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1.
Med Sci Monit ; 26: e925669, 2020 May 29.
Artigo em Inglês | MEDLINE | ID: mdl-32468998

RESUMO

BACKGROUND A growing body of evidence suggests that in the face of life adversity, threats, or other major stressful events, resilience is more conducive to individual adaptation and growth. MATERIAL AND METHODS The Connor-Davidson Resilience Scale and the Chinese Perceived Stress Scale were used to evaluate the resilience and perceived stress of 600 medical staff members from the radiology departments in 32 public hospitals in Sichuan Province, China, respectively. Multiple linear regression was used to analyze factors related to resilience. RESULTS The total resilience score was 65.76±17.26, wherein the toughness dimension score was 33.61±9.52, the strength dimension score was 21.25±5.50, and the optimism dimension score was 10.91±3.15. There was a significant negative correlation between perceived stress and resilience (r=-0.635, P<0.001). According to multivariate analysis, the total perceived stress score (ß=-1.318, P<0.001), gender (ß=-4.738, P<0.001), knowledge of COVID-19 (ß=2.884, P=0.043), knowledge of COVID-19 protective measures (ß=3.260, P=0.042), and availability of adequate protective materials (ß=-1.268, P=0.039) were independent influencing factors for resilience. CONCLUSIONS The resilience level of the medical staff in the radiology departments during the outbreak of COVID-19 was generally low, particularly regarding toughness. More attention should be paid to resilience influence factors such as high perceived stress, female gender, lack of understanding of COVID-19 and protective measures, and lack of protective materials, and targeted interventions should be undertaken to improve the resilience level of the medical staff in the radiology departments during the outbreak of COVID-19.


Assuntos
Atitude do Pessoal de Saúde , Infecções por Coronavirus/psicologia , Corpo Clínico Hospitalar/psicologia , Pneumonia Viral/psicologia , Serviço Hospitalar de Radiologia , Resiliência Psicológica , Adaptação Psicológica , Adulto , China , Infecções por Coronavirus/prevenção & controle , Estudos Transversais , Análise Fatorial , Feminino , Conhecimentos, Atitudes e Prática em Saúde , Hospitais Públicos , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias/prevenção & controle , Equipamento de Proteção Individual , Pneumonia Viral/prevenção & controle , Enfermagem Radiológica e de Imagem , Radiologistas/psicologia , Amostragem , Estresse Psicológico/etiologia , Inquéritos e Questionários , Tecnologia Radiológica
2.
Coluna/Columna ; 19(1): 67-70, Jan.-Mar. 2020. graf
Artigo em Inglês | LILACS | ID: biblio-1089642

RESUMO

ABSTRACT This study presents details about the applicability of the new image acquisition system, called the biplanar imaging system, with three-dimensional capabilities (EOS®) to the treatment of spinal deformities. This system allows radiographic acquisition of the entire body, with a great reduction in the dose of radiation absorbed by the patient and three-dimensional (3D) stereoradiographic image reconstruction of bone structures, including the spine. In the case of adolescent idiopathic scoliosis, the analysis of the spinal deformity with 3D reconstruction allows better understanding of the deformity and surgical planning. In the case of adult spinal deformity, full-body analysis allows an evaluation of the spinopelvic deformity, including loss of sagittal alignment, in addition to an evaluation of compensatory mechanisms recruited by the individual in an attempt to maintain the sagittal balance. Level of evidence III; Descriptive Review.


RESUMO O presente estudo apresenta detalhes sobre a aplicabilidade do novo sistema de aquisição de imagem, denominado sistema de imagem biplanar, com capacidade tridimensional (EOS®) no tratamento de deformidades da coluna vertebral. Tal sistema permite a aquisição radiográfica do corpo inteiro, com grande redução da dose de radiação absorvida pelo paciente e reconstrução estereoradiográfica em imagem tridimensional (3D) das estruturas ósseas, incluindo a coluna vertebral. No caso de escoliose idiopática do adolescente, a análise da deformidade da coluna vertebral com reconstrução 3D permite a melhor compreensão da deformidade e planejamento cirúrgico. No caso da deformidade da coluna vertebral do adulto, a análise do corpo inteiro permite a avaliação da deformidade espinopélvica, incluindo a perda do alinhamento sagital, além da avaliação adicional dos mecanismos compensatórios recrutados pelo indivíduo na tentativa de manter o equilíbrio sagital. Nível de evidência III; Revisão Descritiva.


