIONS

Fat quantification within the rotator cuff muscles using CT HA is both feasible and reliable.

ADVANCES IN KNOWLEDGE

CT HA provides reliable quantification of intramuscular fat and has strengths regarding its retrospective applicability to conventional CT studies without additional radiation dose to subjecting patients.

Low bone mineral density is associated with fatty liver disease and respiratory illness in a pediatric mortality sample

This study finds that fatty liver disease is associated with low bone density in a pediatric mortality sample. Since non-alcoholic fatty liver disease has increased in prevalence over the past few decades among children, a better understanding of the disease's impacts on bone health is of significance to clinicians.

PURPOSE

Chronic illness leads to decreased bone modeling and remodeling. This can be especially problematic during childhood and adolescence, since the majority of an individual's peak bone mass is achieved by the age of 20. In this study, we examine relationships between chronic illness and low bone mineral density (BMD) in a pediatric mortality sample (aged 0.5 to 20.9 years) from New Mexico. We also test whether low BMD is related to decelerated linear growth by examining its relationship to growth stunting and arrest (Harris lines).

METHODS

Hounsfield units (HU), a proxy for trabecular BMD, were obtained at the fourth lumbar vertebra and the femoral neck from postmortem CT scans. Linear regression was used to examine associations between z-standardized HU and age, sex, medical conditions, Harris lines, and growth stunting.

RESULTS

We find that lumbar HU is significantly lower for individuals with fatty liver disease and respiratory illness; femoral HU is significantly lower in individuals with Harris lines.

CONCLUSION

The mechanisms of low BMD in individuals with fatty liver disease and respiratory illness are likely multifactorial and involve vitamin D deficiency (malnutrition, malabsorption), systemic inflammation, and sedentary lifestyles. However, better awareness of this relationship can provide clinicians with the ability to introduce nutritional and behavioral interventions early to mitigate deleterious effects on bone. Harris lines, on the other hand, mark temporary growth cessation due to physiological stress followed by a rapid resumption of growth. Low BMD in these individuals may be due to bone mineralization lagging behind relatively rapid linear growth.

Diagnosis of Osteoporosis by Quantifying Volumetric Bone Mineral Density of Lumbar Vertebrae Using Abdominal CT Images and Two-Compartment Model

With the aging population, osteoporosis has become an important public health issue. The purpose of this study was to establish a two-compartment model (TCM) to quantify the volumetric bone mineral density (vBMD) of the lumbar spine using abdominal computed tomography (CT) images. The TCM approach uses water as the bone marrow equivalent and K₂HPO₄ solution as the cortical bone equivalent. A phantom study was performed to evaluate the accuracy of vBMD estimation at 100 kVp and 120 kVp. The data of 180 patients who underwent abdominal CT imaging and dual-energy X-ray absorptiometry (DXA) within one month were retrospectively collected. vBMD of L1-L4 vertebrae were calculated, and the receiver-operating characteristic curve analysis was performed to establish the diagnostic thresholds for osteoporosis and osteopenia in terms of vBMD. The average difference between the measured vBMD following TCM and the theoretical vBMD of the self-made phantom was 0.2%, and the maximum difference was 0.5%. vBMD of lumbar vertebrae obtained from TCM and aBMD obtained by DXA had a significant positive correlation (r = 0.655 to 0.723). The average diagnostic threshold for osteoporosis was 0.116 g/cm³. The sensitivity, specificity, and accuracy were 95.7%, 75.6.5%, and 80.0%, respectively. The average diagnostic threshold for osteopenia was 0.126 g/cm³. The sensitivity, specificity, and accuracy were 81.3%, 82.5%, and 82.7%, respectively. The aforementioned threshold values were used to perform the diagnostics on a test cohort, and the performance was equivalent to that in the experimental cohort. From the perspective of preventive medicine, opportunistic screening of bone mineral density using abdominal CT images and the TCM approach can facilitate early detection of osteoporosis and osteopenia and, with in-time treatment, slow down their progression.

