Incidental vertebral compression fractures in imaging studies: Lessons not learned by radiologists

A deep learning pipeline for systematic and accurate vertebral fracture reporting in computed tomography

AIM

Spine fractures are a frequent and relevant diagnosis, but systematic documentation is time-consuming and sometimes overlooked. A deep learning pipeline for opportunistic fracture detection in computed tomography (CT) spine images of varying field-of-views is introduced.

MATERIALS AND METHODS

This retrospective study builds on 452 CTs of the lumbar/thoracolumbar spine. Patients were included based on the evidence of ≥1 vertebral body fracture and excluded in case of history of spinal surgery or pathologic fractures. The collective was split into training/validation (405) and test (47) sets. An open-source spine dataset was used to train a preliminary segmentation model, which was applied on the training set. The resulting segmentation was post-processed to remove posterior vertebral structures and if needed, manually refined by a radiologist. Using the refined version as new training data, a final segmentation nnU-net was trained. Sagittal slices from each vertebra were labelled individually with regard to fracture evidence. Slices without fracture were used as negative class. Twenty seven thousand nineteen slices (20,396 negative, 6,623 positive) trained a classification algorithm using resnet18. Two senior readers independently assessed fractures in the test set to obtain a consensual ground truth. The segmentation-classification pipeline was applied to the test set and compared with the ground truth.

RESULTS

The segmentation model correctly segmented 330/339 (97%) vertebrae. Considering every segmented vertebra, the classifier detected fractures with 88% sensitivity, 95% specificity, and 93% accuracy.

CONCLUSION

A deep learning pipeline was built and shown to accurately detect fractures on CT images. The final models as well as our code material are available at https://github.com/usb-radiology/VertebraeFx.

Automated vertebral compression fracture detection and quantification on opportunistic CT scans: a performance evaluation

AIM

Since the majority of vertebral compression fractures (VCFs) are asymptomatic, they often go undetected on opportunistic CT scans. To reduce rates of undiagnosed osteoporosis, we developed a deep learning (DL)-based algorithm using 2D/3D U-Nets convolutional neural networks to opportunistically screen for VCF on CT scans. This study aimed to evaluate the performance of the algorithm using external real-world data.

MATERIALS AND METHODS

CT scans acquired for various indications other than a suspicion of VCF from January 2019 to August 2020 were retrospectively and consecutively collected. The algorithm was designed to label each vertebra, detect VCF, measure vertebral height loss (VHL) and calculate mean Hounsfield Units (mean HU) for vertebral bone attenuation. For the ground truth, two board-certified radiologists defined if VCF was present and performed the measurements. The algorithm analyzed the scans and the results were compared to the experts' assessments.

RESULTS

A total of 100 patients (mean age: 76.6 years ± 10.1[SD], 72% women) were evaluated. The overall labeling agreement was 94.9% (95%CI: 93.7%-95.9%). Regarding VHL, the 95% limits of agreement (LoA) between the algorithm and the radiologists was [-9.3, 8.6]; 94.1% of the differences lay within the radiologists' LoA and the intraclass correlation coefficient was 0.854 (95%CI: 0.822-0.881). For the mean HU, Pearson's correlation was 0.89 (95%CI: 0.84-0.92; p-value <0.0001). Finally, the algorithm's VCF screening sensitivity and specificity were 92.3% (95%CI: 81.5%-97.9%) and 91.7% (95%CI: 80.0%-97.7%), respectively.

CONCLUSIONS

This automated tool for screening and quantification of opportunistic VCF demonstrated high reliability and performance that may facilitate radiologists' task and improve opportunistic osteoporosis assessments.

Risk of Subsequent Hip Fractures across Varying Treatment Patterns for Index Vertebral Compression Fractures

Introduction: Vertebral compression fractures (VCFs) pose a considerable healthcare burden and are linked to elevated morbidity and mortality. Despite available anti-osteoporotic treatments (AOTs), guideline adherence is lacking. This study aims to evaluate subsequent hip fracture incidence after index VCF and to elucidate AOT prescribing patterns in VCF patients, further assessing the impact of surgical interventions on these patterns. Materials and Methods: Patients with index VCFs between 2010 and 2021 were identified using the PearlDiver database. Diagnostic and procedural data were recorded using International Classification of Diseases (ICD-9, ICD-10) and Current Procedural Terminology (CPT) codes. Patients under age 50 and follow-up Collapse

Key Words

• anti-osteoporotic treatment
• bisphosphonates
• fragility fracture
• hip fracture
• osteoporosis
• vertebral compression fracture

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Affiliation(s)

Andy Ton Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)
Jennifer A. Bell Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)
William J. Karakash Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)
Thomas D. Alter Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN 55905, USA
Mary Kate Erdman Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.) Department of Orthopaedic Surgery, University of Chicago Medical Center, Chicago, IL 60612, USA
Hyunwoo Paco Kang Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)
Emily S. Mills Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)
Jonathan Mina Ragheb Department of Orthopaedic Surgery, Kaiser Permanente Bernard J. Tyson School of Medicine, Los Angeles, CA 91101, USA
Mirbahador Athari Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)
Jeffrey C. Wang Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)
Ram K. Alluri Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)
Raymond J. Hah Department of Orthopaedic Surgery, Keck School of Medicine, The University of Southern California, Los Angeles, CA 90033, USA; (A.T.); (W.J.K.)

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Accuracy of an artificial intelligence algorithm for detecting moderate-to-severe vertebral compression fractures on abdominal and thoracic computed tomography scans

Objective

To describe the accuracy of HealthVCF, a software product that uses artificial intelligence, in the detection of incidental moderate-to-severe vertebral compression fractures (VCFs) on chest and abdominal computed tomography scans.

Materials and Methods

We included a consecutive sample of 899 chest and abdominal computed tomography scans of patients 51-99 years of age. Scans were retrospectively evaluated by the software and by two specialists in musculoskeletal imaging for the presence of VCFs with vertebral body height loss > 25%. We compared the software analysis with that of a general radiologist, using the evaluation of the two specialists as the reference.

