Computer-Aided Diagnosis of Vertebral Compression Fractures Using Convolutional Neural Networks and Radiomics.

Vertebral Compression Fracture (VCF) occurs when the vertebral body partially collapses under the action of compressive forces. Non-traumatic VCFs can be secondary to osteoporosis fragility (benign VCFs) or tumors (malignant VCFs). The investigation of the etiology of non-traumatic VCFs is usually necessary, since treatment and prognosis are dependent on the VCF type. Currently, there has been great interest in using Convolutional Neural Networks (CNNs) for the classification of medical images because these networks allow the automatic extraction of useful features for the classification in a given problem. However, CNNs usually require large datasets that are often not available in medical applications. Besides, these networks generally do not use additional information that may be important for classification. A different approach is to classify the image based on a large number of predefined features, an approach known as radiomics. In this work, we propose a hybrid method for classifying VCFs that uses features from three different sources: i) intermediate layers of CNNs; ii) radiomics; iii) additional clinical and image histogram information. In the hybrid method proposed here, external features are inserted as additional inputs to the first dense layer of a CNN. A Genetic Algorithm is used to: i) select a subset of radiomic, clinical, and histogram features relevant to the classification of VCFs; ii) select hyper-parameters of the CNN. Experiments using different models indicate that combining information is interesting to improve the performance of the classifier. Besides, pre-trained CNNs presents better performance than CNNs trained from scratch on the classification of VCFs.

Radiomic Quantification for MRI Assessment of Sacroiliac Joints of Patients with Spondyloarthritis.

Spondyloarthritis (SpA) is a group of diseases primarily involving chronic inflammation of the spine and peripheral joints, as evaluated by magnetic resonance imaging (MRI). Considering the complexity of SpA, we performed a retrospective study to discover quantitative/radiomic MRI-based features correlated with SpA. We also investigated different fat-suppression MRI techniques to develop detection models for inflammatory sacroiliitis. Finally, these model results were compared with those of experienced musculoskeletal radiologists, and the concordance level was evaluated. Examinations of 46 consecutive patients were obtained using SPAIR (spectral attenuated inversion recovery) and STIR (short tau inversion recovery) MRI sequences. Musculoskeletal radiologists manually segmented the sacroiliac joints for further extraction of 230 MRI features from gray-level histogram/matrices and wavelet filters. These features were associated with sacroiliitis, SpA, and the current biomarkers of ESR (erythrocyte sedimentation rate), CRP (C-reactive protein), BASDAI (Bath Ankylosing Spondylitis Activity Index), BASFI (Bath Ankylosing Spondylitis Functional Index), and MASES (Maastricht Ankylosing Spondylitis Enthesis Score). The Mann-Whitney U test showed that the radiomic markers from both MRI sequences were associated with active sacroiliitis and with SpA and its axial and peripheral subtypes (p < 0.05). Spearman's coefficient also identified a correlation between MRI markers and data from clinical practice (p < 0.05). Fat-suppression MRI models yielded performances that were statistically equivalent to those of specialists and presented strong concordance in identifying inflammatory sacroiliitis. SPAIR and STIR acquisition protocols showed potential for the evaluation of sacroiliac joints and the composition of a radiomic model to support the clinical assessment of SpA.

Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times.

OBJECTIVE: To evaluate the degree of similarity between manual and semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging (MRI). MATERIALS AND METHODS: This was a retrospective study of 15 MRI examinations of patients with histopathologically confirmed soft-tissue sarcomas acquired before therapeutic intervention. Manual and semiautomatic segmentations were performed by three radiologists, working independently, using the software 3D Slicer. The Dice similarity coefficient (DSC) and the Hausdorff distance were calculated in order to evaluate the similarity between manual and semiautomatic segmentation. To compare the two modalities in terms of the tumor volumes obtained, we also calculated descriptive statistics and intraclass correlation coefficients (ICCs). RESULTS: In the comparison between manual and semiautomatic segmentation, the DSC values ranged from 0.871 to 0.973. The comparison of the volumes segmented by the two modalities resulted in ICCs between 0.9927 and 0.9990. The DSC values ranged from 0.849 to 0.979 for intraobserver variability and from 0.741 to 0.972 for interobserver variability. There was no significant difference between the semiautomatic and manual modalities in terms of the segmentation times (p > 0.05). CONCLUSION: There appears to be a high degree of similarity between manual and semiautomatic segmentation, with no significant difference between the two modalities in terms of the time required for segmentation.