Average Lumbar Hounsfield Units Predicts Osteoporosis-Related Complications Following Lumbar Spine Fusion

STUDY DESIGN

Retrospective Study.

OBJECTIVE

To compare methods of assessing pre-operative bone density to predict risk for osteoporosis related complications (ORC), defined as proximal junctional kyphosis, pseudarthrosis, accelerated adjacent segment disease, reoperation, compression fracture, and instrument failure following spine fusions.

METHODS

Chart review of primary posterior thoracolumbar or lumbar fusion patients during a 7 year period. Inclusion criteria: preoperative dual-energy x-ray absorptiometry (DXA) test within 1 year and lumbar CT scan within 6 months prior to surgery with minimum of 1 year follow-up. Exclusion criteria: <18 years at time of index procedure, infection, trauma, malignancy, skeletal dysplasia, neuromuscular disorders, or anterior-posterior procedures.

RESULTS

140 patients were included. The average age was 67.9 years, 83 (59.3%) were female, and 45 (32%) had an ORC. There were no significant differences in patient characteristics between those with and without an ORC. Multilevel fusions were associated with ORCs (46.7% vs 26.3%, p = 0.02). Patients with ORCs had lower DXA t-scores (-1.62 vs -1.10, p = 0.003) and average Hounsfield units (HU) (112.1 vs 148.1, p ≤ 0.001). Multivariable binary logistic regression analysis showed lower average HU (Adj. OR 0.00 595% CI 0.0001-0.1713, p = 0.001) was an independent predictor of an ORC. The odds of an ORC increased by 1.7-fold for every 25 point decrease in average HU.

CONCLUSIONS

The gold standard for assessing bone mineral density has been DXA t-scores, but the best predictor of ORC remains unclear. While both lower t-scores and average HU were associated with ORC, only HU was an independent predictor of ORC.

Assessment of Lumbar Vertebrae Morphology by Computed Tomography in Older Adults with Osteoporosis

BACKGROUND

Hounsfield units (HU) values derived from computerized tomography (CT) have been used in diagnosis for osteoporosis in the lumbar spine.

OBJECTIVE

This study aimed to identify anatomical dimensions of lumbar vertebrae on CT images, which were different between older normal, osteopenic and osteoporotic subjects.

METHODS

This prospective pilot study enrolled 79 older adults. Based on CT measurements of lumbar vertebrae in HU, participants were classified into three groups: normal (HU > 109), osteopenia (HU: 94-108), and osteoporosis (HU < 93). Altogether, 42 anatomical variables of lumbar vertebrae, L2, L3, L4, and L5, were measured in each participant by CT, including 24 parameters measurable by MRI or plain X-ray, and 18 parameters measurable by MRI only.

RESULTS

Among the morphological measurements also measurable by MRI and plain X-ray, the length upper curve, 50% and 75% of L5, length upper with cortex of L4, length center of cortex of L3, as well as width upper curve 75% of L2, were significantly different between the three groups (p= 0.008, 0.007, 0.035, 0.036, and 0.003 respectively). Among the morphological measurements also measurable by MRI, only width upper cortex 75% of L5 and width lower cortex 25% of L3 were significantly different between the three groups (p= 0.031 and 0.020, respectively).

CONCLUSION

Seven CT morphological measurements may be used as "reference standard" CT measurements for preliminarily diagnosing osteoporosis and osteopenia in older adults.

Comparison of diagnostic accuracy of 2D and 3D measurements to determine opportunistic screening of osteoporosis using the proximal femur on abdomen-pelvic CT

OBJECTIVES

To compare the osteoporosis-predicting ability of computed tomography (CT) indexes in abdomen-pelvic CT using the proximal femur and the reliability of measurements in two- and three-dimensional analyses.

