Performance of ChatGPT on the Brazilian Radiology and Diagnostic Imaging and Mammography Board Examinations.

This prospective exploratory study conducted from January 2023 through May 2023 evaluated the ability of ChatGPT to answer questions from Brazilian radiology board examinations, exploring how different prompt strategies can influence performance using GPT-3.5 and GPT-4. Three multiple-choice board examinations that did not include image-based questions were evaluated: (a) radiology and diagnostic imaging, (b) mammography, and (c) neuroradiology. Five different styles of zero-shot prompting were tested: (a) raw question, (b) brief instruction, (c) long instruction, (d) chain-of-thought, and (e) question-specific automatic prompt generation (QAPG). The QAPG and brief instruction prompt strategies performed best for all examinations (P < .05), obtaining passing scores (≥60%) on the radiology and diagnostic imaging examination when testing both versions of ChatGPT. The QAPG style achieved a score of 60% for the mammography examination using GPT-3.5 and 76% using GPT-4. GPT-4 achieved a score up to 65% in the neuroradiology examination. The long instruction style consistently underperformed, implying that excessive detail might harm performance. GPT-4's scores were less sensitive to prompt style changes. The QAPG prompt style showed a high volume of the "A" option but no statistical difference, suggesting bias was found. GPT-4 passed all three radiology board examinations, and GPT-3.5 passed two of three examinations when using an optimal prompt style. Keywords: ChatGPT, Artificial Intelligence, Board Examinations, Radiology and Diagnostic Imaging, Mammography, Neuroradiology © RSNA, 2023 See also the commentary by Trivedi and Gichoya in this issue.

Can three-dimensional models enhance understanding and knowledge of rotator cuff tears?

OBJECTIVES: Magnetic resonance imaging (MRI) is currently the standard diagnostic tool for rotator cuff tears. However, its two-dimensional (2D) output, displayed on a monitor, can complicate the interpretation of anatomy. Three-dimensional (3D) imaging may offer a solution to this issue. This study aimed to demonstrate the diagnostic and interpretive value of a 3D model in assessing lesion anatomy. The hypothesis was that 3D models, compared to 2D MRI, can enhance the comprehension and knowledge of rotator cuff injuries, improve the application of classifications for total tears, and provide a more precise definition of the size and type of tear. METHODS: A prospective single-centre study was conducted. 3D models for rotator cuff tears were created and analysed in conjunction with preoperative MRI for each patient up to 2 months before surgery. The 3D models were based on the preoperative MRI. Collected data included 2D plane measurements by MRI in coronal and sagittal planes, descriptions of 3D lesion geometry (new shapes), 3D measurements in coronal and sagittal planes, arthroscopic classifications of rotator cuff injuries, and arthroscopic measurements in coronal and sagittal planes. RESULTS: After examining 25 cases, 3D imaging demonstrated similar arthroscopic values post-bursectomy in the sagittal plane (16.70 â€‹mm for 3D and 18.28 â€‹mm for post-bursectomy, p-value â€‹= â€‹0.189), although these measurements did not align with those of MRI (which underestimated measurements, p-value â€‹= â€‹0.010). Both MRI measurement and 3D imaging showed similar measurement accuracy in the coronal plane when compared to arthroscopic measurements taken before and after bursectomy. The creation of 3D objects enabled the analysis of new geometries, including the length, width, and depth of each lesion. These geometries included the rectangle, rectangular trapezoid, scalene trapezoid, irregular pentagon, and irregular hexagon. CONCLUSIONS: 3D models can enhance the understanding and knowledge of rotator cuff injuries. They can be a promising tool for diagnosing and interpreting the anatomy of the injury, particularly in the sagittal plane. The new 3D understanding of the pathological process has led to the description of new geometric features not visible in conventional 2D MRI. LEVEL OF EVIDENCE: II - Development of diagnostic criteria on consecutive patients (all compared to "gold" standard).

Decreasing false-positive detection of intracranial hemorrhage (ICH) using RAPID ICH 3.