OBJETIVO: Verificar a similaridade entre as segmentações manual e semiautomática de sarcomas de tecidos moles na ressonância magnética (RM) e a similaridade interobservador e intraobservador entre as segmentações manuais. MATERIAIS E MÉTODOS: Estudo retrospectivo que incluiu 15 exames de RM de pacientes com diagnóstico de sarcoma de tecidos moles realizados antes de intervenções terapêuticas. As segmentações manual e semiautomática foram realizadas por três radiologistas utilizando o software 3D Slicer. O coeficiente de similaridade Dice (CSD) e a distância de Hausdorff foram utilizados para avaliar a similaridade das segmentações. Análise estatística descritiva e coeficiente de correlação intraclasse (CCI) foram realizados para comparar volumes tumorais. RESULTADOS: A comparação dos métodos manual e semiautomático obteve valores de CSD entre 0,871 e 0,973. A comparação dos volumes segmentados pelos dois métodos de segmentação mostrou CCI entre 0,9927 e 0,9990. As análises intraobservador e interobservador obtiveram valores de CSD, respectivamente, de 0,849 a 0,979 e de 0,741 a 0,972. Não houve diferença significativa entre os tempos de segmentação dos métodos semiautomático e manual (p > 0,05). CONCLUSÃO: Houve alta similaridade entre as segmentações de sarcomas de tecidos moles obtidas pelos métodos manual e semiautomático, sem diferença significativa para o tempo despendido para as segmentações.

The UTrack framework for segmenting and measuring dermatological ulcers through telemedicine.

Chronic dermatological ulcers cause great discomfort to patients, and while monitoring the size of wounds over time provides significant clues about the healing evolution and the clinical condition of patients, the lack of practical applications in existing studies impairs users' access to appropriate treatment and diagnosis methods. We propose the UTrack framework to help with the acquisition of photos, the segmentation and measurement of wounds, the storage of photos and symptoms, and the visualization of the evolution of ulcer healing. UTrack-App is a mobile app for the framework, which processes images taken by standard mobile device cameras without specialized equipment and stores all data locally. The user manually delineates the regions of the wound and the measurement object, and the tool uses the proposed UTrack-Seg segmentation method to segment them. UTrack-App also allows users to manually input a unit of measurement (centimeter or inch) in the image to improve the wound area estimation. Experiments show that UTrack-Seg outperforms its state-of-the-art competitors in ulcer segmentation tasks, improving F-Measure by up to 82.5% when compared to superpixel-based approaches and up to 19% when compared to Deep Learning ones. The method is unsupervised, and it semi-automatically segments real-world images with 0.9 of F-Measure, on average. The automatic measurement outperformed the manual process in three out of five different rulers. UTrack-App takes at most 30 s to perform all evaluation steps over high-resolution images, thus being well-suited to analyze ulcers using standard mobile devices.

Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity.

OBJECTIVE: To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. MATERIALS AND METHODS: This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. RESULTS: Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). CONCLUSION: A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.