METHODS

Four hundred thirty female patients (age range, 50-96 years) who underwent dual-energy X-ray absorptiometry and abdominal-pelvic CT within 1 month were retrospectively selected. The volumes of interest (VOIs) from the femoral head to the lesser trochanter and the femoral neck were expressed as 3DFemur. Round regions of interest (ROIs) of image plane drawn over the femoral neck touching the outer cortex were determined as 2Dcoronal. In HU histogram analysis (HUHA), the percentages of HU histogram ranges related to the ROI or VOI were classified as HUHAFat (<0 HU) and HUHABone (126 HU≤). Diagnostic performance, correlation analysis and measurement reliability were analyzed by receiver operating characteristic curves, correlation coefficient and interobserver correlation coefficient (ICC), respectively.

RESULTS

AUCs of each HUHA and mean-HU measurement on 2D-ROI and 3D-VOI were 0.94 or higher (P < 0.001). Both 3DFemur-Mean-HU and 3DFemur-HUHABone showed the highest AUC (0.96). The cut-off value of 3DFemur-Mean-HU was 231HU or less, (sensitivity: 94.8%; specificity: 85.0%; correlation coefficient: -0.65; P <0.001) for diagnosis of osteoporosis. There was no superiority between AUCs in 2D-ROI and 3D-VOI measurements (P > 0.05). Reliability of the 3D-VOI measurement showed perfect agreement (ICC ≥ 0.94), and 2D-ROI showed moderate to good agreement (ICC range: 0.63~0.84).

CONCLUSIONS

CT indexes on 3D-VOI for predicting femoral osteoporosis showed similar diagnostic accuracy with better reproducibility of measurement, compared with 2D-ROI.

Larger Volume and Higher Fat Content of the Pancreatic Head Are Predictive Factors for Postendoscopic Retrograde Cholangiopancreatography Pancreatitis

OBJECTIVES

Acute pancreatitis is the most critical complication of endoscopic retrograde cholangiopancreatography (ERCP). In this study, we investigated the association between the volume/fat content of the pancreatic head and the incidence of post-ERCP pancreatitis (PEP).

METHODS

We retrospectively enrolled 157 patients who underwent ERCP. The volume and fat content of the pancreas were calculated by multislice computed tomographic imaging by using a volume analyzer. Multivariate analysis was performed to identify risk factors for PEP.

RESULTS

The mean volumes of the whole pancreas and pancreatic head were significantly larger, and the fat content of the pancreatic head was significantly higher in the PEP group (P < 0.01). There were no significant differences in the mean volume and fat content of the pancreatic body and tail in the PEP group. Multivariate analysis revealed that the pancreatic guidewire placement (odds ratio [OR], 12.4; P < 0.01), pancreatic head volume (OR, 5.3; P < 0.01), and the pancreatic head fat content (OR, 4.8; P < 0.01) were independent risk factors for PEP.

CONCLUSIONS

The pancreatic head volume and fat content were independent predicting factors of PEP. Quantitative assessment of the pancreas may contribute to the prediction of PEP onset.

Correlation between Hounsfield unit derived from head, thorax, abdomen, spine and pelvis CT and t-scores from DXA

OBJECTIVES

CT examination can potentially be utilised for early detection of bone density changes with no additional procedure and radiation dose. We hypothesise that the Hounsfield unit (HU) measured from CT images is correlated to the t-scores derived from dual energy X-ray absorptiometry (DXA) in multiple anatomic regions.

MATERIALS & METHODS

Data were obtained retrospectively from all patients who underwent both CT examinations - brain (frontal bone), thorax (T7), abdomen (L3), spine (T7 & L3) or pelvis (left hip) - and DXA between 2014 and 2018 in our centre. To ensure comparability, the period between CT and DXA studies must not exceed one year. Correlations between HU values and t-scores were calculated using Pearson's correlation. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was used to determine threshold HU values for predicting osteoporosis.

