Pulmonary lymphangioleiomyomatosis: analysis of disease manifestation by region-based quantification of lung parenchyma

PURPOSE Lymphangioleiomyomatosis (LAM) is characterized by proliferation of smooth muscle tissue that causes bronchial obstruction and secondary cystic destruction of lung parenchyma. The aim of this study was to evaluate the typical distribution of cystic defects in LAM with quantitative volumetric chest computed tomography (CT). MATERIALS AND METHODS CT examinations of 20 patients with confirmed LAM were evaluated with region-based quantification of lung parenchyma. Additionally, 10 consecutive patients were identified who had recently undergone CT imaging of the lung at our institution, in which no pathologies of the lung were found, to serve as a control group. Each lung was divided into three regions (upper, middle and lower thirds) with identical number of slices. In addition, we defined a "peel" and "core" of the lung comprising the 2 cm subpleural space and the remaining inner lung area. Computerized detection of lung volume and relative emphysema was performed with the PULMO 3D software (v3.42, Fraunhofer MEVIS, Bremen, Germany). This software package enables the quantification of emphysematous lung parenchyma by calculating the pixel index, which is defined as the ratio of lung voxels with a density <-950HU to the total number of voxels in the lung. RESULTS Cystic changes accounted for 0.1-39.1% of the total lung volume in patients with LAM. Disease manifestation in the central lung was significantly higher than in peripheral areas (peel median: 15.1%, core median: 20.5%; p=0.001). Lower thirds of lung parenchyma showed significantly less cystic changes than upper and middle lung areas combined (lower third: median 13.4, upper and middle thirds: median 19.0, p=0.001). CONCLUSION The distribution of cystic lesions in LAM is significantly more pronounced in the central lung compared to peripheral areas. There is a significant predominance of cystic changes in apical and intermediate lung zones compared to the lung bases.

Robustness of the Voluntary Breath-Hold Approach for the Treatment of Peripheral Lung Tumors Using Hypofractionated Pencil Beam Scanning Proton Therapy.

PURPOSE The safe clinical implementation of pencil beam scanning (PBS) proton therapy for lung tumors is complicated by the delivery uncertainties caused by breathing motion. The purpose of this feasibility study was to investigate whether a voluntary breath-hold technique could limit the delivery uncertainties resulting from interfractional motion. METHODS AND MATERIALS Data from 15 patients with peripheral lung tumors previously treated with stereotactic radiation therapy were included in this study. The patients had 1 computed tomographic (CT) scan in voluntary breath-hold acquired before treatment and 3 scans during the treatment course. PBS proton treatment plans with 2 fields (2F) and 3 fields (3F), respectively, were calculated based on the planning CT scan and subsequently recalculated on the 3 repeated CT scans. Recalculated plans were considered robust if the V95% (volume receiving ≥95% of the prescribed dose) of the gross target volume (GTV) was within 5% of what was expected from the planning CT data throughout the simulated treatment. RESULTS A total of 14/15 simulated treatments for both 2F and 3F met the robustness criteria. Reduced V95% was associated with baseline shifts (2F, P=.056; 3F, P=.008) and tumor size (2F, P=.025; 3F, P=.025). Smaller tumors with large baseline shifts were also at risk for reduced V95% (interaction term baseline/size: 2F, P=.005; 3F, P=.002). CONCLUSIONS The breath-hold approach is a realistic clinical option for treating lung tumors with PBS proton therapy. Potential risk factors for reduced V95% are small targets in combination with large baseline shifts. On the basis of these results, the baseline shift of the tumor should be monitored (eg, through image guided therapy), and appropriate measures should be taken accordingly. The intrafractional motion needs to be investigated to confirm that the breath-hold approach is robust.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Assessment of cone beam computed tomography techniques for imaging lung damage in mice in vivo