INTRODUCTION: The prompt detection of intracranial hemorrhage (ICH) on a non-contrast head CT (NCCT) is critical for the appropriate triage of patients, particularly in high volume/high acuity settings. Several automated ICH detection tools have been introduced; however, at present, most suffer from suboptimal specificity leading to false-positive notifications. METHODS: NCCT scans from 4 large databases were evaluated for the presence of an ICH (IPH, IVH, SAH or SDH) of >0.4 ml using fully-automated RAPID ICH 3.0 as compared to consensus detection from at least two neuroradiology experts. Scans were excluded for (1) severe CT artifacts, (2) prior neurosurgical procedures, or (3) recent intravenous contrast. ICH detection accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios by were determined. RESULTS: A total of 881 studies were included. The automated software correctly identified 453/463 ICH-positive cases and 416/418 ICH-negative cases, resulting in a sensitivity of 97.84% and specificity 99.52%, positive predictive value 99.56%, and negative predictive value 97.65% for ICH detection. The positive and negative likelihood ratios for ICH detection were similarly favorable at 204.49 and 0.02 respectively. Mean processing time was <40 seconds. CONCLUSIONS: In this large data set of nearly 900 patients, the automated software demonstrated high sensitivity and specificity for ICH detection, with rare false-positives.

Validation of a deep learning algorithm for bone age estimation among patients in the city of São Paulo, Brazil / Validação de algoritmo de aprendizado profundo para detecção da idade óssea em pacientes de São Paulo, Brasil

Abstract Objective: To validate a deep learning (DL) model for bone age estimation in individuals in the city of São Paulo, comparing it with the Greulich and Pyle method. Materials and Methods: This was a cross-sectional study of hand and wrist radiographs obtained for the determination of bone age. The manual analysis was performed by an experienced radiologist. The model used was based on a convolutional neural network that placed third in the 2017 Radiological Society of North America challenge. The mean absolute error (MAE) and the root-mean-square error (RMSE) were calculated for the model versus the radiologist, with comparisons by sex, race, and age. Results: The sample comprised 714 examinations. There was a correlation between the two methods, with a coefficient of determination of 0.94. The MAE of the predictions was 7.68 months, and the RMSE was 10.27 months. There were no statistically significant differences between sexes or among races (p > 0.05). The algorithm overestimated bone age in younger individuals (p = 0.001). Conclusion: Our DL algorithm demonstrated potential for estimating bone age in individuals in the city of São Paulo, regardless of sex and race. However, improvements are needed, particularly in relation to its use in younger patients.

Resumo Objetivo: Validar em indivíduos paulistas um modelo de aprendizado profundo (deep learning - DL) para estimativa da idade óssea, comparando-o com o método de Greulich e Pyle. Materiais e Métodos: Estudo transversal com radiografias de mão e punho para idade óssea. A análise manual foi feita por um radiologista experiente. Foi usado um modelo baseado em uma rede neural convolucional que ficou em terceiro lugar no desafio de 2017 da Radiological Society of North America. Calcularam-se o erro médio absoluto (mean absolute error - MAE) e a raiz do erro médio quadrado (root mean-square error - RMSE) do modelo contra o radiologista, com comparações entre sexo, etnia e idade. Resultados: A amostra compreendia 714 exames. Houve correlação entre ambos os métodos com coeficiente de determinação de 0,94. O MAE das predições foi 7,68 meses e a RMSE foi 10,27 meses. Não houve diferenças estatisticamente significantes entre sexos ou raças (p > 0,05). O algoritmo superestimou a idade óssea nos mais jovens (p = 0,001). Conclusão: O nosso algoritmo de DL demonstrou potencial para estimar a idade óssea em indivíduos paulistas, independentemente do sexo e da raça. Entretanto, há necessidade de aprimoramentos, particularmente em pacientes mais jovens.

Validation of a deep learning algorithm for bone age estimation among patients in the city of São Paulo, Brazil.