RESULTS

The inclusion criteria were met by 1043 CT examinations (136 head, 537 thorax, 159 lumbar and 151 left hip). The left hip consistently provided the most robust correlations (r = 0.664-0.708, p < 0.001) and the best AUC (0.875-0.893). Meanwhile, thorax T7 and lumbar L3 showed average correlations (range of r values is 0.497-0.679, p < 0.001, AUC range = 0.680-0.783, 95% CI 0.561-0.922, all p < 0.02) and moderate AUC (0.680-0.783). Frontal bone shows low correlation and weak AUC with r < 0.5, AUC = 0.538-0.655, all p > 0.05.

CONCLUSION

HU values derived from the hip, T7 and L3 provided a good to moderate correlation to t-scores with a good prediction for osteoporosis. The suggested optimal thresholds may be used in clinical settings after external validations are performed.

Significant reduced loss of bone mineral density after four vs. six cycles of R-CHOP: an analysis of the FLYER-trial

Patients with diffuse large B-cell lymphoma (DLBCL) treated with the R-CHOP regime receive a high cumulative dose of prednisone. We used computer tomography-ascertained Hounsfield units (HU) as a surrogate parameter for bone mineral density (BMD) in three different locations of the L3 vertebral body at baseline and post-treatment. HU were measured in 50 patients with DLBCL of the previously published FLYER-trial which compared four cycles of R-CHOP + 2 × rituximab infusion to six cycles of R-CHOP in young, favorable DLBCL patients. In total, median loss was 26.8 HU in all patients over time. The median HU loss was significantly lower in the four cycles arm (21.3 HU vs. 41.3 HU, p = 0.023). In conclusion, young patients with DLBCL receiving R-CHOP have significant HU/BMD loss under treatment with R-CHOP. Patients receiving four cycles of R-CHOP had less HU/BMD loss than patients receiving six cycles.

Comparison of HU histogram analysis and BMD for proximal femoral fragility fracture assessment: a retrospective single-center case-control study

OBJECTIVES

To evaluate the feasibility of HU histogram analysis (HUHA) to assess proximal femoral fragility fractures with respect to BMD.

METHODS

This retrospective study included 137 patients with femoral fragility fractures who underwent hip CT and 137 control patients without fractures who underwent abdominal CT between January 2018 and February 2019. HUHA was calculated with the 3D volume of interest from the femoral head to the lesser trochanter. HUHA_fat (percentage of negative HU values) and HUHA_bone (percentage of HU values ≥ 125 HU) were assumed to be fat and bone components, respectively. Statistical significance was assessed using the area under the receiver operating characteristic curve (AUC), Spearman correlation (ρ), and odds ratio.

RESULTS

HUHA_fat was strongly positively correlated (ρ = 0.56) and BMD was moderately negatively correlated with fragility fractures (ρ =  - 0.37). AUC of HUHA_fat (0.82, 95% CI [0.77, 0.87]) significantly differed from that of BMD (0.69, 95% CI [0.63, 0.75]) (p < .001). The cutoff value was 15.8% for HUHA_fat (sensitivity: 90.4%; specificity: 67.7%) and 0.709 g/cm² for BMD (sensitivity: 87.5%; specificity: 51.5%), with higher HUHA_fat and lower BMD values indicating fragility fractures. The odds ratio of HUHA_fat was 19.5 (95% CI [9.9, 38.2], p < .001), which was higher than that of BMD, 7.4 (95% CI [4.0, 13.6], p < .001).

CONCLUSION

HUHA_fat revealed better performance than BMD and demonstrated feasibility in assessing proximal femoral fragility fractures.

KEY POINTS

• HUHA_fat showed a strong positive correlation (Spearman ρ = 0.56, p < .001), and BMD showed a moderate negative correlation (Spearman ρ =  - 0.37, p < .001) with proximal femoral fragility fractures. • HUHA_fat (AUC = 0.82) performed significantly better than BMD in assessing proximal femoral fragility fractures (AUC = 0.69) (p < .001). • The odds ratio of HUHA_fat for proximal femoral fragility fractures was higher than that of BMD (19.5 and 7.4, respectively; p < .001).

