Computed Tomography (CT) scans of the cervical spine are often performed to evaluate patients for trauma and degenerative changes of the cervical spine. We hypothesized that the CT attenuation of the ...cervical vertebrae can be used to identify patients who should be screened for osteoporosis.
A retrospective study of 253 patients (177 training/validation and 76 test) with unenhanced CT scans of the cervical spine and Dual-energy x-ray Absorbtiometry (DXA) studies within 12 months of each other was performed. Volumetric segmentation of C1-T1, clivus, and first ribs was performed to obtain the CT attenuation of each bone. The correlations of the CT attenuations between the bones and with DXA measurements were evaluated. Univariate receiver operator characteristic (ROC) analyses, and multivariate classifiers (Random Forest (RF), XGBoost, Naïve Bayes (NB), and Support Vector Machines (SVM)) analyzing the CT attenuation of all bones, were utilized to predict patients with osteopenia/osteoporosis and femoral neck bone mineral density (BMD) T-scores <-1.
There were positive correlations between the CT attenuation of each bone, and with the DXA measurements. A CT attenuation threshold of 305.2 Hounsfield Units (HU) at C3 had the highest accuracy (0.763, AUC=0.814) to detect femoral neck BMD T-scores ≤-1 and a CT attenuation threshold of 323.6 HU at C3 had the highest accuracy (0.774, AUC=0.843) to detect osteopenia/osteoporosis. The SVM classifier (AUC=0.756) had higher AUC than the RF (AUC=0.692, P=0.224), XGBoost (AUC=0.736; P=0.814), NB (AUC=0.622, P=0.133) and CT threshold of 305.2 HU at C3 (AUC=0.704, P=0.531) classifiers to identify patients with femoral neck BMD T-scores <-1. The SVM classifier (accuracy=0.816) was more accurate than using the CT threshold of 305.2 HU at C3 (accuracy=0.671) (McNemar's χ12=7.55, P=0.006).
Opportunistic screening for low BMD can be done using cervical spine CT scans. A SVM classifier was more accurate than using the CT threshold of 305.2 HU at C3.
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•We show that C3 is the best bone for opportunistic screening for osteoporosis/osteopenia from cervical spine CT•We use machine learning involving the CT attenuation data from multiple osseous sites to better identify patients with osteoporosis/osteopenia•We include the CT attenuation of all bones routinely visible on CT scans of the cervical spine including clivus, T1 and the first rib•We use three-dimensional (3D) volumetric segmentation of the vertebral bodies so that the entire trabecular bone is evaluated rather than sampling the trabecular bone on a single CT slice.
Patients with low bone mineral density (BMD) are at risk for fractures however are often undiagnosed. Therefore, there is a need to opportunistically screen for low BMD in patients who present for ...other studies. This is a retrospective study of 812 patients aged 50 years or older who had dual-energy X-ray absorptiometry (DXA) and radiographs of the hands within 12 months of each other. This dataset was randomly split into training/validation (n=533) and test (n=136) datasets. A deep learning (DL) framework was used to predict osteoporosis/osteopenia. Correlations between the textural analysis of the bones and DXA measurements were obtained. We found that the DL model had an accuracy of 82.00%, sensitivity of 87.03%, specificity of 61.00% and an area under the curve (AUC) of 74.00% to detect osteoporosis/osteopenia. Our findings show that radiographs of the hand can be used to screen for osteoporosis/osteopenia and identify patients who should get formal DXA evaluation.
Purpose
One or more vertebrae are sometimes excluded from dual-energy X-ray absorptiometry (DXA) analysis if the bone mineral density (BMD) T-score estimates are not consistent with the other lumbar ...vertebrae BMD T-score estimates. The goal of this study was to build a machine learning framework to identify which vertebrae would be excluded from DXA analysis based on the computed tomography (CT) attenuation of the vertebrae.
Methods
Retrospective review of 995 patients (69.0% female) aged 50 years or greater with CT scans of the abdomen/pelvis and DXA within 1 year of each other. Volumetric semi-automated segmentation of each vertebral body was performed using 3D-Slicer to obtain the CT attenuation of each vertebra. Radiomic features based on the CT attenuation of the lumbar vertebrae were created. The data were randomly split into training/validation (90%) and test datasets (10%). We used two multivariate machine learning models: a support vector machine (SVM) and a neural net (NN) to predict which vertebra(e) were excluded from DXA analysis.
Results
L1, L2, L3, and L4 were excluded from DXA in 8.7% (87/995), 9.9% (99/995), 32.3% (321/995), and 42.6% (424/995) patients, respectively. The SVM had a higher area under the curve (AUC = 0.803) than the NN (AUC = 0.589) for predicting whether L1 would be excluded from DXA analysis (P = 0.015) in the test dataset. The SVM was better than the NN for predicting whether L2 (AUC = 0.757 compared to AUC = 0.478), L3 (AUC = 0.699 compared to AUC = 0.555), or L4 (AUC = 0.751 compared to AUC = 0.639) were excluded from DXA analysis.