Lung damage is a common side effect of chemotherapeutic drugs such as bleomycin. This study used a bleomycin mouse model which simulates the lung damage observed in humans. Noninvasive, in vivo cone-beam computed tomography (CBCT) was used to visualize and quantify fibrotic and inflammatory damage over the entire lung volume of mice. Bleomycin was used to induce pulmonary damage in vivo and the results from two CBCT systems, a micro-CT and flat panel CT (fpCT), were compared to histologic measurements, the standard method of murine lung damage quantification. Twenty C57BL/6 mice were given either 3 U/kg of bleomycin or saline intratracheally. The mice were scanned at baseline, before the administration of bleomycin, and then 10, 14, and 21 days afterward. At each time point, a subset of mice was sacrificed for histologic analysis. The resulting CT images were used to assess lung volume. Percent lung damage (PLD) was calculated for each mouse on both the fpCT (PLDfpcT) and the micro-CT (PLDμCT). Histologic PLD (PLDH) was calculated for each histologic section at each time point (day 10, n = 4; day 14, n = 4; day 21, n = 5; control group, n = 5). A linear regression was applied to the PLDfpCT vs. PLDH, PLDμCT vs. PLDH and PLDfpCT vs. PLDμCT distributions. This study did not demonstrate strong correlations between PLDCT and PLDH. The coefficient of determination, R, was 0.68 for PLDμCT vs. PLDH and 0.75 for the PLD fpCT vs. PLDH. The experimental issues identified from this study were: (1) inconsistent inflation of the lungs from scan to scan, (2) variable distribution of damage (one histologic section not representative of overall lung damage), (3) control mice not scanned with each group of bleomycin mice, (4) two CT systems caused long anesthesia time for the mice, and (5) respiratory gating did not hold the volume of lung constant throughout the scan. Addressing these issues might allow for further improvement of the correlation between PLDCT and PLDH. ^

SABR for early-stage lung cancer: Early-adoption and future directions

Early-stage lung cancer incidence among older adults is expected to increase due to demographic trends and CT-based screening, yet optimal treatment of lung cancer in the elderly remains controversial. There are several accepted strategies for treating lung cancer including surgery, conventional radiation, and stereotactic ablative body radiotherapy (SABR). However, there are currently no randomized controlled trials to help distinguish the comparative effectiveness of these various strategies. This is an unfortunate omission as lung cancer causes the most deaths among all cancers in the United States (as well as the entire world). SABR holds particular promise as it is a completely non-invasive, ambulatory technique for achieving cure without an operation, thus avoiding the risks of surgery and the associated pre-operative and post-operative costs. To provide fair view of the potential effect on SABR on controlling lung cancer in the United States, a systematic review of SABR with a focus on its achieved outcomes, toxicities, and comparison to conventional radiation and surgical options is presented. ^

Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.

3D Frangi-based lung vessel enhancement filter penalizing airways

This paper describes a fully automatic simultaneous lung vessel and airway enhancement filter. The approach consists of a Frangi-based multiscale vessel enhancement filtering specifically designed for lung vessel and airway detection, where arteries and veins have high contrast with respect to the lung parenchyma, and airway walls are hollow tubular structures with a non negative response using the classical Frangi's filter. The features extracted from the Hessian matrix are used to detect centerlines and approximate walls of airways, decreasing the filter response in those areas by applying a penalty function to the vesselness measure. We validate the segmentation method in 20 CT scans with different pathological states within the VESSEL12 challenge framework. Results indicate that our approach obtains good results, decreasing the number of false positives in airway walls.

Pulmonary Artery-Vein Segmentation in Real and Synthetic CT Images = Segmentación Arteria-Vena Pulmonares en Imágenes de CT Reales y Sintéticas