Objective: To validate a deep learning (DL) model for bone age estimation in individuals in the city of São Paulo, comparing it with the Greulich and Pyle method. Materials and Methods: This was a cross-sectional study of hand and wrist radiographs obtained for the determination of bone age. The manual analysis was performed by an experienced radiologist. The model used was based on a convolutional neural network that placed third in the 2017 Radiological Society of North America challenge. The mean absolute error (MAE) and the root-mean-square error (RMSE) were calculated for the model versus the radiologist, with comparisons by sex, race, and age. Results: The sample comprised 714 examinations. There was a correlation between the two methods, with a coefficient of determination of 0.94. The MAE of the predictions was 7.68 months, and the RMSE was 10.27 months. There were no statistically significant differences between sexes or among races (p > 0.05). The algorithm overestimated bone age in younger individuals (p = 0.001). Conclusion: Our DL algorithm demonstrated potential for estimating bone age in individuals in the city of São Paulo, regardless of sex and race. However, improvements are needed, particularly in relation to its use in younger patients.

Objetivo: Validar em indivíduos paulistas um modelo de aprendizado profundo (deep learning - DL) para estimativa da idade óssea, comparando-o com o método de Greulich e Pyle. Materiais e Métodos: Estudo transversal com radiografias de mão e punho para idade óssea. A análise manual foi feita por um radiologista experiente. Foi usado um modelo baseado em uma rede neural convolucional que ficou em terceiro lugar no desafio de 2017 da Radiological Society of North America. Calcularam-se o erro médio absoluto (mean absolute error - MAE) e a raiz do erro médio quadrado (root mean-square error - RMSE) do modelo contra o radiologista, com comparações entre sexo, etnia e idade. Resultados: A amostra compreendia 714 exames. Houve correlação entre ambos os métodos com coeficiente de determinação de 0,94. O MAE das predições foi 7,68 meses e a RMSE foi 10,27 meses. Não houve diferenças estatisticamente significantes entre sexos ou raças (p > 0,05). O algoritmo superestimou a idade óssea nos mais jovens (p = 0,001). Conclusão: O nosso algoritmo de DL demonstrou potencial para estimar a idade óssea em indivíduos paulistas, independentemente do sexo e da raça. Entretanto, há necessidade de aprimoramentos, particularmente em pacientes mais jovens.

Clinical Artificial Intelligence Applications in Radiology: Neuro.

Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown. This article illustrates some of these applications. This article reviews machine learning challenges related to neuroradiology. The first approval of reimbursement for an AI algorithm by the Centers for Medicare and Medicaid Services, covering a stroke software for early detection of large vessel occlusion, is also discussed.

Arthroscopic Superior Capsule Reconstruction Using Three-Dimensional Preoperative Planning: Technique Description.

We describe a technique using a fascia lata autograft with 3-dimensional (3D) printing to reconstruct the rotator cuff. Prototyping constitutes the construction of physical prototypes with high complexity after virtual studies. Such models increase the knowledge of the characteristics and size of rotator cuff injuries, thus improving the accuracy of determining the correct size of the graft to be used in superior capsule reconstruction. We present a case of superior capsule reconstruction using 3D printing for enhancing the accuracy of fascia lata allograft size and tension determination; 3D reconstruction has never been described in the literature for rotator cuff injuries.

Intra-session test-retest reliability of functional connectivity in infants.

Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks - the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14-0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40-0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study sites. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientists may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success.

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID-) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.

A Practical Approach to Diagnosis of Spinal Dysraphism.

Spinal dysraphisms (SDs) are congenital malformations of the spinal cord, determined by derangement in the complex cascade of embryologic events involved in spinal development. They represent a heterogeneous group ranging from mild clinical manifestations-going unnoticed or being discovered at clinical examination-to a causal factor of life quality impairment, especially when associated with musculoskeletal, gastrointestinal, genitourinary, or respiratory system malformations. Knowledge of the normal embryologic development of the spinal cord-which encompasses three main steps (gastrulation, primary neurulation, and secondary neurulation)-is crucial for understanding the pathogenesis, neuroradiologic scenarios, and clinical-radiologic classification of congenital malformations of the spinal cord. SDs can be divided with clinical examination or neuroradiologic study into two major groups: open SDs and closed SDs. Congenital malformations of the spinal cord include a wide range of abnormalities that vary considerably in imaging and clinical characteristics and complexity and therefore may represent a diagnostic challenge, even for the experienced radiologist. Online supplemental material is available for this article. ©RSNA, 2021.