RESUMEN El presente estudio presenta detalles sobre la aplicabilidad del nuevo sistema de adquisición de imagen denominado sistema de imagen biplanar, con capacidad tridimensional (EOS®) en el tratamiento de deformidades de la columna vertebral. Tal sistema permite la adquisición radiográfica del cuerpo entero, con gran reducción de la dosis de radiación absorbida por el paciente y reconstrucción estereorradiográfica en imagen tridimensional (3D) de las estructuras óseas, incluyendo la columna vertebral. En el caso de escoliosis idiopática del adolescente, el análisis de la deformidad de la columna vertebral con reconstrucción 3D permite la mejor comprensión de la deformidad y planificación quirúrgica. En el caso de la deformidad de la columna vertebral del adulto, el análisis del cuerpo entero permite la evaluación de la deformidad espinopélvica, incluyendo la pérdida de la alineación sagital, además de la evaluación adicional de los mecanismos compensatorios reclutados por el individuo en el intento de mantener el equilibrio sagital. Nivel de evidencia III; Revisión Descriptiva.


Assuntos
Humanos , Escoliose , Coluna Vertebral , Radiografia , Tecnologia Radiológica , Mau Alinhamento Ósseo
3.
Br J Radiol ; 93(1108): 20190948, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32101448

RESUMO

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.


Assuntos
Aprendizado Profundo/tendências , Diagnóstico por Imagem/tendências , Processamento de Imagem Assistida por Computador/tendências , Tecnologia Radiológica/tendências , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Diagnóstico por Imagem/métodos , Feminino , Previsões , Humanos , Processamento de Imagem Assistida por Computador/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Masculino , Radiografia/métodos , Tecnologia Radiológica/métodos , Fluxo de Trabalho
4.
Radiol Med ; 125(5): 451-460, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-32048157

RESUMO

PURPOSE: To evaluate the effect of dose reduction with iterative reconstruction (IR) on image quality of chest CT scan comparing two protocols. MATERIALS AND METHODS: Fifty-nine patients were enrolled. The two CT protocols were applied using Iterative Reconstruction (ASIR™) 40% but different noise indexes, recording dose-length product (DLP) and volume computed tomography dose index (CTDIvol). The subjective IQ was rated based on the distinction of anatomic details using a 4-point Likert scale based on the European Guidelines on Quality Criteria for CT. For each patient, two single CTs, at enrollment (group 1) and at follow-up after lowering the dose (group 2), were evaluated by two radiologists evaluating, for each examination, five different lung regions (central zone-CZ; peripheral zone-PZ; sub-pleural region-SPR; centrilobular region-CLR; and apical zone-AZ). An inter-observer agreement was expressed by weighted Cohen's kappa statistics (k) and intra-individual differences of subjective image analysis through visual grading characteristic (VGC) analysis. RESULTS: An average 50.4% reduction in CTDIvol and 51.5% reduction in DLP delivered were observed using the dose-reduced protocol. An agreement between observers evaluating group 1 CTs was perfect (100%) and moderate to good in group 2 examinations (k-Cohen ranging from 0.56 for PZ and AZ to 0.70 for SPR). In the VGC analysis, image quality ratings were significantly better for group 1 than group 2 scans for all regions (AUCVGC ranging from 0.56 for CZ to 0.62). However, disagreement was limited to a score 4 (excellent)-to-score 3 (good) IQ transition; apart from a single case in PZ, both the observers scored the IQ at follow-up as 2 (sufficient) starting from a score 4 (excellent). CONCLUSION: Dose reduction achieved in the follow-up CT scans, although a lower IQ still allows a good diagnostic confidence.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Pneumopatias/diagnóstico por imagem , Doses de Radiação , Radiografia Torácica/métodos , Tomografia Computadorizada por Raios X/métodos , Adulto , Área Sob a Curva , Interpretação Estatística de Dados , Feminino , Humanos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Variações Dependentes do Observador , Exposição à Radiação/prevenção & controle , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiografia Torácica/efeitos adversos , Infecções Respiratórias/diagnóstico por imagem , Razão Sinal-Ruído , Tecnologia Radiológica , Tomografia Computadorizada por Raios X/efeitos adversos
6.
Radiol Med ; 124(12): 1281-1295, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31792703

RESUMO

The physical principles of dual-energy computed tomography (DECT) are as old as computed tomography (CT) itself. To understand the strengths and the limits of this technology, a brief overview of theoretical basis of DECT will be provided. Specific attention will be focused on the interaction of X-rays with matter, on the principles of attenuation of X-rays in CT toward the intrinsic limits of conventional CT, on the material decomposition algorithms (two- and three-basis-material decomposition algorithms) and on effective Rho-Z methods. The progresses in material decomposition algorithms, in computational power of computers and in CT hardware, lead to the development of different technological solutions for DECT in clinical practice. The clinical applications of DECT are briefly reviewed in relation to the specific algorithms.