CT Hounsfield unit and histogram analysis for differentiation of recent versus remote vertebral compression fractures

OBJECTIVES

The purpose of this study was to analyze the intraosseous tissue changes in recent vertebral compression fractures (VCFs) and to differentiate recent from remote VCFs using CT Hounsfield unit histogram analysis (HUHA).

METHODS

65 patients with T11 to L3 VCFs were included. HUHA of 2 vertebral bodies (VBs)- a fractured VB and the closest lower-level unaffected VB-was done. The mean Hounsfield unit (HU) value and HU proportions of 5 ranges (HU ≤ 0, 0 < HU ≤ 50, 50 < HU ≤ 100, 100 < HU ≤ 150, and HU > 150) were obtained. Then, ΔHU value and ΔHU proportion were calculated by subtracting the values from the two vertebrae. Finally, the obtained values were compared between the recent and remote VCF groups and subjected to ROC curve analysis.

RESULTS

In recent VCF group, the ΔHU proportion (HU ≤ 0) corresponding to normal fatty marrow was lower (-0.17 vs 0.01) and the ΔHU proportion (HU > 150) representing trabecular bone was higher (0.23 vs 0.04) than in remote VCF group (p < 0.001). In the differentiation of recent from remote VCF, the ΔHU value and ΔHU proportion (HU > 150) showed high area under the curve (AUC, 0.939 and 0.912, respectively).

CONCLUSION

CT HUHA demonstrated both trabecular bone and bone marrow changes in recent VCFs, and showed high diagnostic performance in differentiating between recent and remote VCFs.

ADVANCES IN KNOWLEDGE

With its vendor neutral applicability, CT HUHA can be used for the differentiation of recent and remote VCFs.

Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study

Background

Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is silent before major fragility fracture and the osteoporosis screening rate is low. Abdomen-pelvic CT (APCT) is one of the most widely conducted medical tests. Artificial intelligence and radiomics analysis have recently been spotlighted. This is the first study to evaluate the prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT.

Materials and methods

500 patients (M: F = 70:430; mean age, 66.5 ± 11.8yrs; range, 50–96 years) underwent both dual-energy X-ray absorptiometry and APCT within 1 month. The volume of interest of the left proximal femur was extracted and 41 radiomics features were calculated using 3D volume of interest analysis. Top 10 importance radiomic features were selected by the intraclass correlation coefficient and random forest feature selection. Study cohort was randomly divided into 70% of the samples as the training cohort and the remaining 30% of the sample as the validation cohort. Prediction performance of machine-learning analysis was calculated using diagnostic test and comparison of area under the curve (AUC) of receiver operating characteristic curve analysis was performed between training and validation cohorts.

Results

The osteoporosis prevalence of this study cohort was 20.8%. The prediction performance of the machine-learning analysis to diagnose osteoporosis in the training and validation cohorts were as follows; accuracy, 92.9% vs. 92.7%; sensitivity, 86.6% vs. 80.0%; specificity, 94.5% vs. 95.8%; positive predictive value, 78.4% vs. 82.8%; and negative predictive value, 96.7% vs. 95.0%. The AUC to predict osteoporosis in the training and validation cohorts were 95.9% [95% confidence interval (CI), 93.7%-98.1%] and 96.0% [95% CI, 93.2%-98.8%], respectively, without significant differences (P = 0.962).

Conclusion

Prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT showed high validity with more than 93% accuracy, specificity, and negative predictive value.

Exploratory models comparing ethiodized oil-glue and gold fiducials for bladder radiotherapy image-guidance

Background and purpose

Image-guidance with fiducials has been shown to improve pelvic radiotherapy outcome. However, bladder fiducials using ethiodized oil (EO) alone can disperse widely, and gold causes Computed Tomography scan (CT) metal artifacts. The study's purpose was to investigate the ability to deliver EO-tissue glue fiducials and compare them to gold for bladder radiotherapy image guidance.