The RSNA Pediatric Bone Age Machine Learning Challenge.

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue.

Ischemic stroke enhancement using a variational model and the expectation maximization method.

OBJECTIVES: In order to enable less experienced physicians to reliably detect early signs of stroke, A novel approach was proposed to enhance the visual perception of ischemic stroke in non-enhanced CT. METHODS: A set of 39 retrospective CT scans were used, divided into 23 cases of acute ischemic stroke and 16 normal patients. Stroke cases were obtained within 4.5 h of symptom onset and with a mean NIHSS of 12.9±7.4. After selection of adjunct slices from the CT exam, image averaging was performed to reduce the noise and redundant information. This was followed by a variational decomposition model to keep the relevant component of the image. The expectation maximization method was applied to generate enhanced images. RESULTS: We determined a test to evaluate the performance of observers in a clinical environment with and without the aid of enhanced images. The overall sensitivity of the observer's analysis was 64.5 % and increased to 89.6 % and specificity was 83.3 % and increased to 91.7 %. CONCLUSION: These results show the importance of a computational tool to assist neuroradiology decisions, especially in critical situations such as the diagnosis of ischemic stroke. KEY POINTS: • Diagnosing patients with stroke requires high efficiency to avoid irreversible cerebral damage. • A computational algorithm was proposed to enhance the visual perception of stroke. • Observers' performance was increased with the aid of enhanced images.

Safety of Onyx Transarterial Embolization of Skull Base Dural Arteriovenous Fistulas from Meningeal Branches of the External Carotids also Fed by Meningeal Branches of Internal Carotid or Vertebral Arteries.

PURPOSE: To analyze the angiographic and clinical results of transarterial embolization with Onyx (Medtronic-Covidien, Irvine, CA) in dural arteriovenous fistulas (DAVFs) partially fed by arteries arising from the carotid siphon or the vertebral arteries. METHODS: We isolated 40 DAVFs supplied by either the tentorial artery of the internal carotid artery (ICA) or the posterior meningeal artery of the vertebral artery. These DAVFs were embolized with Onyx through the middle meningeal artery or the occipital artery. We reviewed the occurrence of reflux into the arteries of carotid or vertebral origin. RESULTS: In all the cases, reflux occurred into the first millimeters of the DAVF arterial feeders arising from carotid or vertebral arteries but slowly enough to be controlled by interruption of Onyx injection. Reflux was always minimal and Onyx never reached the ostium of the arteries. No cerebral ischemic complications occurred in our series. CONCLUSION: The behavior of Onyx is clearly different from that of cyanoacrylate glue, resulting in superior control during injection. Reflux into arteries arising from the ICA or vertebral artery during DAVF treatment always carries a risk of unintentional non-target embolization of normal cerebral vasculature but Onyx appears to be safe in this situation.

Learning curve of radiology residents during training in fluoroscopy-guided facet joint injections.

OBJECTIVE: To develop a simulator for training in fluoroscopy-guided facet joint injections and to evaluate the learning curve for this procedure among radiology residents. MATERIALS AND METHODS: Using a human lumbar spine as a model, we manufactured five lumbar vertebrae made of methacrylate and plaster. These vertebrae were assembled in order to create an anatomical model of the lumbar spine. We used a silicon casing to simulate the paravertebral muscles. The model was placed into the trunk of a plastic mannequin. From a group of radiology residents, we recruited 12 volunteers. During simulation-based training sessions, each student carried out 16 lumbar facet injections. We used three parameters to assess the learning curves: procedure time; fluoroscopy time; and quality of the procedure, as defined by the positioning of the needle. RESULTS: During the training, the learning curves of all the students showed improvement in terms of the procedure and fluoroscopy times. The quality of the procedure parameter also showed improvement, as evidenced by a decrease in the number of inappropriate injections. CONCLUSION: We present a simple, inexpensive simulation model for training in facet joint injections. The learning curves of our trainees using the simulator showed improvement in all of the parameters assessed.