Results

The software showed a diagnostic accuracy of 89.6% (95% CI: 87.4-91.5%) for moderate-to-severe VCFs, with a sensitivity of 73.8%, a specificity of 92.7%, and a negative predictive value of 94.8%. Among the 145 positive scans detected by the software, the general radiologist failed to report the fractures in 62 (42.8%), and the algorithm detected additional fractures in 38 of those scans.

Conclusion

The software has good accuracy for the detection of moderate-to-severe VCFs, with high specificity, and can increase the opportunistic detection rate of VCFs by radiologists who do not specialize in musculoskeletal imaging.

External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans

The Convolutional Neural Network algorithm achieved a sensitivity of 94% and specificity of 93% in identifying scans with vertebral fractures (VFs). The external validation results suggest that the algorithm provides an opportunity to aid radiologists with the early identification of VFs in routine CT scans of abdomen and chest.

PURPOSE

To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort.

METHODS

Two Chinese studies and clinical data were used to retrospectively select CT scans of the chest, abdomen and thoracolumbar spine in men and women aged ≥50 years. The CT scans were assessed using the semiquantitative (SQ) Genant classification for prevalent VFs in a process blinded to clinical information. The performance of the CNN model was evaluated against reference standard readings by the area under the receiver operating characteristics curve (AUROC), accuracy, Cohen's kappa, sensitivity, and specificity.

RESULTS

A total of 4,810 subjects were included, with a median age of 62 years (IQR 56-67), of which 2,654 (55.2%) were females. The scans were acquired between January 2013 and January 2019 on 16 different CT scanners from three different manufacturers. 2,773 (57.7%) were abdominal CTs. A total of 628 scans (13.1%) had ≥1 VF (grade 2-3), representing 899 fractured vertebrae out of a total of 48,584 (1.9%) visualized vertebral bodies. The CNN's performance in identifying scans with ≥1 moderate or severe fractures achieved an AUROC of 0.94 (95% CI: 0.93-0.95), accuracy of 93% (95% CI: 93%-94%), kappa of 0.75 (95% CI: 0.72-0.77), a sensitivity of 94% (95% CI: 92-96%) and a specificity of 93% (95% CI: 93-94%).

CONCLUSION

The algorithm demonstrated excellent performance in the identification of vertebral fractures in a cohort of chest and abdominal CT scans of Chinese patients ≥50 years.

Towards Improved Identification of Vertebral Fractures in Routine Computed Tomography (CT) Scans: Development and External Validation of a Machine Learning Algorithm

Vertebral fractures (VFs) are the hallmark of osteoporosis, being one of the most frequent types of fragility fracture and an early sign of the disease. They are associated with significant morbidity and mortality. VFs are incidentally found in one out of five imaging studies, however, more than half of the VFs are not identified nor reported in patient computed tomography (CT) scans. Our study aimed to develop a machine learning algorithm to identify VFs in abdominal/chest CT scans and evaluate its performance. We acquired two independent data sets of routine abdominal/chest CT scans of patients aged 50 years or older: a training set of 1011 scans from a non-interventional, prospective proof-of-concept study at the Universitair Ziekenhuis (UZ) Brussel and a validation set of 2000 subjects from an observational cohort study at the Hospital of Holbaek. Both data sets were externally reevaluated to identify reference standard VF readings using the Genant semiquantitative (SQ) grading. Four independent models have been trained in a cross-validation experiment using the training set and an ensemble of four models has been applied to the external validation set. The validation set contained 15.3% scans with one or more VF (SQ2-3), whereas 663 of 24,930 evaluable vertebrae (2.7%) were fractured (SQ2-3) as per reference standard readings. Comparison of the ensemble model with the reference standard readings in identifying subjects with one or more moderate or severe VF resulted in an area under the receiver operating characteristic curve (AUROC) of 0.88 (95% confidence interval [CI], 0.85-0.90), accuracy of 0.92 (95% CI, 0.91-0.93), kappa of 0.72 (95% CI, 0.67-0.76), sensitivity of 0.81 (95% CI, 0.76-0.85), and specificity of 0.95 (95% CI, 0.93-0.96). We demonstrated that a machine learning algorithm trained for VF detection achieved strong performance on an external validation set. It has the potential to support healthcare professionals with the early identification of VFs and prevention of future fragility fractures. © 2023 UCB S.A. and The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Screening for osteoporosis based on IQon spectral CT virtual low monoenergetic images: Comparison with conventional 120 kVp images

Objectives

To explore the differences between low kiloelectron volt (keV) virtual monoenergetic images (VMIs) using IQon spectral CT and conventional CT (120 kVp) in the diagnosis of osteoporosis.

Methods

This retrospective study included 317 patients who underwent IQon spectral CT and dual-energy X-ray absorptiometry (DXA) examination. Commercial deep learning-based software was used for the fully automated extraction of the CT values of the first to fourth lumbar vertebrae (L1-L4) from two different low-keV levels (including 40/70 keV) VMIs and conventional 120 kVp images. The DXA examination results served as the standard of reference (normal [T-score ≥ -1], osteopenia [-2.5 < T-score < -1], and osteoporosis [T-score < -2.5]). Osteoporosis diagnosis models were constructed using machine learning classifiers (logistic regression, support vector machine, random forest, XGBoost, and multilayer perceptron) based on the average CT values of L1-L4. The area under the receiver operating characteristic curve (AUC) and DeLong test were performed to compare differences in the performance of the osteoporosis diagnosis model between virtual low-keV VMIs and standard 120 kVp images.

Results

Random forest-based prediction model obtained good overall performance among all classifiers, and macro/micro average AUC values of 0.820/840, 0.834/853, and 0.831/852 were obtained based on 40/70 keV and 120 kVp images, respectively. The model presented no significant difference between low-keV VMIs and standard 120 kVp images for the diagnosis of osteoporosis (p > 0.05).

Conclusions

The performance of the osteoporosis diagnosis model using IQon spectral CT simulating the low tube voltage scanning condition (less than 120 kVp) was also satisfactory. Bone density screening evaluation can be performed with a combination of low-dose lung scanning CT, greatly reducing the radiation dose without affecting the diagnosis.