OBJETIVO: Associar características radiômicas de lesões pulmonares em imagens de tomografia computadorizada com a sobrevida global de pacientes com câncer de pulmão. MATERIAIS E MÉTODOS: Estudo retrospectivo composto por 101 pacientes consecutivos com neoplasia maligna confirmada por biópsia/cirurgia. As lesões foram semiautomaticamente segmentadas e caracterizadas por 2.465 variáveis radiômicas. A avaliação prognóstica foi baseada na análise de Kaplan-Meier e no teste log-rank, de acordo com a mediana dos valores das variáveis. RESULTADOS: Vinte e oito pacientes faleceram (16 por câncer de pulmão) e 73 foram censurados, com tempo médio de sobrevida de 1.819,4 dias (intervalo de confiança 95% [IC 95%]: 1.481,2-2.157,5). Uma característica radiômica (média de Fourier) apresentou diferença nas curvas de Kaplan-Meier (p < 0,05). Um grupo de pacientes de maior risco foi identificado a partir de valores altos da variável: sobrevida de 1.465,4 dias (IC 95%: 985,2-1.945,6) e razão de risco de 2,12 (IC 95%: 1,01-4,48). Um grupo de menor risco foi identificado a partir de valores baixos da variável (sobrevida de 2.164,8 dias; IC 95%: 1.745,4-2.584,1). CONCLUSÃO: Este estudo apresentou uma assinatura radiômica em imagens de tomografia computadorizada, baseada na transformada de Fourier, correlacionada com a sobrevida global de pacientes com câncer de pulmão, representando assim um biomarcador prognóstico.

A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis.

PURPOSE: To evaluate the performance of texture-based biomarkers by radiomic analysis using magnetic resonance imaging (MRI) of patients with sacroiliitis secondary to spondyloarthritis (SpA). RELEVANCE: The determination of sacroiliac joints inflammatory activity supports the drug management in these diseases. METHODS: Sacroiliac joints (SIJ) MRI examinations of 47 patients were evaluated. Thirty-seven patients had SpA diagnoses (27 axial SpA and ten peripheral SpA) which was established previously after clinical and laboratory follow-up. To perform the analysis, the SIJ MRI was first segmented and warped. Second, radiomics biomarkers were extracted from the warped MRI images for associative analysis with sacroiliitis and the SpA subtypes. Finally, statistical and machine learning methods were applied to assess the associations of the radiomics texture-based biomarkers with clinical outcomes. RESULTS: All diagnostic performances obtained with individual or combined biomarkers reached areas under the receiver operating characteristic curves ≥ 0.80 regarding SpA related sacroiliitis and and SpA subtypes classification. Radiomics texture-based analysis showed significant differences between the positive and negative SpA groups and differentiated the axial and peripheral subtypes (P < 0.001). In addition, the radiomics analysis was also able to correctly identify the disease even in the absence of active inflammation. CONCLUSION: We concluded that the application of the radiomic approach constitutes a potential noninvasive tool to aid the diagnosis of sacroiliitis and for SpA subclassifications based on MRI of sacroiliac joints.

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging.

BACKGROUND: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologists remains subject to significant intrapersonal and interpersonal variation. This encouraged us to use machine-learning methods for this task. METHODS: In this retrospective study including 56 sacroiliac joint MRI exams, 24 patients had positive and 32 had negative findings for inflammatory sacroiliitis according to the ASAS group criteria. The dataset was randomly split with ~ 80% (46 samples, 20 positive and 26 negative) as training and ~ 20% as external test (10 samples, 4 positive and 6 negative). After manual segmentation of the images by a musculoskeletal radiologist, multiple features were extracted. The classifiers used were the Support Vector Machine, the Multilayer Perceptron (MLP), and the Instance-Based Algorithm, combined with the Relief and Wrapper methods for feature selection. RESULTS: Based on 10-fold cross-validation using the training dataset, the MLP classifier obtained the best performance with sensitivity = 100%, specificity = 95.6% and accuracy = 84.7%, using 6 features selected by the Wrapper method. Using the test dataset (external validation) the same MLP classifier obtained sensitivity = 100%, specificity = 66.7% and accuracy = 80%. CONCLUSIONS: Our results show the potential of machine learning methods to identify SIJ subchondral bone marrow edema in axSpA patients and are promising to aid in the detection of active inflammatory sacroiliitis on MRI STIR sequences. Multilayer Perceptron (MLP) achieved the best results.