OBJETIVO: Desenvolver um simulador para treinamento em punção de articulações facetárias guiada por fluoroscopia e avaliar a curva de aprendizado neste procedimento em um grupo de residentes de radiologia. MATERIAIS E MÉTODOS: Tomando uma coluna lombar humana como modelo, desenvolvemos cinco vértebras lombares feitas de metacrilato e gesso. Essas vértebras foram combinadas para formar um modelo anatômico de coluna lombar. Utilizamos um invólucro de silicone para simular a musculatura paravertebral. O modelo foi colocado dentro do tronco de um manequim de plástico. Recrutamos 12 voluntários dentre residentes de radiologia de nosso departamento. Cada aluno realizou 16 punções de articulações facetárias em nosso simulador em uma única sessão de treinamento. Usamos três parâmetros para avaliar as curvas de aprendizado: tempo de procedimento, tempo de fluoroscopia e qualidade do procedimento, definida pelo posicionamento da agulha. RESULTADOS: As curvas de aprendizado de todos os estudantes mostraram melhora nos tempos de procedimento e fluoroscopia com o treinamento. O parâmetro de qualidade do procedimento também mostrou melhora, definida por decréscimo no número de punções inadequadas. CONCLUSÃO: Apresentamos um modelo simulador simples e de baixo custo para treinamento em punção de articulações facetárias. As curvas de aprendizado de nossos estudantes mostraram melhora em todos os parâmetros avaliados.

Learning curve of radiology residents during training in fluoroscopy-guided facet joint injections / Curva de aprendizado de residentes em radiologia durante treinamento em punção de articulações facetárias guiada por fluoroscopia

Abstract Objective: To develop a simulator for training in fluoroscopy-guided facet joint injections and to evaluate the learning curve for this procedure among radiology residents. Materials and Methods: Using a human lumbar spine as a model, we manufactured five lumbar vertebrae made of methacrylate and plaster. These vertebrae were assembled in order to create an anatomical model of the lumbar spine. We used a silicon casing to simulate the paravertebral muscles. The model was placed into the trunk of a plastic mannequin. From a group of radiology residents, we recruited 12 volunteers. During simulation-based training sessions, each student carried out 16 lumbar facet injections. We used three parameters to assess the learning curves: procedure time; fluoroscopy time; and quality of the procedure, as defined by the positioning of the needle. Results: During the training, the learning curves of all the students showed improvement in terms of the procedure and fluoroscopy times. The quality of the procedure parameter also showed improvement, as evidenced by a decrease in the number of inappropriate injections. Conclusion: We present a simple, inexpensive simulation model for training in facet joint injections. The learning curves of our trainees using the simulator showed improvement in all of the parameters assessed.

Resumo Objetivo: Desenvolver um simulador para treinamento em punção de articulações facetárias guiada por fluoroscopia e avaliar a curva de aprendizado neste procedimento em um grupo de residentes de radiologia. Materiais e Métodos: Tomando uma coluna lombar humana como modelo, desenvolvemos cinco vértebras lombares feitas de metacrilato e gesso. Essas vértebras foram combinadas para formar um modelo anatômico de coluna lombar. Utilizamos um invólucro de silicone para simular a musculatura paravertebral. O modelo foi colocado dentro do tronco de um manequim de plástico. Recrutamos 12 voluntários dentre residentes de radiologia de nosso departamento. Cada aluno realizou 16 punções de articulações facetárias em nosso simulador em uma única sessão de treinamento. Usamos três parâmetros para avaliar as curvas de aprendizado: tempo de procedimento, tempo de fluoroscopia e qualidade do procedimento, definida pelo posicionamento da agulha. Resultados: As curvas de aprendizado de todos os estudantes mostraram melhora nos tempos de procedimento e fluoroscopia com o treinamento. O parâmetro de qualidade do procedimento também mostrou melhora, definida por decréscimo no número de punções inadequadas. Conclusão: Apresentamos um modelo simulador simples e de baixo custo para treinamento em punção de articulações facetárias. As curvas de aprendizado de nossos estudantes mostraram melhora em todos os parâmetros avaliados.

Distance between Great Vessels and the Lumbar Spine: MRI Study for Anterior Longitudinal Ligament Release Through a Lateral Approach.