A software program for automated compressive vertebral fracture detection on elderly women's lateral chest radiograph: Ofeye 1.0

Background

Because osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate.

Methods

For model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF. For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OVF classification included: minimal and mild grades with <20% or ≥20-25% vertebral height loss respectively, moderate grade with ≥25-40% vertebral height loss, severe grade with ≥40%-2/3 vertebral height loss, and collapsed grade with ≥2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed 'base-model 1.0'. A user-friendly interface was also developed, with the synthesized software termed 'Ofeye 1.0'.

Results

Counting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OVF cases. Eighteen OVFs in 15 cases had OVFs of ≥ moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with ≥ moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows 'batch processing', for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer.

Conclusions

A user-friendly software program was developed for CVF detection on elderly women's lateral chest radiographs. It has an overall low false positivity rate, and for moderate and severe CVFs an acceptably low false negativity rate. The integration of this software into radiological practice is expected to improve osteoporosis management for elderly women.

Multilevel "fish-vertebra" sign in a patient with idiopathic osteoporosis

"Fish-vertebra" is an uncommon deformity of vertebral body shape, consisting of central depression of the superior and inferior surfaces of vertebral bodies. It is characteristic in idiopathic osteoporosis and can help in the differential diagnosis of other conditions, such as osteomalacia, hyperparathyroidism, sickle cell disease, Paget disease, and multiple myeloma.

Assessment of Skeletal Strength: Bone Density Testing and Beyond

A bone fractures when a force applied to it exceeds its strength. Assessment of bone strength is an important component in determining the risk of fracture and guiding treatment decisions. Dual-energy X-ray absorptiometry is used to diagnosis osteoporosis, estimate fracture risk, and monitor changes in bone density. Fracture risk algorithms provide enhanced fracture risk predictability. Advanced technologies with computed tomography (CT) and MRI can measure parameters of bone microarchitecture. Mathematical modeling using CT data can evaluate the behavior of bone structures in response to external loading. Microindentation techniques directly measure the strength of outer bone cortex.

Opportunistic diagnosis of osteoporotic vertebral fractures on standard imaging performed for alternative indications

Osteoporotic vertebral fractures (VFs) are the most common type of osteoporotic fracture. Patients with VF are at increased risk of hip fractures or additional VFs, both of which contribute to patient morbidity and mortality. Early diagnosis of VFs is essential so patients can be prescribed appropriate medical therapy.

Most patients with clinical suspicion for VF have an X-ray of the spine. Many VFs are invisible on X-ray and require further imaging. CT can provide excellent bony detail but uses high doses of ionising radiation. MRI provides excellent soft tissue detail and can distinguish old from new fractures in addition to differentiating osteoporotic VFs from other causes of back pain. Bone scans have a limited role due to poor specificity.

The literature suggests that radiologists frequently miss or do not report VFs when imaging is requested for an alternative clinical indication and when there is no clinical suspicion of VF. Common examples include failure to identify VFs on lateral chest X-rays, sagittal reformats of CT thorax and abdomen, lateral localizers on MRI and scout views on CT.

Failure to diagnose a VF is a missed opportunity to improve management of osteoporosis and reduce risk of further fractures. This article discusses the role of radiographs, CT, MRI and bone scintigraphy in the assessment and recognition of osteoporotic fractures. This article focuses on opportunistic diagnosis of VFs on imaging studies that are performed for other clinical indications. It does not discuss use of dual energy X-ray absorptiometry which is a specific imaging modality for osteoporosis.

The role of radiography in the study of spinal disorders

Despite the growing use of computed tomography (CT) and magnetic resonance imaging (MRI) in the study of spinal disorders, radiography still plays an important role in many conditions affecting the spine. However, the study and interpretation of spine radiograph is receiving less attention and radiologists are increasingly unfamiliar with the typical findings in normal and pathologic conditions of the spine. The aim of this article is to review the radiologic indications of radiograph in different pathologic conditions that affect the spine, including congenital, traumatic, degenerative, inflammatory, infectious and tumour disorders, as well as their main radiographic manifestations.

Prevalence of Vertebral Fractures in CTPA's in Adults Aged 75 and Older and Their Association with Subsequent Fractures and Mortality

Identifying vertebral fractures is prudent in the management of osteoporosis and the current literature suggests that less than one-third of incidental vertebral fractures are reported. The aim of this study is to determine the prevalence of reported and unreported vertebral fractures in computerized tomography pulmonary angiograms (CTPA) and their relevance to clinical outcomes. All acutely unwell patients aged 75 or older who underwent CTPAs were reviewed retrospectively. 179 CTPAs were reviewed to identify any unreported vertebral fractures. A total of 161 were included for further analysis. Of which, 14.3% (23/161) were reported to have a vertebral fracture, however, only 8.7% (14/161) of reports used the correct terminology of ‘fracture’. On subsequent review, an additional 19.3% (31/161) were noted to have vertebral fractures. Therefore, the overall prevalence of vertebral fractures was 33.5% (54/161). A total of 22.2% (12/54) of patients with a vertebral fracture on CTPA sustained a new fragility fracture during the follow-up period (4.5 years). In comparison, a significantly lower 10.3% (11/107) of patients without a vertebral fracture developed a subsequent fragility fracture during the same period (p = 0.04). Overall mortality during the follow-up period was significantly higher for patients with vertebral fractures (68.5%, 37/54) as compared to those without (45.8%, 49/107, p = 0.006). Vertebral fractures within the elderly population are underreported on CTPAs. The significance of detecting incidental vertebral fractures is clear given the increased rates of subsequent fractures and mortality. Radiologists and physicians alike must be made aware of the importance of identifying and treating incidental, vertebral fragility fractures.