Assuntos
Algoritmos , Imagem Radiográfica a Partir de Emissão de Duplo Fóton/métodos , Tecnologia Radiológica/métodos , Tomografia Computadorizada por Raios X/métodos , Feminino , Humanos , Masculino , Espalhamento de Radiação , Raios X
8.
Pathologe ; 40(Suppl 3): 271-276, 2019 Dec.
Artigo em Alemão | MEDLINE | ID: mdl-31745604

RESUMO

Radiomics deals with the statistical analysis of radiologic image data. In this article, radiomics is introduced and some of its applications are presented. In particular, an example is used to demonstrate that pathology and radiology can work together for better diagnoses. There is no denying that artificial intelligence will find its place in radiology (and pathology). Deep learning in particular will increasingly find applications. However, the impact on clinical routine is more long term and probably gradual, so AI will initially only be used in the form of specialized tools to support everyday clinical practice until methods and programs improve to the extent that AI can also take on more general diagnoses. However, this will not replace pathologists and radiologists in the long term, but rather turn them into "information specialists" who interpret the results obtained and integrate them into clinical contours.


Assuntos
Inteligência Artificial , Interpretação de Imagem Radiográfica Assistida por Computador , Radiologia , Tecnologia Radiológica , Aprendizado Profundo , Humanos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiografia
12.
Injury ; 50(9): 1511-1515, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31399208

RESUMO

BACKGROUND: Increasing global demand for specialized radiological investigations has resulted in delayed or non-reporting of plain trauma radiographs by radiologists. This is particularly true in resource-limited environments, where referring clinicians rely largely on their own radiographic interpretation. A wide accuracy range has been documented for non-radiologist reporting of conventional trauma radiographs. The Lodox Statscan whole-body digital X-ray machine is a relatively new technology that poses unique interpretive challenges. The fracture detection rate of trauma clinicians utilizing this modality has not been determined. OBJECTIVE: An audit of the polytrauma fracture detection rate of clinicians evaluating Lodox Statscan bodygrams in two South African public-sector Trauma Units. METHODS: A retrospective descriptive study of imaging data of Cape Town Level 1-equivalent public-sector Trauma Units during March-April 2015. Statscan bodygrams acquired for adult polytrauma triage were reviewed and correlated with follow-up imaging and patient records. Missed fractures were stratified by body part, mechanism of injury and ventilatory support. The fracture detection rate was determined with 95% confidence. The Generalised Fischer Exact Test assessed any association between the fracture site and failure of detection. Specialist orthopaedic review assessed the potential need for surgical management of missed fractures. RESULTS: 227 patients (male = 193, 85%; mean age: 33 years) were included; 195 fractures were demonstrated on the whole-body triage projections. Lower limb fractures predominated (n = 66, 34%). The fracture detection rate was 89% (95% CI = 86-93%), with the site of fracture associated with failure of detection (p = 0.01). Twelve of 21 undetected fractures (57%) involved the elbow or shoulder girdle. All elbow fractures (n = 3, 100%), more than half the shoulder girdle fractures (9/13,69%) and 12% (15/123) of extremity fractures were undetected. One missed fracture (1/21,4.7%) unequivocally required surgical management, while a further 7 (7/21, 33.3%) could potentially have benefitted from surgery, depending on follow-up imaging findings. CONCLUSION: This is the first analysis of the accuracy of bodygram polytrauma fracture detection by clinicians. Particular review of the shoulder girdle, elbow and extremities for subtle fractures, in addition to standardized limb positioning, are recommended for improved diagnostic accuracy in this setting. These findings can inform clinician training courses in this domain.