Objective Anterior column reconstruction using the lateral transpsoas approach requires sectioning of the anterior longitudinal ligament while protecting the great vessels. Our aim was to study the anatomical plane of separation between the retroperitoneal vessels and the anterior aspect of the lumbar spine as they relate to safety in the lateral transpsoas anterior column reconstruction procedure. Method A total of 100 T2-weighted magnetic resonance imaging (MRI) examinations were studied. Measurements were obtained for each vertebral body and for each intervertebral disk levels from L1-L2 to L4-L5, and for these vessels: abdominal aorta, inferior vena cava , and common iliac vessels. The following parameters were obtained: (sagittal) total lumbar lordosis and segmental lordosis; (axial) closest distance (areolar space [AS]) between the lumbar spine and vessels; and position of the great vessels. Results The AS was differently distributed for the abdominal aorta and the inferior vena cava. Average values for the inferior vena cava were larger at upper levels (p < 0.001; range: 0.2-9.2 mm), and there were differences between the arteries among the levels (p < 0.001; range: 1.0-4.3 mm) but with no clear difference between the upper and lower lumbar spine. A narrower AS was found at the intervertebral disk level compared with the adjacent vertebral body. At L4-L5, the veins usually lay over the anterior border of the lumbar spine, with substantially wider AS at other lumbar levels. Conclusion The plane between the great vessels and the lumbar spine is differently distributed along the lumbar spine and is especially narrow at lower lumbar levels and in front of the intervertebral disk. The results shown here may help guide surgical decision making for the lateral anterior column reconstruction and may aggregate data from dislocation of the vessels in the lateral decubitus and individualized analysis.

A new index for the assessment of transverse sinus stenosis for diagnosing idiopathic intracranial hypertension.

BACKGROUND AND PURPOSE: To assess the role of MR venography (MRV) for detecting transverse sinus stenosis, to determine the importance of this finding in idiopathic intracranial hypertension (IIH), and to propose an index that contributes to this diagnosis. MATERIALS AND METHODS: We retrospectively assessed consecutive intracranial MRV of patients aged >18 years diagnosed with IIH according to the diagnostic criteria, between January 2010 and July 2012. The assessments were randomly analyzed by three radiologists. Stenoses in the right and left transverse sinuses were independently classified according to the following scale: 0, normal; 1, stenosis <33%; 2, stenosis 33-66%; 3, stenosis >66%; and 4, hypoplasia or agenesis. We established an index based on multiplication of the stenosis scale values for each transverse sinus. A point and range estimate of the sensitivity, specificity, and the area under the receiver operating characteristic curve was performed to obtain cut-off points to differentiate between controls and patients. RESULTS: 63 individuals were included in this study: 32 (50.8%) diagnosed with IIH (31 (96.9%) women and 1 (3.1%) man) and 31 (49.2%) controls. According to all of the examiners, the IIH group showed a higher degree of stenosis than the control group. Index values ≥4 for a diagnosis of IIH had a sensitivity and specificity of 94.7% and 93.5%, respectively. CONCLUSIONS: MRV should be used to assess patients with suspected IIH, and bilateral transverse sinus stenosis should be considered for the diagnosis. The stenosis classifying index proposed here is a fast and accessible method for diagnosing IIH.

In which patients is it possible to perform standalone lateral lumbar interbody fusion without cage subsidence? / Em quais pacientes é possível realizar fusão intersomática lombar por via lateral sem subsidência do cage? / Cuáles son los pacientes que pueden ser sometidos a fusión intersomática lumbar por vía lateral sin subsidencia del cage?

ABSTRACT Objective: To identify the factors related to the non-occurrence of cage subsidence in standalone lateral lumbar interbody fusion procedures. Methods: Case-control study of single level standalone lateral lumbar interbody fusion (LLIF) including 86 cases. Patients without cage subsidence composed the control group (C), while those in the subsidence group (S) developed cage subsidence. Preoperative data were examined to create a risk score based on correlation factors with S group. The proven risk factors were part of an evaluation score. Results: Of the 86 cases included, 72 were in group C and 14 in group S. The following risk factors were more prevalent in group S compared to C group: spondylolisthesis (93% vs 18%; p<0.001); scoliosis (31% vs 12%; p=0.033); women (79% vs 38%; p=0.007); older patients (average 57.0 vs 68.4 years; p=0.001). These risk factors were used in a score (0-4) to evaluate the risk in each case. The patients with higher risk scores had greater subsidence (p<0.001). Scores ≥2 were predictive of subsidence with 92% sensitivity and 72% specificity. Conclusions: It was possible to correlate the degree of subsidence in standalone LLIF procedures using demographic (age and gender) and pathological (spondylolisthesis and scoliosis) data. With a score based on risk factors and considering any score <2, the probability of non-occurrence of subsidence following standalone LLIF (negative predictive value) was 98%.