Distribution of Prevalent and Incident Vertebral Fractures and Their Association with Bone Mineral Density in Postmenopausal Women in the Teriparatide Versus Risedronate VERO Clinical Trial

Vertebral fractures (VFx) occur most frequently in the mid-thoracic and thoraco-lumbar regions, which experience the highest mechanical loading along the spine. The prevalence and incidence of VFx by their location and severity, and their relationship with bone mineral density (BMD), are seldom reported in randomized clinical trial cohorts. The VERO trial randomized 1360 postmenopausal women with at least two moderate or one severe VFx to receive either teriparatide or risedronate for up to 24 months. In this post hoc analysis, we describe the centrally read distribution and severity of prevalent and incident VFx, and the association of their location with the baseline BMD. At baseline, 21.4% of all evaluable vertebral bodies had a prevalent VFx; most commonly at L₁, T₁₂, L₂ and T₁₁ (38.5%, 37.4%, 25.3% and 23.5% of patients, respectively). Patients with prevalent VFx only at T₁₂/L₁ showed a higher baseline BMD compared to patients with VFx at other levels. At month 24, 100 patients had 126 incident VFx (teriparatide: 35; risedronate: 91). The most frequent incident VFx occurred at T₁₂ (n = 17, 1.6% of patients), followed by L₁ and T₁₁ (n = 14, 1.3% both). The frequency of incident VFx was lower at all vertebral levels in patients given teriparatide. These results confirm prior reports that VFx occurs more frequently at mid-thoracic and thoraco-lumbar regions of the spine. Patients with these VFx locations have higher BMD than those who fracture at other sites, suggesting a role for mechanical stress in the etiology of VFx. Teriparatide is superior to risedronate in the prevention of VFx at these common fracture locations.Trial registration ClinicalTrials.gov Identifier: NCT01709110.

Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network

OBJECTIVES

To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images.

METHODS

In this Institutional Review Board-approved retrospective study, patients who received both unenhanced CT examinations and dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae, in two institutions (1 and 2), were included. Supervised deep learning was employed to obtain a convolutional neural network (CNN) model using axial CT images, including the lumbar vertebrae as input data and BMD values obtained with DXA as reference data. For this purpose, 1665 CT images from 183 patients in institution 1, which were augmented to 99,900 (= 1665 × 60) images (noise adding, parallel shift and rotation were performed), were used. Internal (by using data of 45 other patients in institution 1) and external validations (by using data of 50 patients in institution 2) were performed to evaluate the performance of the trained CNN model. Correlations and diagnostic performances were evaluated with Pearson's correlation coefficient (r) and area under the receiver operating characteristic curve (AUC), respectively.

RESULTS

The estimated BMD values, according to the CNN model (BMDCNN), were significantly correlated with the BMD values obtained with DXA (r = 0.852 (p < 0.001) and 0.840 (p < 0.001) for the internal and external validation datasets, respectively). Using BMDCNN, osteoporosis was diagnosed with AUCs of 0.965 and 0.970 for the internal and external validation datasets, respectively.

CONCLUSIONS

Using deep learning, the BMD of lumbar vertebrae could be predicted from unenhanced abdominal CT images.

KEY POINTS

• By applying a deep learning technique, the bone mineral density (BMD) of lumbar vertebrae can be estimated from unenhanced abdominal CT images. • A strong correlation was observed between the estimated BMD and the BMD obtained with DXA. • By using the estimated BMD, osteoporosis could be diagnosed with high performance.

Secondary Fracture Prevention: Consensus Clinical Recommendations from a Multistakeholder Coalition

Osteoporosis-related fractures are undertreated, due in part to misinformation about recommended approaches to patient care and discrepancies among treatment guidelines. To help bridge this gap and improve patient outcomes, the American Society for Bone and Mineral Research assembled a multistakeholder coalition to develop clinical recommendations for the optimal prevention of secondary fractureamong people aged 65 years and older with a hip or vertebral fracture. The coalition developed 13 recommendations (7 primary and 6 secondary) strongly supported by the empirical literature. The coalition recommends increased communication with patients regarding fracture risk, mortality and morbidity outcomes, and fracture risk reduction. Risk assessment (including fall history) should occur at regular intervals with referral to physical and/or occupational therapy as appropriate. Oral, intravenous, andsubcutaneous pharmacotherapies are efficaciousandcanreduce risk of future fracture.Patientsneededucation,however, about thebenefitsandrisks of both treatment and not receiving treatment. Oral bisphosphonates alendronate and risedronate are first-line options and are generally well tolerated; otherwise, intravenous zoledronic acid and subcutaneous denosumab can be considered. Anabolic agents are expensive butmay be beneficial for selected patients at high risk.Optimal duration of pharmacotherapy is unknown but because the risk for second fractures is highest in the earlypost-fractureperiod,prompt treatment is recommended.Adequate dietary or supplemental vitaminDand calciumintake shouldbe assured. Individuals beingtreatedfor osteoporosis shouldbe reevaluated for fracture risk routinely, includingvia patienteducationabout osteoporosisandfracturesandmonitoringfor adverse treatment effects.Patients shouldbestronglyencouraged to avoid tobacco, consume alcohol inmoderation atmost, and engage in regular exercise and fall prevention strategies. Finally, referral to endocrinologists or other osteoporosis specialists may be warranted for individuals who experience repeated fracture or bone loss and those with complicating comorbidities (eg, hyperparathyroidism, chronic kidney disease).

Recognizing osteoporotic vertebral deformity on frontal view radiograph: a cohort analysis and a pictorial review

PURPOSE

Opportunities exist to detect osteoporotic vertebral deformities (VDs) on frontal radiograph (FR) indicated for lung or abdominal diseases, while literature have been mostly based on lateral radiograph (LR). This study analyzed the detectability of moderate and severe grades VD on FR.

METHODS

There were 105 female cases (mean 72 years, range 55~93 year), who were referred for digital spine FR and LR with back and/or leg pain. The LR and FR were read, osteoporotic VDs with < 20%, 20-25%, 25-40%, and > 40% vertebral body height loss were recorded as minimal, mild, moderate, and severe grades, respectively. After a 10-month interval, only FRs were read again, and each vertebra was classified as (1) no notable VD, (2) with notable VD, and (3) ambiguous. The first reading was the reference, while the second reading was allowed to miss minimal/mild VCD and endplate/cortex fracture.