Conclusions
Machine learning algorithms could be used to identify which lumbar vertebrae would be excluded from DXA analysis and should not be used for opportunistic CT screening analysis. The SVM was better than the NN for identifying which lumbar vertebra should not be used for opportunistic CT screening analysis.
Purpose
To evaluate whether mediopatellar plica and knee morphometric measurements obtained from magnetic resonance imaging (MRI) studies are associated with isolated medial patellofemoral ...osteoarthritis in young adults.
Methods
MRI studies from 60 patients with isolated medial patellofemoral osteoarthritis and 90 control patients with normal knee MRI studies were reviewed. The presence of mediopatellar plica, the presence of edema in the superolateral aspect of Hoffa's fat pad and suprapatellar fat pad, quadriceps and patellar tendinosis, and axial and sagittal alignment of the patellar and trochlear morphology were assessed using MRI. The relationship between mediopatellar plica, alignment, or morphology and the presence of isolated medial patellofemoral osteoarthritis was evaluated using logistic regression.
Results
Superolateral Hoffa's fat pad edema (odds ratio OR = 3.4, P = .009) and decreased trochlear sulcal angle (OR = 0.95, P = .045) were associated with increased odds of isolated medial patellofemoral osteoarthritis. Decreased lateral patellar tilt (OR = 0.93, P = .087) and patellar tendinosis (OR = 4.13, P = .103) trended toward being associated with increased odds of isolated medial patellofemoral osteoarthritis but were not statistically significant. No significant association was seen between the presence of mediopatellar plica and medial patellofemoral osteoarthritis (OR = 0.95, P = .353).
Conclusions
Medial patellofemoral osteoarthritis is associated with trochlear morphology and patellar alignment but not with mediopatellar plica.
With increasing efforts to create a diverse physician workforce that is reflective of the demographic characteristics of the US population, it remains unclear whether progress has been made since ...2009, when the Liaison Committee on Medical Education set forth new diversity accreditation guidelines.
To examine demographic trends of medical school applicants and matriculants relative to the overall age-adjusted US population.
Repeated cross-sectional study of Association of American Medical Colleges data on self-reported race/ethnicity and sex of medical school applicants and matriculants compared with population distribution of the medical school-aged population (20-34 years). Data from US allopathic medical school applicants and matriculants from 2002 to 2017 were analyzed.
Trends were measured using the representation quotient, the ratio of the proportion of a racial/ethnic group in the medical student body to the general age-matched US population. Linear regression estimates were used to evaluate the trend over time for Asian, black, white, Hispanic, American Indian or Alaska Native (AIAN), and Native Hawaiian or Other Pacific Islander medical school matriculants by sex.
The number of medical school applicants increased 53%, from 33 625 to 51 658, and the number of matriculants increased 29.3%, from 16 488 to 21 326, between 2002 and 2017. During that time, proportions of black, Hispanic, Asian, and Native Hawaiian or Other Pacific Islander male and female individuals aged 20 to 34 years in the United States increased, while proportions of white male and female individuals decreased and proportions of AIAN male and female individuals were stable. From 2002 to 2017, black, Hispanic, and AIAN applicants and matriculants of both sexes were underrepresented, with a significant trend toward decreased representation for black female applicants from 2002 to 2012 (representation quotient slope, -0.011; 95% CI, -0.015 to -0.007; P < .001).
Black, Hispanic, and AIAN students remain underrepresented among medical school matriculants compared with the US population. This underrepresentation has not changed significantly since the institution of the Liaison Committee of Medical Education diversity accreditation guidelines in 2009. This study's findings suggest a need for both the development and the evaluation of more robust policies and programs to create a physician workforce that is demographically representative of the US population.
The objective of our study was to assess whether the maximum and mean CT attenuations are accurate for differentiating between enostoses and treated sclerotic metastases.
We retrospectively reviewed ...CT studies of 165 patients (167 lesions) that included 49 patients with 49 benign lesions, 69 patients with 71 sclerotic treated lesions, and 47 patients with 47 untreated lesions, and calculated the mean and maximum CT attenuations of each lesion. ROC curves were used to identify thresholds for differentiating enostoses from treated sclerotic metastases and from untreated sclerotic metastases.
The maximum CT attenuation of enostoses (1212.0 HU) was higher from that of untreated (754.7 HU) (p = 9.7 × 10
) and that of treated (891.7 HU) (p = 9.9 × 10
) sclerotic metastases. The maximum CT attenuation of treated sclerotic metastases (891.7 HU) was higher than that of untreated sclerotic metastases (754.7 HU) (p = 0.003). Enostoses had higher mean CT attenuation (1123.0 HU) than untreated (602.0 HU) (p < 2.2 × 10
) and treated (731.7 HU) (p = 9.6 × 10
) sclerotic metastases. A threshold mean CT attenuation of 885 HU had an accuracy of 91.7% and 81.7% to differentiate enostoses from untreated and treated metastases, respectively, whereas a threshold maximum CT attenuation of 1060.0 HU had an accuracy of 81.3% and 72.5% to differentiate enostoses from untreated and treated metastases.
The mean and maximum CT attenuations can differentiate between enostoses and sclerotic metastases; however, the accuracy of both metrics decreases after treatment.
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