Assuntos
Erros de Diagnóstico/estatística & dados numéricos , Fraturas Ósseas/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/estatística & dados numéricos , Traumatismo Múltiplo/diagnóstico por imagem , Intensificação de Imagem Radiográfica/normas , Centros de Traumatologia/economia , Imagem Corporal Total/normas , Adulto , Auditoria Clínica , Competência Clínica , Erros de Diagnóstico/economia , Feminino , Fraturas Ósseas/economia , Humanos , Masculino , Traumatismo Múltiplo/economia , Valor Preditivo dos Testes , Setor Público , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sensibilidade e Especificidade , África do Sul/epidemiologia , Tecnologia Radiológica/instrumentação , Tomografia Computadorizada por Raios X , Centros de Traumatologia/normas , Triagem , Imagem Corporal Total/economia
14.
Artigo em Japonês | MEDLINE | ID: mdl-31327786
15.
Simul Healthc ; 14(4): 258-263, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31274828

RESUMO

INTRODUCTION: Immersive virtual reality (VR) simulation environments facilitate novel ways for users to visualize anatomy and quantify performance relative to expert users. The ability of software to provide positional feedback before a practitioner progresses with subsequent stages of examinations has broad implications for primary and allied healthcare professionals, particularly with respect to health and safety (eg, exposing to x-rays). The effect of training student-radiographers (radiology technicians), with a VR simulation environment was quantitatively assessed. METHODS: Year 1 radiography students (N = 76) were randomly split into 2 cohorts, each of which were trained at performing the same tasks relating to optimal hand positioning for projection x-ray imaging; group 1 was trained using the CETSOL VR Clinic software, whereas group 2 was trained using conventional simulated role-play in a real clinical environment. All participants completed an examination 3 weeks after training. The examination required both posterior-anterior and oblique hand x-ray positioning tasks to be performed on a real patient model. The analysis of images from the examination enabled quantification of the students' performance. The results were contextualized through a comparison with 4 expert radiographers. RESULTS: Students in group 1 performed on average 36% (P < 0.001) better in relation to digit separation, 11% (P ≤ 0.001) better in terms of palm flatness and 23% (P < 0.05) better in terms of central ray positioning onto the third metacarpal. CONCLUSION: A significant difference in patient positioning was evident between the groups; the VR clinic cohort demonstrated improved patient positioning outcomes. The observed improvement is attributed to the inherent task deconstruction and variety of visualization mechanisms available in immersive VR environments.


Assuntos
Instrução por Computador/métodos , Treinamento por Simulação/métodos , Tecnologia Radiológica/educação , Realidade Virtual , Comunicação , Humanos , Posicionamento do Paciente
16.
Int J Radiat Oncol Biol Phys ; 105(3): 495-503, 2019 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-31271823

RESUMO

PURPOSE: The first aim of this work is to present a novel deep convolution neural network (DCNN) multiplane approach and compare it to single-plane prediction of synthetic computed tomography (sCT) by using the real computed tomography (CT) as ground truth. The second aim is to demonstrate the feasibility of magnetic resonance imaging (MRI)-based proton therapy planning for the brain by assessing the range shift error within the clinical acceptance threshold. METHODS AND MATERIALS: The image database included 15 pairs of MRI/CT scans of the head. Three DCNNs were trained to estimate, for each voxel, the Hounsfield unit (HU) value from MRI intensities. Each DCNN gave an estimation in the axial, sagittal, and coronal plane, respectively. The median HU among the 3 values was selected to build the sCT. The sCT/CT agreement was evaluated by a mean absolute error (MAE) and mean error, computed within the head contour and on 6 different tissues. Dice similarity coefficients were calculated to assess the geometric overlap of bone and air cavities segmentations. A 3-beam proton therapy plan was simulated for each patient. Beam-by-beam range shift (RS) analysis was conducted to assess the proton-stopping power estimation. RS analysis was performed using clinically accepted thresholds of (1) 3.5% + 1 mm and (2) 2.5% + 1.5 mm of the total range. RESULTS: DCNN multiplane statistically outperformed single-plane prediction of sCT (P < .025). MAE and mean error within the head were 54 ± 7 HU and -4 ± 17 HU (mean ± standard deviation), respectively. Soft tissues were very close to perfect agreement (11 ± 3 HU in terms of MAE). Segmentation of air and bone regions led to a Dice similarity coefficient of 0.92 ± 0.03 and 0.93 ± 0.02, respectively. Proton RS was always below clinical acceptance thresholds, with a relative RS error of 0.14% ± 1.11%. CONCLUSIONS: The multiplane DCNN approach significantly improved the sCT prediction compared with other DCNN methods presented in the literature. The method was demonstrated to be highly accurate for MRI-only proton planning purposes.