RESULTS

Counting by subjects, for 98 cases, the two reading sessions had agreement, including 43 "true negative" cases and 55 true positive cases. There were two false positive cases, and five ambiguous cases. In total, 1286 vertebra were assessed, FR reading had 1126 vertebrae "true negative," 130 vertebrae true positive, one vertebra false negative, 3 vertebrae false positive, and 26 ambiguous vertebrae (65.4% being true negative and 34.6% being true positive). Most of the disagreements were associated with kyphosis or poor X-ray projection. Nineteen illustrative cases are presented graphically.

CONCLUSION

Moderate and severe grades of VD are identifiable on FR as long as the involved vertebrae are clearly filmed.

Secondary Fracture Prevention: Consensus Clinical Recommendations from a Multistakeholder Coalition

Osteoporosis-related fractures are undertreated, due in part to misinformation about recommended approaches to patient care and discrepancies among treatment guidelines. To help bridge this gap and improve patient outcomes, the American Society for Bone and Mineral Research assembled a multistakeholder coalition to develop clinical recommendations for the optimal prevention of secondary fracture among people aged 65 years and older with a hip or vertebral fracture. The coalition developed 13 recommendations (7 primary and 6 secondary) strongly supported by the empirical literature. The coalition recommends increased communication with patients regarding fracture risk, mortality and morbidity outcomes, and fracture risk reduction. Risk assessment (including fall history) should occur at regular intervals with referral to physical and/or occupational therapy as appropriate. Oral, intravenous, and subcutaneous pharmacotherapies are efficacious and can reduce risk of future fracture. Patients need education, however, about the benefits and risks of both treatment and not receiving treatment. Oral bisphosphonates alendronate and risedronate are first-line options and are generally well tolerated; otherwise, intravenous zoledronic acid and subcutaneous denosumab can be considered. Anabolic agents are expensive but may be beneficial for selected patients at high risk. Optimal duration of pharmacotherapy is unknown but because the risk for second fractures is highest in the early post-fracture period, prompt treatment is recommended. Adequate dietary or supplemental vitamin D and calcium intake should be assured. Individuals being treated for osteoporosis should be reevaluated for fracture risk routinely, including via patient education about osteoporosis and fractures and monitoring for adverse treatment effects. Patients should be strongly encouraged to avoid tobacco, consume alcohol in moderation at most, and engage in regular exercise and fall prevention strategies. Finally, referral to endocrinologists or other osteoporosis specialists may be warranted for individuals who experience repeated fracture or bone loss and those with complicating comorbidities (eg, hyperparathyroidism, chronic kidney disease). © 2019 American Society for Bone and Mineral Research.

Underreporting characteristics of osteoporotic vertebral fracture in back pain clinic patients of a tertiary hospital in China

Aim

Osteoporotic vertebral compressive fractures (VCFs) ​are known to be commonly missed in X-rays indicated for pulmonary or heart diseases. In this study, we investigated the underreporting status of VCF in back pain clinic patients when the spine was the focus of interest.

Materials and methods

This is a retrospective analysis of 105 female cases (mean: 72 years, range: 55–93 years) from a tertiary hospital in China (facility A, FA). The patients with back and/or leg pain were referred for a spine X-ray. The images were retrieved and transferred to a central reading facility (facility B, FB), where images were double-read by two readers experienced in evaluating osteoporotic vertebral compressive deformity (VCD)/VCF. A qualitative VCD with <20%, 20–25%, 25–40%, and >40% vertebral body height loss was recorded as minimal, mild, moderate, and severe grades, respectively. A ​VCD coexisted with endplate/cortex fracture (ECF) was VCF. FB readings were considered as the reference.

Results

There were 34 true negative cases where FA and FB had a consensus. In 7 cases with minimal VCD, 3 cases with ECF, and 7 cases with minimal or mild VCFs, the FA readings were false negative. No standalone singular moderate or severe VCD/VCF in a patient was missed in FA's reports. In 25 FA reading positive cases with multiple VCFs, one VCF was missed in 8 cases, more than one VCF was missed in 15 cases, and one additional ECF was missed in 2 cases. In 14 cases, FA and FB had VCF number agreement, with the term ‘vertebral fracture’ was used appropriately in FA reports. In 15 cases, FA and FB had agreement in VCF number; however, the appropriate term ‘vertebral fracture’ was not used in FA reports; instead the terms of ‘compressive change’ or ‘wedging change’ were used. In most VCFs, severity grading was not given in FA. In 13 VCFs where grading was reported, all were marked as ‘mild’, including seven mild VCFs, five moderate VCFs, and even one severe VCF.

Conclusion

Among the patients with VCD/VCF, the false negative rate among was 23.9% (17/71), but the missed cases were all minimal or mild grades. One or more VCFs were missed in 32.4% (23/71) of the cases with multiple VCFs. Appropriate severity grading was not reported for most cases.

The translational potential of this article

The underreporting rate of osteoporotic vertebral compressive fracture in back pain clinic patients in a typical tertiary hospital setting in China compared favorably with literature reports. However, there is a general lack of awareness of vertebral endplate/cortex fracture sign and vertebral fracture severity grading, while minimal and mild VCD with endplate/cortex fracture may have clinical significance. Moreover, after one VCF is spotted in a patient, it is highly advisable to carefully check the whole spine so that multiple VCFs will not be missed.

Opportunistic screening for osteoporosis using thoraco-abdomino-pelvic CT-scan assessing the vertebral density in rheumatoid arthritis patients

INTRODUCTION

Screening for osteoporosis is crucial in rheumatoid arthritis (RA) patients. The aim of this study was to assess the value of thoraco-abdomino-pelvic CT-derived bone mineral density (BMD) results in L1, compared to dual energy X-ray absorptiometry (DXA) results for osteoporosis screening in rheumatoid arthritis patients.