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.

PURPOSE: As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malignant lung tumors with distant and nodal metastases (according to clinical TNM staging) and histopathology (according to biopsy and surgical resection) using radiomics assessment. METHODS: A local cohort of 85 patients were retrospectively (2010-2017) analyzed after approval by the institutional research review board. CT images acquired with the same protocol were semiautomatically segmented by a volumetric segmentation method. Tumors were characterized by quantitative CT features of shape, first-order, second-order, and higher-order textures. Statistical and machine learning analyses assessed the features individually and combined with clinical data. RESULTS: Univariate and multivariate analyses identified 40, 2003, and 45 quantitative features associated with distant metastasis, nodal metastasis, and histopathology (adenocarcinoma and squamous cell carcinoma), respectively. A machine learning model yielded the highest areas under the receiver operating characteristic curves of 0.92, 0.84, and 0.88 to predict the same previous patterns. CONCLUSION: Several radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura's texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.

Blockchain Based Network for Tuberculosis: A Data Sharing Initiative in Brazil.

Data sharing, information exchange, knowledge acquisition and health intelligence are the basis of an efficient and effective evidence-based decision-making tool. A decentralized blockchain architecture is a flexible solution that can be adapted to institutional and managerial culture of organizations and services. Blockchain can play a fundamental role in enabling data sharing within a network and, to achieve that, this work defines the high-level resources necessary to apply this technology to Tuberculosis related issues. Thus, relying in open-source tools and in a collaborative development approach, we present a proposal of a blockchain based network, the TB Network, to underpin an initiative of sharing of Tuberculosis scientific, operational and epidemiologic data between several stakeholders across Brazilian cities.

Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture.

Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PURPOSE: Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. METHODS: A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. RESULTS: Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. CONCLUSION: Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research.

Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.

Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images.

PURPOSE: Vertebral compression fractures (VCFs) result in partial collapse of vertebral bodies. They usually are nontraumatic or occur with low-energy trauma in the elderly secondary to different etiologies, such as insufficiency fractures of bone fragility in osteoporosis (benign fractures) or vertebral metastasis (malignant fractures). Our study aims to classify VCFs in T1-weighted magnetic resonance images (MRI). METHODS: We used the median sagittal planes of lumbar spine MRIs from 63 patients (38 women and 25 men) previously diagnosed with VCFs. The lumbar vertebral bodies were manually segmented and statistical features of gray levels were computed from the histogram. We also extracted texture and shape features to analyze the contours of the vertebral bodies. In total, 102 lumbar VCFs (53 benign and 49 malignant) and 89 normal lumbar vertebral bodies were analyzed. The k-nearest-neighbor method, a neural network with radial basis functions, and a naïve Bayes classifier were used with feature selection. We compared the classification obtained by these classifiers with the final diagnosis of each case, including biopsy for the malignant fractures and clinical and laboratory follow up for the benign fractures. RESULTS: The results obtained show an area under the receiver operating characteristic curve of 0.97 in distinguishing between normal and fractured vertebral bodies, and 0.92 in discriminating between benign and malignant fractures. CONCLUSIONS: The proposed classification methods based on shape, texture, and statistical features have provided high accuracy and may assist in the diagnosis of VCFs.

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy.

Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.

Analyses of Budget Impact Considering the Use of the Picture Archiving and Communication System.