Materials and methods

A fluid-filled porcine bladder model was used to assess the ability to cystoscopically inject visible EO glue fiducials into the submucosa. We then transferred the bladders into a porcine pelvis for imaging and compared them to gold fiducials using CT, Cone Beam CT (CBCT), and kilovoltage (KV) planar views. A tissue-equivalent phantom was utilized to analyze the CT number Hounsfield Unit (HU) characteristics and artifacts of the glue and gold fiducials. Percentile ranges and normal tissue voxel percentages of the subsequent CT number voxel histogram from a 2 cm sphere surrounding the fiducial was used to characterize the artifact.

Results

We successfully delivered all EO glue fiducials into the porcine bladders as discrete fiducials. They were well seen on CT, CBCT, and KV imaging. The glue fiducials had lower CT number values, but less CT number spread of the voxel percentile ranges consistent with the diminished contrast and less artifact than gold. The glue fiducial types had similar CT number characteristics.

Conclusion

This study has shown that EO glue fiducials can be delivered with online visualization qualities comparable to gold fiducials without metal-related artifacts.

Reliability of 3D image analysis and influence of contrast medium administration on measurement of Hounsfield unit values of the proximal femur

PURPOSE

To evaluate the reliability of 3D image analysis and the effect of an iodine contrast agent on the computed tomography (CT) Hounsfield unit (HU) values of the proximal femur.

MATERIALS AND METHODS

Fifty female patients (mean age, 61.3 years; age range, 50-79 years) who underwent both pre- and post-enhancement abdominopelvic CT scans were included in this retrospective study. Whole 3D volumes of the left proximal femur from the head to the lesser trochanter were extracted using the regional growth technique with commercial 3D software. Total volume, mean HU, and HU histogram analysis (HUHA) values of the extracted femur were calculated. HUHA distribution was classified into HUHAfat for the assumed fatty marrow (percentage of negative HU values) and HUHAdense-bone (percentage of HU values ≥ 126 HU). Reliability was assessed by calculating intra- and interobserver correlation coefficients (ICCs) and by drawing Bland-Altman plots. The effect of contrast medium administration was evaluated by the paired t-test.

RESULTS

All intra- and interobserver ICCs of 3D volume measurements showed excellent reproducibility (all ICCs > 0.90). On Bland-Altman analysis of two observers' 3D volume measurements, the differences in the mean total volume, HUHAfat, HUHAdense-bone, and mean HU were 2.4 cm3, 0.17%, 0.6%, and 1.9 HU, respectively. The mean difference in HU after contrast agent administration (-2.2 HU) was not significant (P = 0.27). The mean difference in HUHAfat and HUHAdense-bone after contrast agent administration were -1.1% and -2.2%, respectively, on the Bland-Altman plot. HUHAfat and HUHAdense-bone showed significant differences (P < 0.05). The 95% limits of agreement for HUHAfat, HUHAdense-bone, and mean HU were -3.6% to 1.3%, -6.5% to 2.1%, and -30.0 to 25.5 HU, respectively.

CONCLUSION

Image analysis based on 3D volume measurement of the proximal femur showed excellent reliability, with the contrast agent administration showing negligible influence on the mean HU.

ICU admission body composition: skeletal muscle, bone, and fat effects on mortality and disability at hospital discharge-a prospective, cohort study

Background

Reduced body weight at the time of intensive care unit (ICU) admission is associated with worse survival, and a paradoxical benefit of obesity has been suggested in critical illness. However, no research has addressed the survival effects of disaggregated body constituents of dry weight such as skeletal muscle, fat, and bone density.

Methods

Single-center, prospective observational cohort study of medical ICU (MICU) patients from an academic institution in the USA. Five hundred and seven patients requiring CT scanning of chest or abdomen within the first 24 h of ICU admission were evaluated with erector spinae muscle (ESM) and subcutaneous adipose tissue (SAT) areas and with bone density determinations at the time of ICU admission, which were correlated with clinical outcomes accounting for potential confounders.