METHODS

Consecutive RA patients who underwent a CT-scan and DXA within a 2-year period were retrospectively included. The CT sagittal images were then evaluated for vertebral fractures from T4 to L5 using the Genant classification. The CT-attenuation values (in Hounsfield units (HU)) of trabecular bone in L1 were measured on axial images and compared to the DXA results.

RESULTS

This study included 105 patients (mean age 61.1 years (± 9.5), 78.1% women). There were 28 patients (26.7%) with DXA-defined osteoporosis and 32 (30%) with osteoporotic fractures (vertebral and/or non-vertebral). The CT assessment indicated that the mean (SD) vertebral L1 attenuation was 142.2 HU (± 18.5). The diagnostic performance for the vertebral CT-attenuation measurement was acceptable: the AUC was 0.67 for predicting osteoporotic fractures and of 0.69 for predicting vertebral fractures. Among patients with osteoporotic fractures, there were 23 (74%) patients categorized as osteoporotic with a L1 CT-attenuation of 135 HU or less, whereas there were only 13 patients (42%) identified by DXA.

CONCLUSION

CT offers a combined opportunistic screening for osteoporosis by assessing both vertebral fractures and bone density on routine CT-scans. This approach may be particularly interesting for RA patients with a high osteoporosis risk.

Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans

Osteoporotic vertebral fractures (OVFs) are prevalent in older adults and are associated with substantial personal suffering and socio-economic burden. Early diagnosis and treatment of OVFs are critical to prevent further fractures and morbidity. However, OVFs are often under-diagnosed and under-reported in computed tomography (CT) exams as they can be asymptomatic at an early stage. In this paper, we present and evaluate an automatic system that can detect incidental OVFs in chest, abdomen, and pelvis CT examinations at the level of practicing radiologists. Our OVF detection system leverages a deep convolutional neural network (CNN) to extract radiological features from each slice in a CT scan. These extracted features are processed through a feature aggregation module to make the final diagnosis for the full CT scan. In this work, we explored different methods for this feature aggregation, including the use of a long short-term memory (LSTM) network. We trained and evaluated our system on 1432 CT scans, comprised of 10,546 two-dimensional (2D) images in sagittal view. Our system achieved an accuracy of 89.2% and an F1 score of 90.8% based on our evaluation on a held-out test set of 129 CT scans, which were established as reference standards through standard semiquantitative and quantitative methods. The results of our system matched the performance of practicing radiologists on this test set in real-world clinical circumstances. We expect the proposed system will assist and improve OVF diagnosis in clinical settings by pre-screening routine CT examinations and flagging suspicious cases prior to review by radiologists.

Reuse and reduce: abdominal CT, lumbar spine MRI, and a potential 1.2 to 3.4 billion dollars in cost savings

PURPOSE

To determine how much money could potentially be saved by re-evaluating a patient's prior recent abdominal CT for lumbar spine pathology instead of ordering a lumbar spine MRI.

METHODS

Abdominal CT studies, from all consecutive patients who had an abdominal CT within 12 months prior to a lumbar spine MRI obtained between 11/1/15 and 5/30/16, were retrospectively reviewed in a blinded fashion for the presence of any significant lumbar spine abnormalities. CT studies that accurately reflected all normal and abnormal findings when compared to the standard of reference, the prospectively interpreted lumbar spine MR imaging reports, were used to indicate which lumbar spine MRI studies potentially could have been avoided and to calculate the potential cost savings.

RESULTS

Of the 81 abdominal CT studies that met the inclusion criteria of this study, 62% (50/81) were TP, 28% (23/81) were TN, 5% (4/81) were FP, and 5% (4/81) were FN studies. 90% (73/81) of the lumbar spine MRI studies could potentially have been avoided during the 7 months of this study. The predicted savings by reviewing the abdominal CT for lumbar spine abnormalities prior to ordering a lumbar spine MRI are an estimated 1.2-3.4 billion dollars per year.

CONCLUSION

Recent abdominal CT studies should be reviewed for lumbar spine pathology prior to a patient undergoing lumbar spine MRI. Avoiding unnecessary lumbar spine MRI studies could potentially save the U.S. healthcare system an estimated 1.2-3.4 billion dollars per year.

Lumbar spine evaluation: accuracy on abdominal CT

OBJECTIVE

To determine if the lumbar spine can be accurately evaluated on an abdominal CT.

METHODS

The electronic medical records at our institution were searched to find all consecutive patients who had an abdominal CT within 12 months of a lumbar spine MRI obtained between 01 November 2010 and 31 October 2015. The abdominal CT studies were retrospectively reviewed in a blinded fashion for the presence of any significant lumbar spine abnormalities. The prospective lumbar spine MRI reports were used as the standard of reference.

RESULTS

5,031 patients had lumbar spine MRI studies at our institution during the study period of 01 November 2010 to 31 October 2015. 144 patients met the inclusion criteria of our study. No patients were excluded. 107 patients had 256 abnormal findings on the lumbar spine MRI studies. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of abdominal CT in lumbar spine evaluation on a per patient/per finding basis were 89.7/95.3%, 97.3/100%, 99.0/99.2%, 76.6/99.8% and 91.7/99.8%, respectively.

CONCLUSION

Despite several limitations (e.g. spinal cord assessment, bone marrow assessment and quantum mottle) compared with evaluation of the lumbar spine using MRI, evaluation of the lumbar spine on abdominal CT studies can be accurately performed with current state of the art CT scanners. Additional prospective studies are needed for a more definitive analysis. Advances in knowledge: With recent advances in CT technology, accurate evaluation of the lumbar spine on abdominal CT studies is feasible, potentially providing significant additional information to patients without additional imaging.

Fracture Risk Assessment in Clinical Practice: Why Do It? What to Do It With?