RESUMO Objetivo: Identificar os fatores relacionados a não ocorrência de subsidência de cage em procedimentos de fusão lombar intersomática por via lateral em um só nível. Métodos: Estudo de caso controle em fusão intersomática lombar por via lateral (LLIF) em um só nível, incluindo 86 casos. Os pacientes sem subsidência do cage formaram o grupo controle (C), enquanto os do grupo subsidência (S) desenvolveram subsidência do cage. Os dados pré-operatórios foram examinados para criar um escore de risco com base em fatores de correlação com o grupo S. Os fatores de risco comprovados fizeram parte de um escore de avaliação. Resultados: Dos 86 casos incluídos, 72 estavam no grupo C e 14 no grupo S. Os seguintes fatores de risco foram mais prevalentes no grupo S com relação ao grupo C: espondilolistese (93% vs. 18%; p < 0,001); escoliose (31% vs. 12%; p = 0,033); mulheres (79% vs. 38%; p = 0,007); pacientes idosos (média de 57,0 vs. 68,4 anos; p = 0,001). Esses fatores de risco foram utilizados em um escore (0-4) para avaliar o risco em cada caso. Os pacientes com escores mais altos de risco tiveram maior subsidência (p < 0,001). Os escores ≥ 2 foram preditivos de subsidência com sensibilidade de 92% e especificidade de 72%. Conclusões: Foi possível correlacionar o grau de subsidência em procedimentos LLIF em um só nível com a utilização de dados demográficos (idade e sexo) e patológicos (espondilolistese e escoliose). Com um escore baseado em fatores de risco e considerando qualquer pontuação <2, a probabilidade de não ocorrência de subsidência depois de LLIF em um só nível (valor preditivo negativo) foi de 98%.

RESUMEN Objetivo: Identificar los factores relacionados con la ausencia de subsidencia de cage en los procedimientos de fusión intersomática lumbar por vía lateral en un solo nivel. Métodos: Estudio de caso-control en la fusión intersomática lumbar por vía lateral (LLIF) en un solo nivel, incluyendo 86 casos. Los pacientes sin subsidencia del cage formaron el grupo control (C), mientras que el grupo de subsidencia (S) desarrolló subsidencia del cage. Los datos preoperatorios fueron examinados para crear una puntuación de riesgo basada en factores de correlación con el grupo S. Los factores de riesgo comprobados formaron parte de una puntuación de evaluación. Resultados: De los 86 casos incluidos, 72 formaron el grupo C y 14 el grupo S. Los siguientes factores de riesgo son más prevalentes en el grupo S con respecto al grupo C: espondilolistesis (93% vs. 18%, p <0,001); escoliosis (31% vs. 12%, p = 0,033); mujeres (79% vs. 38%, p = 0,007); ancianos (media de 57,0 a 68,4 años; p = 0,001). Estos factores de riesgo se utilizaron en una puntuación (0-4) para evaluar el riesgo en cada caso. Los pacientes con puntuaciones más altas de riesgo tuvieron mayor subsidencia (p < 0,001). Las puntuaciones ≥ 2 fueron predictivas de la subsidencia con una sensibilidad del 92% y una especificidad del 72%. Conclusiones: Se ha podido relacionar el grado de subsidencia en los procedimientos LLIF en un solo nivel con el uso de los datos demográficos (edad y sexo) y patológicos (espondilolistesis y escoliosis). Con una puntuación basada en factores de riesgo y considerado un puntaje < 2, la probabilidad de no ocurrencia de subsidencia después de LLIF en un solo nivel (valor predictivo negativo) fue del 98%.