Assuntos
Neoplasias Encefálicas , Glioblastoma , Imagem por Ressonância Magnética/métodos , Redes Neurais de Computação , Terapia com Prótons/métodos , Planejamento da Radioterapia Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Ar , Algoritmos , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/radioterapia , Estudos de Viabilidade , Glioblastoma/diagnóstico por imagem , Glioblastoma/radioterapia , Cabeça/diagnóstico por imagem , Humanos , Imagem Multimodal/métodos , Dosagem Radioterapêutica , Radioterapia Guiada por Imagem/métodos , Reprodutibilidade dos Testes , Crânio/diagnóstico por imagem , Tecnologia Radiológica/métodos
17.
Int J Radiat Oncol Biol Phys ; 105(2): 423-431, 2019 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-31158426

RESUMO

PURPOSE: To investigate a Bayesian network (BN)-based method to detect errors in external beam radiation therapy physician orders. METHODS AND MATERIALS: A total of 4431 external beam radiation therapy orders from 2008 to 2017 at the authors' institution were obtained from clinical treatment management systems and divided into 3 groups: single prescription, concurrent boost, and sequential boost. Multiple BNs were developed for each group to detect errors in new orders using joint posterior probabilities of the order parameters, given disease information. Each BN was trained with a group of orders using a Bayesian learning algorithm. A procedure was developed to select the optimal BN for each treatment site in each group and to determine site-specific parameters and error detection thresholds. Potential clinical errors, created both manually and automatically, were applied to test error detection performance. RESULTS: The average true-positive rate (TPR) and false-positive rate (FPR) of error detection were 95.72% and 1.99%, respectively, for the single-prescription cohort with 9 treatment sites. For the concurrent-boost cohort, the TPR and FPR were 92.94% and 14.53%, respectively. For the sequential-boost cohort, the TPR and FPR were 100% and 9.48%, respectively, for the prescribed dose values and 100% and 4.34%, respectively, for the remaining order parameters. For the patient simulation and imaging parameters for 9 treatment sites, the TPR and FPR were 100% and 4.96%, respectively. CONCLUSIONS: The probabilistic BN method was able to perform physician order error detection at a higher accuracy than previously reported in a variety of complex prescription instances, thus warranting further development in incorporating BNs into clinical error detection tools to assist manual physician order checks.


Assuntos
Teorema de Bayes , Erros Médicos/estatística & dados numéricos , Neoplasias/radioterapia , Redes Neurais de Computação , Radiologistas/estatística & dados numéricos , Algoritmos , Estudos de Coortes , Conjuntos de Dados como Assunto , Fracionamento da Dose de Radiação , Reações Falso-Negativas , Reações Falso-Positivas , Humanos , Erros Médicos/prevenção & controle , Neoplasias/patologia , Especificidade de Órgãos , Curva ROC , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador , Erros de Configuração em Radioterapia , Radioterapia Guiada por Imagem , Treinamento por Simulação , Tecnologia Radiológica
18.
AJR Am J Roentgenol ; 213(5): 1003-1007, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31216200

RESUMO

OBJECTIVE. The purpose of this study was to evaluate the technologist productivity and accuracy in assigning protocols for abdominal CT and MRI examinations compared with a standard work flow whereby protocols are assigned by physicians. MATERIALS AND METHODS. In this quality improvement project at a large academic medical center, two CT technologists and two MRI technologists assigned protocols for examinations during a 15-week study period. The primary outcome measure was mean number of protocols assigned by technologists per hour. Secondary outcome measures were proportion of examinations with protocols assigned by technologists and rate of filing of quality assurance reports for protocols completed by technologists. A two-tailed t test was used to compare mean number of protocols; a chi-square test was used to compare proportions between CT and MRI. RESULTS. The mean number of protocols assigned by technologists per hour was not different between CT and MRI (CT, 22/h; MRI, 19/h; p = 0.28). CT and MRI technologist protocols accounted for 1650 of 4867 (33.9%) CT examinations (range, 23-275 per week) and 569 of 2388 (23.8%) MRI examinations (range, 0-95 per week) (p < 0.001). Radiologist quality assurance reports on inaccurate protocols were rare: three for CT (3/1650 [0.18%]), five for MRI (5/569 [0.88%]) (p = 0.017). A retrospective review of randomly selected CT and MRI protocols revealed no errors (80/80 correct). No patients were called back for repeat imaging due to protocol error. CONCLUSION. Technologists can efficiently and accurately assign protocols for abdominal CT and MRI examinations at an academic medical center, leading to increased radiologist time spent on other value-added activities.


Assuntos
Eficiência , Imagem por Ressonância Magnética , Radiografia Abdominal , Radiologistas/estatística & dados numéricos , Tecnologia Radiológica , Tomografia Computadorizada por Raios X , Carga de Trabalho/estatística & dados numéricos , Centros Médicos Acadêmicos , Competência Clínica , Feminino , Humanos , Masculino , Melhoria de Qualidade , Estudos Retrospectivos
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