Fracture is the outcome of concern in osteoporosis, and fracture reduction is the primary goal of osteoporosis treatment. Fracture risk assessment is a critical component in osteoporosis management. The earlier approach of deciding on whether to treat solely based on bone mineral density (BMD) T-scores has been supplanted by employing the concept of absolute risk over medium time periods and more encompassing integration of clinical risk factors with or without BMD into robust fracture risk assessment tools. Fracture risk estimation allows for identifying high-risk patient groups not only at a health system and population-based level and thereby allowing allocation of financial resources to the people most at risk, but also at an individual level for the clinician to involve the patient in shared decision-making processes for treatment. The process of fracture risk assessment involves several steps including performing a thorough history and physical examination, assessing BMD, doing radiological assessment for vertebral fractures, and laboratory evaluation to rule out secondary contributors to osteoporosis. The data thus obtained can be input into any one of several fracture risk assessment tools that are now available. The decision on which tool to use can be made on the background of country-specific guidelines, although it is imperative that the physician be aware of the limitations inherent to whichever tool is chosen. This article aims to provide a brief overview of why fracture risk estimation is important and the methods that can be employed for it by the physician in clinical practice.

Vertebral body fractures of unknown origin in cancer patients receiving MDCT: reporting by radiologists and awareness by clinicians

Background

To evaluate prevalence, radiological reporting and clinical management of pathologic vertebral body fractures (VBFs) of unknown origin in cancer patients receiving computed tomography (CT) examinations.

Methods

We investigated all CT examinations (over 1 year) of male and female patients with an underlying malignancy and an increased risk of osteoporosis (age 55–79 years) for the presence of VBFs. We evaluated midline sagittal CT-reformations of the spine for prevalence, fracture type, severity and location, the accuracy and style of radiological reporting, subsequent clinical management and documentation in hospital discharge letters.

Results

848 patients were investigated. We found 143 VBFs in 94 (11 %) patients. 6, 49, and 45 % were grade 1, grade 2, and grade 3 fractures, respectively, while 20, 66, and 14 % were wedge, biconcave and crush fractures, respectively. 32 (34 %) radiological reports correctly classified VBFs as fractures, 25 (27 %) reports recognized VBFs, but did not type them, and VBFs were not described in 37 (39 %) reports. In 3 (3 %) patients further clinical work-up of VBFs was performed, while only 8 (9 %) hospital discharge letters contained the information of the presence of pathologic VBFs of unknown origin.

Conclusions

VBFs of unknown origin appear frequently in cancer patients, however, clinical management and documentation was found in only few cases. Moreover, especially in cancer patients consistent radiological reporting of VBFs seems important, as aetiology of VBFs could be from osteoporosis, disease progression or oncological therapy, however, reporting is still performed inconsistently.

Opportunistic Identification of Vertebral Fractures

Vertebral fractures are powerful predictors of future fracture, so, their identification is important to ensure that patients are commenced on appropriate bone protective or bone-enhancing therapy. Risk factors (e.g., low bone mineral density and increasing age) and symptoms (back pain, loss of height) may herald the presence of vertebral fractures, which are usually confirmed by performing spinal radiographs or, increasingly, using vertebral fracture assessment with dual-energy X-ray absorptiometry scanners. However, a large number (30% or more) of vertebral fractures are asymptomatic and do not come to clinical attention. There is, therefore, scope for opportunistic (fortuitous) identification of vertebral fractures from various imaging modalities (radiographs, computed tomography, magnetic resonance imaging, and radionuclide scans) performed for other clinical indications and which include the spine in the field of view, with midline sagittal reformatted images from computed tomography having the greatest potential for such opportunistic detection. Numerous studies confirm this potential for identification but consistently find underreporting of vertebral fractures. So, a valuable opportunity to improve the management of patients at increased risk of future fracture is being squandered. Educational training programs for all clinicians and constant reiteration, stressing the importance of the accurate and clear reporting of vertebral fractures ("you only see what you look for"), can improve the situation, and automated computer-aided diagnostic tools also show promise to solve the problem of this underreporting of vertebral fractures.

Fracture prevention in COPD patients; a clinical 5-step approach

Although osteoporosis and its related fractures are common in patients with COPD, patients at high risk of fracture are poorly identified, and consequently, undertreated. Since there are no fracture prevention guidelines available that focus on COPD patients, we developed a clinical approach to improve the identification and treatment of COPD patients at high risk of fracture. We organised a round-table discussion with 8 clinical experts in the field of COPD and fracture prevention in the Netherlands in December 2013. The clinical experts presented a review of the literature on COPD, osteoporosis and fracture prevention. Based on the Dutch fracture prevention guideline, they developed a 5-step clinical approach for fracture prevention in COPD. Thereby, they took into account both classical risk factors for fracture (low body mass index, older age, personal and family history of fracture, immobility, smoking, alcohol intake, use of glucocorticoids and increased fall risk) and COPD-specific risk factors for fracture (severe airflow obstruction, pulmonary exacerbations and oxygen therapy). Severe COPD (defined as postbronchodilator FEV₁ < 50% predicted) was added as COPD-specific risk factor to the list of classical risk factors for fracture. The 5-step clinical approach starts with case finding using clinical risk factors, followed by risk evaluation (dual energy X-ray absorptiometry and imaging of the spine), differential diagnosis, treatment and follow-up. This systematic clinical approach, which is evidence-based and easy-to-use in daily practice by pulmonologists, should contribute to optimise fracture prevention in COPD patients at high risk of fracture.

Unreported vertebral body compression fractures at abdominal multidetector CT

PURPOSE

To retrospectively assess the prevalence and clinical outcomes of unreported vertebral compression fractures at abdominal computed tomography (CT).

MATERIALS AND METHODS

This HIPAA-compliant study had institutional review board approval; the need for informed consent was waived for this retrospective analysis. A total of 2041 consecutive adult patients (1640 women, 401 men; age range, 19-94 years) underwent both abdominal multidetector CT and dual-energy x-ray absorptiometry (DXA) within 6 months of each other between 2000 and 2007, before sagittal CT reconstructions were obtained routinely. Transverse (axial) and retrospective sagittal multidetector CT reconstructions were reviewed for the presence of moderate or severe vertebral body compression fractures of the lower thoracic and lumbar spine by using the Genant visual semiquantitative method. Twenty-six patients were excluded for evidence of pathologic fracture or for technical factors limiting compression fracture detection. Electronic medical records were reviewed for patients with moderate or severe compression fractures to determine whether the fracture was reported at prospective CT interpretation, was known previously, or was diagnosed subsequently. Correlation was made with central DXA T scores. Statistical analysis was performed with the Student t test and Fisher exact test.