Results

Larger admission ESM area was associated with decreased odds of 6-month mortality (OR per cm², 0.96; 95% CI, 0.94–0.97; p < 0.001) and disability at discharge (OR per cm², 0.98; 95% CI, 0.96–0.99; p = 0.012). Higher bone density was similarly associated with lower odds of mortality (OR per 100 HU, 0.69; 95% CI, 0.49–0.96; p = 0.027) and disability at discharge (OR per 100 HU, 0.52; 95% CI, 0.37–0.74; p < 0.001). SAT area was not significantly associated with these outcomes’ measures. Multivariable modeling indicated that ESM area remained significantly associated with 6-month mortality and survival after adjusting for other covariates including preadmission comorbidities, albumin, functional independence before admission, severity scores, age, and exercise capacity.

Conclusion

In our cohort, ICU admission skeletal muscle mass measured with ESM area and bone density were associated with survival and disability at discharge, although muscle area was the only component that remained significantly associated with survival after multivariable adjustments. SAT had no association with the analyzed outcome measures.

The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population

OBJECTIVE

Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray features extracted by deep learning to alert high-risk osteoporosis populations.

MATERIALS AND METHODS

We retrospectively used data obtained from health check-ups including spine X-ray and dual-energy X-ray absorptiometry (DXA). Consecutively, we selected people with normal and abnormal bone mineral density. From the regions of interest of X-ray images, deep convolutional networks were used to generate image features. We designed prediction models for abnormal BMD using the image features trained by machine learning classification algorithms. The performances of each model were evaluated.

RESULTS

From 334 participants, 170 images of abnormal (T scores < - 1.0 standard deviations (SD)) and 164 of normal BMD (T scores > = - 1.0 SD) were used for analysis. We found that a combination of feature extraction by VGGnet and classification by random forest based on the maximum balanced classification rate (BCR) yielded the best performance in terms of the area under the curve (AUC) (0.74), accuracy (0.71), sensitivity (0.81), specificity (0.60), BCR (0.70), and F1-score (0.73).

CONCLUSION

In this study, we explored various machine learning algorithms for the prediction of BMD using simple spine X-ray image features extracted by three deep learning algorithms. We identified the combination for the best performance in predicting high-risk populations with abnormal BMD.

Measurement of Pancreatic Fat Fraction by CT Histogram Analysis to Predict Pancreatic Fistula after Pancreaticoduodenectomy

Objective

To evaluate the effectiveness of computed tomography (CT) Hounsfield unit histogram analysis (HUHA) in postoperative pancreatic fistula (PF) prediction.

Materials and Methods

Fifty-four patients (33 males and 21 females; mean age, 65.6 years; age range, 37–89 years) who had undergone preoperative CT and pancreaticoduodenectomy were included in this retrospective study. Two radiologists measured mean CT Hounsfield unit (CTHU) values by drawing regions of interest (ROIs) at the level of the pancreaticojejunostomy site on preoperative pre-contrast images. The HUHA values were arbitrarily divided into three categories, comprising HUHA-A ≤ 0 HU, 0 HU < HUHA-B < 30 HU, and HUHA-C ≥ 30 HU. Each HUHA value within the ROI was calculated as a percentage of the entire area using commercial 3-dimensional analysis software. Pancreas texture was evaluated as soft or hard by manual palpation.

Results

Fifteen patients (27.8%) had clinically relevant PFs. The PF group had significantly higher HUHA-A (p < 0.01) and significantly lower mean CTHU (p < 0.01) values than those of the non-PF group. The HUHA-A value had a moderately strong correlation with PF occurrence (r = 0.60, p < 0.01), whereas the mean CTHU had a weak negative correlation with PF occurrence (r = −0.27, p < 0.01). The HUHA-A and mean CTHU areas under the curve (AUCs) for predicting PF occurrence were 0.86 and 0.65, respectively, with significant difference (p < 0.01). The HUHA-A and mean CTHU AUCs for predicting pancreatic softness were 0.86 and 0.64, respectively, with significant difference (p < 0.01).

Conclusion

The HUHA-A values on preoperative pre-contrast CT images demonstrate a strong correlation with PF occurrence.