OBJECTIVE: To analyze the budget impact of using the picture archiving and communication system (PACS) in comparison to the screen/film system. METHODS: The budget impact analysis was conducted on the basis of registry data from the Clinics Hospital of the Faculty of Medicine, University of São Paulo, Ribeirão Preto, Brazil. The budget impacts were compared between the PACS, with high- and low-cost PACS architectures, and the screen/film system by considering reference and alternative scenarios over the course of 5 years. RESULTS: The budget impact associated with the use of PACS was lower than that associated with the use of the screen/film system in all the evaluated scenarios. The low-cost PACS architecture (mini-PACS) had an even lower budget impact, especially in the scenario in which a simulation of lower numbers of medical examinations was performed. CONCLUSIONS: The screen/film system had a high budget impact in all the scenarios evaluated, wherein its costs were higher than the available budget. In contrast, the PACS (high- and low-cost architectures) showed a budget impact that allowed for savings in resources, especially the mini-PACS. Therefore, we recommend the implementation and use of the PACS in health services with any volume of examinations performed.

Characterization of a lead breast shielding for dose reduction in computed tomography.

OBJECTIVE: Several studies have been published regarding the use of bismuth shielding to protect the breast in computed tomography (CT) scans and, up to the writing of this article, only one publication about barium shielding was found. The present study was aimed at characterizing, for the first time, a lead breast shielding. MATERIALS AND METHODS: The percentage dose reduction and the influence of the shielding on quantitative imaging parameters were evaluated. Dose measurements were made on a CT equipment with the aid of specific phantoms and radiation detectors. A processing software assisted in the qualitative analysis evaluating variations in average CT number and noise on images. RESULTS: The authors observed a reduction in entrance dose by 30% and in CTDIvol by 17%. In all measurements, in agreement with studies in the literature, the utilization of cotton fiber as spacer object reduced significantly the presence of artifacts on the images. All the measurements demonstrated increase in the average CT number and noise on the images with the presence of the shielding. CONCLUSION: As expected, the data observed with the use of lead shielding were of the same order as those found in the literature about bismuth shielding.

OBJETIVO: Diversos estudos foram publicados quanto ao uso de blindagens de bismuto para proteção de mamas em exames de tomografia computadorizada (TC), e até a redação deste artigo encontrou-se apenas uma publicação sobre blindagens de bário. O objetivo deste estudo foi caracterizar, pela primeira vez, uma manta plumbífera para proteção de mamas. MATERIAIS E MÉTODOS: Foram avaliadas a redução percentual da dose e a influência desta blindagem em parâmetros quantitativos da imagem. Medidas de dose foram feitas em um equipamento de TC com auxílio de fantomas específicos e detectores de radiação. Um software de processamento auxiliou na análise qualitativa, que consistiu em avaliar a variação no número médio de TC e do ruído nas imagens. RESULTADOS: Uma redução de dose na entrada em até 30% e do CTDIvol em até 17% foi encontrada. Como previsto na literatura, a presença do algodão como objeto espaçador reduziu significativamente os artefatos presentes na imagem. Em todas as medidas realizadas foi constatado aumento do número médio de TC e do ruído das imagens na presença da manta. CONCLUSÃO: Como esperado, os dados encontrados para a blindagem com chumbo foram da mesma ordem daqueles encontrados na literatura para blindagem com bismuto.

Deterministic record linkage versus similarity functions: a study in health databases from Brazil.

The record linkage is a strategy that allows linking different databases of information from patient records. Adopting the deterministic method and similarity functions (Dice, Jaro, Jaro-Winkler and Levenshtein) for the integration of heterogeneous databases aimed at different levels of health care Brazilian (primary, secondary and tertiary). The sensitivity of deterministic method was 54.5% (95% CI: 50.4 to 58.5). The best result obtained with the dissent of only one variable (mother's name) was 80.6% (95% CI: 77.2 to 83.6) and the best result obtained using the similarity function Jaro-Winkler was 91.8% (95% CI: 89.4 to 93.9). The deterministic method has high specificity but sensitivity can be reduced by the existence of spellings and typing errors in the databases. Thus, the step-by-step approach where there was disagreement in at least one of the relationship variable can increase the sensitivity of the method and the use of similarity functions.