RESULTS

At least one moderate or severe vertebral body compression fracture was identified retrospectively at CT in 97 patients (mean age, 70.8 years). Fractures involved one level in 67 and multiple levels in 30 patients, for a total of 141 fractures. In 81 (84%) patients, prospective CT diagnosis was not made. Patients in whom fractures were reported prospectively were significantly older and were more likely to have a severe compression fracture (P < .05). In 52 (64%) patients with an unreported fracture, the vertebral compression fracture was not known clinically. In 18 patients, subsequent diagnosis of a compression fracture was determined by means of another imaging study (median interval, 7 months). At DXA, 39 (48%) of 81 patients with unreported vertebral body compression fractures had a nonosteoporotic T score (greater than -2.5).

CONCLUSION

Most clinically important vertebral body compression fractures in nontrauma patients at risk for low bone mineral density may go unreported at abdominal multidetector CT if sagittal reconstructions are not routinely evaluated.

Osteoporosis in men: insights for the clinician

Osteoporosis has finally been recognized as an important disorder in men. Men have osteoporotic fractures about 10 years later in life than women. Owing to increasing life expectancy, more fractures are predicted. Important risk factors for men include advancing age, smoking or chronic obstructive pulmonary disease, glucocorticoid therapy, and androgen deprivation therapy for prostate cancer. Other groups at risk for osteoporosis include those with alcohol abuse, men on enzyme-inducing antiseizure drugs, and those with malabsorption or history of surgery for peptic ulcer disease. History and physical examination will likely reveal secondary causes of osteoporosis. Some, but not all organizations, recommend screening for osteoporosis in men older than age 70. In the USA, The Department of Veterans Affairs recommends case finding rather than screening. Evaluation starts with bone mineral density testing by dual energy X-ray absorptiometry of the spine, hip, and in some cases forearm. A few laboratory tests can be helpful, including measurement of 25-hydroxyvitamin D. Most studies of osteoporosis therapy in men are small; but alendronate, risedronate, zoledronic acid, and teriparatide are FDA-approved to increase bone density in men with osteoporosis. A new potent antiresorptive agent, denosumab, increased bone density dramatically in men on androgen deprivation therapy and is approved for this indication in Europe. Recognition, diagnosis, and treatment of osteoporosis in men should lead to fewer fractures and probably fewer deaths.

Why do geriatric outpatients have so many moderate and severe vertebral fractures? Exploring prevalence and risk factors

OBJECTIVES

to determine the prevalence of vertebral fractures and their risk factors in geriatric patients.

DESIGN

prospective cohort study.

SETTING

teaching hospital in Amsterdam, The Netherlands.

SUBJECTS

three hundred and three geriatric patients, who had their first visit at a diagnostic day hospital between April and August 2007.

MEASUREMENTS

lateral X-rays of the lumbar spine and chest were performed; vertebral fractures were scored according to the semi-quantitative method of Genant by trained observers and compared with the official report of radiologists. Co-morbidity, reported falls, mobility and cognitive function were scored.

RESULTS

vertebral fractures were observed in 51% (156/303) of geriatric patients. Sixty-nine per cent (107/156) of these fractures were moderate to severe. In 21% (33/156) of the patients with a fracture, vertebral fractures were diagnosed on the lumbar spine X-ray alone. Patients with vertebral fractures had more previous non-vertebral fractures (odds ratio: 2.40 95% CI: 1.40-4.10), had lower serum albumin levels (OR: 0.92 95% CI: 0.87-0.97) and more current prednisone use (OR: 8.94 95% CI: 1.12-71.45). Co-morbidity and cognitive decline were not identified as risk factors. Radiologists reported vertebral fractures in 53% (82/156) of the cases.

CONCLUSION

this study showed a very high prevalence of vertebral fractures in geriatric patients; particularly the high prevalence of moderate and severe fractures is remarkable. Because of this high prevalence, the routinely performed lateral X-ray of the chest should be used to look for vertebral fractures. An additional X-ray of the lumbar spine might be useful in patients without vertebral fractures on the chest X-ray.

A careful evaluation of scout CT lateral radiograph may prevent unreported vertebral fractures

OBJECTIVES

Our purpose was to review scout CT lateral radiographs to reveal osteoporotic vertebral fractures unreported by radiologists and to explore scout CT as a potential diagnostic tool in the detection of vertebral fractures.

METHODS

We considered 500 patients (303 males, 197 females, age 64.6±13.5 year-old). Our investigation was firstly focused on scout CT lateral images to detect vertebral fractures with a combined semiquantitative and quantitative diagnostic approach. Findings addressed to vertebral fracture were subsequently confirmed by multiplanar sagittal CT reconstructions. Whenever a vertebral fracture was discovered the radiologist report was read and a collection of patient anamnesis followed to understand whether fractures were already known.

RESULTS

In 44/500 patients (8.8%) the evaluation on scout CT was incomplete or limited for patient/technical-based conditions, and 15 were excluded from the analysis. In 67/485 patients (13.8%) 99 vertebral fractures were detected. Among 67 fractured patients only 18 (26.9%) were previously diagnosed by radiologists. However, in the clinical history of 32 patients vertebral fractures were already known.

CONCLUSIONS

The perception and sensibility to vertebral fractures among radiologists are still poor when the assessment of the spine is not the aim of the examination. Short time spent for the evaluation of scout CT lateral radiographs could improve our accuracy.

Missed incidental vertebral compression fractures on computed tomography imaging: More optimism justified

Missed incidental vertebral compression fractures on computed tomography (CT) imaging are a common problem. Although numerous publications are available on this topic, recent publications still show a high percentage of such missed fractures. The rate of such missed fractures in the authors department is much lower than that in the reported literature when routine multiplanar reconstructions are used for reporting CT scans. Therefore, a more optimistic view on this topic seems to be justified.