Histogram analysis with computed tomography angiography for discriminating soft tissue sarcoma from benign soft tissue tumor

To investigate the feasibility of histogram analysis with computed tomography angiography (CTA) in distinguishing between soft tissue sarcomas and benign soft tissue tumors. Fourty nine patients (23 men, mean age = 44.3 years, age range = 25-64) with pathologically-confirmed soft tissue sarcoma (n = 24) or benign soft tissue tumors (n = 25) in the lower extremities undergoing CTA for tumor evaluation were retrospectively analyzed. Two radiologists separately performed histogram analyses of CT density with CTA images by drawing a region of interest (ROI). The 10th (P10), 25th (P25), 50th (P50), 75th (P75), 90th percentiles (P90), mean, and standard deviations (SD) of measured tumor density were obtained along with measurements of the absolute value of kurtosis (AVK), absolute value of skewness (AVS), and inhomogeneity for each tumor. Intra-class correlation coefficients (ICC) were calculated to determine inter- and intra-reader variability in parameter measurements. The Mann-Whitney U test was used to compare histogram parameters between soft tissue sarcomas and benign soft tissue tumors. Receiver operator characteristic (ROC) curves were constructed to evaluate the accuracy of tumor discrimination. ICC was greater than 0.7 for AVS, AVK, and inhomogeneity, and >0.9 for mean, SD, and all percentile measures. There was no significant difference in P10, P25, P50, P75, P90, mean, or SD between soft tissue sarcomas and benign tumors (P > .05). AVS, AVK, and inhomogeneity were significantly higher in soft tissue sarcomas (P < .05). Areas under the curve (AUC) were 0.81, 0.83, and 0.84 for AVS, AVK, and inhomogeneity respectively. AUC were below 0.6 for mean, SD, and all percentiles.Skewness, kurtosis, and inhomogeneity measurements derived from histogram analysis from CTA distinguish between soft tissue sarcomas and benign soft tissue tumors.

Use of Bone Health Evaluation in Orthopedic Surgery: 2019 ISCD Official Position

This position development conference (PDC) Task Force examined the assessment of bone status in orthopedic surgery patients. Key questions included which orthopedic surgery patients should be evaluated for poor bone health prior to surgery and which subsets of patients are at high risk for poor bone health and adverse outcomes. Second, the reliability and validity of using bone densitometry techniques and measurement of specific geometries around the hip and knee before and after arthroplasty was determined. Finally, the use of computed tomography (CT) attenuation coefficients (Hounsfield units) to estimate bone quality at anatomic locations where orthopedic surgery is performed including femur, tibia, shoulder, wrist, and ankle were reviewed. The literature review identified 665 articles of which 198 met inclusion exclusion criteria and were selected based on reporting of methodology, reliability, or validity results. We recommend that the orthopedic surgeon be aware of established ISCD guidelines for determining who should have additional screening for osteoporosis. Patients with inflammatory arthritis, chronic corticosteroid use, chronic renal disease, and those with history of fracture after age 50 are at high risk of osteoporosis and adverse events from surgery and should have dual energy X-ray absorptiometry (DXA) screening before surgery. In addition to standard DXA, bone mineral density (BMD) measurement along the femur and proximal tibia is reliable and valid around implants and can provide valuable information regarding bone remodeling and identification of loosening. Attention to positioning, selection of regions of interest, and use of special techniques and software is required. Plain radiographs and CT provide simple, reliable methods to classify the shape of the proximal femur and to predict osteoporosis; these include the Dorr Classification, Cortical Index, and critical thickness. Correlation of these indices to central BMD is moderate to good. Many patients undergoing orthopedic surgery have had preoperative CT which can be utilized to assess regional quality of bone. The simplest method available on most picture archiving and communications systems is to simply measure a regions of interest and determine the mean Hounsfield units. This method has excellent reliability throughout the skeleton and has moderate correlation to DXA based on BMD. The prediction of outcome and correlation to mechanical strength of fixation of a screw or implant is unknown.