La tomografía axial computerizada (TAC) es la modalidad de imagen médica preferente para el estudio de enfermedades pulmonares y el análisis de su vasculatura. La segmentación general de vasos en pulmón ha sido abordada en profundidad a lo largo de los últimos años por la comunidad científica que trabaja en el campo de procesamiento de imagen; sin embargo, la diferenciación entre irrigaciones arterial y venosa es aún un problema abierto. De hecho, la separación automática de arterias y venas está considerado como uno de los grandes retos futuros del procesamiento de imágenes biomédicas. La segmentación arteria-vena (AV) permitiría el estudio de ambas irrigaciones por separado, lo cual tendría importantes consecuencias en diferentes escenarios médicos y múltiples enfermedades pulmonares o estados patológicos. Características como la densidad, geometría, topología y tamaño de los vasos sanguíneos podrían ser analizados en enfermedades que conllevan remodelación de la vasculatura pulmonar, haciendo incluso posible el descubrimiento de nuevos biomarcadores específicos que aún hoy en dípermanecen ocultos. Esta diferenciación entre arterias y venas también podría ayudar a la mejora y el desarrollo de métodos de procesamiento de las distintas estructuras pulmonares. Sin embargo, el estudio del efecto de las enfermedades en los árboles arterial y venoso ha sido inviable hasta ahora a pesar de su indudable utilidad. La extrema complejidad de los árboles vasculares del pulmón hace inabordable una separación manual de ambas estructuras en un tiempo realista, fomentando aún más la necesidad de diseñar herramientas automáticas o semiautomáticas para tal objetivo. Pero la ausencia de casos correctamente segmentados y etiquetados conlleva múltiples limitaciones en el desarrollo de sistemas de separación AV, en los cuales son necesarias imágenes de referencia tanto para entrenar como para validar los algoritmos. Por ello, el diseño de imágenes sintéticas de TAC pulmonar podría superar estas dificultades ofreciendo la posibilidad de acceso a una base de datos de casos pseudoreales bajo un entorno restringido y controlado donde cada parte de la imagen (incluyendo arterias y venas) está unívocamente diferenciada. En esta Tesis Doctoral abordamos ambos problemas, los cuales están fuertemente interrelacionados. Primero se describe el diseño de una estrategia para generar, automáticamente, fantomas computacionales de TAC de pulmón en humanos. Partiendo de conocimientos a priori, tanto biológicos como de características de imagen de CT, acerca de la topología y relación entre las distintas estructuras pulmonares, el sistema desarrollado es capaz de generar vías aéreas, arterias y venas pulmonares sintéticas usando métodos de crecimiento iterativo, que posteriormente se unen para formar un pulmón simulado con características realistas. Estos casos sintéticos, junto a imágenes reales de TAC sin contraste, han sido usados en el desarrollo de un método completamente automático de segmentación/separación AV. La estrategia comprende una primera extracción genérica de vasos pulmonares usando partículas espacio-escala, y una posterior clasificación AV de tales partículas mediante el uso de Graph-Cuts (GC) basados en la similitud con arteria o vena (obtenida con algoritmos de aprendizaje automático) y la inclusión de información de conectividad entre partículas. La validación de los fantomas pulmonares se ha llevado a cabo mediante inspección visual y medidas cuantitativas relacionadas con las distribuciones de intensidad, dispersión de estructuras y relación entre arterias y vías aéreas, los cuales muestran una buena correspondencia entre los pulmones reales y los generados sintéticamente. La evaluación del algoritmo de segmentación AV está basada en distintas estrategias de comprobación de la exactitud en la clasificación de vasos, las cuales revelan una adecuada diferenciación entre arterias y venas tanto en los casos reales como en los sintéticos, abriendo así un amplio abanico de posibilidades en el estudio clínico de enfermedades cardiopulmonares y en el desarrollo de metodologías y nuevos algoritmos para el análisis de imágenes pulmonares. ABSTRACT Computed tomography (CT) is the reference image modality for the study of lung diseases and pulmonary vasculature. Lung vessel segmentation has been widely explored by the biomedical image processing community, however, differentiation of arterial from venous irrigations is still an open problem. Indeed, automatic separation of arterial and venous trees has been considered during last years as one of the main future challenges in the field. Artery-Vein (AV) segmentation would be useful in different medical scenarios and multiple pulmonary diseases or pathological states, allowing the study of arterial and venous irrigations separately. Features such as density, geometry, topology and size of vessels could be analyzed in diseases that imply vasculature remodeling, making even possible the discovery of new specific biomarkers that remain hidden nowadays. Differentiation between arteries and veins could also enhance or improve methods processing pulmonary structures. Nevertheless, AV segmentation has been unfeasible until now in clinical routine despite its objective usefulness. The huge complexity of pulmonary vascular trees makes a manual segmentation of both structures unfeasible in realistic time, encouraging the design of automatic or semiautomatic tools to perform the task. However, this lack of proper labeled cases seriously limits in the development of AV segmentation systems, where reference standards are necessary in both algorithm training and validation stages. For that reason, the design of synthetic CT images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image (including arteries and veins) is differentiated unequivocally. In this Ph.D. Thesis we address both interrelated problems. First, the design of a complete framework to automatically generate computational CT phantoms of the human lung is described. Starting from biological and imagebased knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. These synthetic cases, together with labeled real CT datasets, have been used as reference for the development of a fully automatic pulmonary AV segmentation/separation method. The approach comprises a vessel extraction stage using scale-space particles and their posterior artery-vein classification using Graph-Cuts (GC) based on arterial/venous similarity scores obtained with a Machine Learning (ML) pre-classification step and particle connectivity information. Validation of pulmonary phantoms from visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems, show good correspondence between real and synthetic lungs. The evaluation of the Artery-Vein (AV) segmentation algorithm, based on different strategies to assess the accuracy of vessel particles classification, reveal accurate differentiation between arteries and vein in both real and synthetic cases that open a huge range of possibilities in the clinical study of cardiopulmonary diseases and the development of methodological approaches for the analysis of pulmonary images.

Congenital cystic adenomatoid malformation of the lung: indications and timing of surgery

We review a single surgeon and surgical centre's experience with congenital cystic adenomatoid malformation of the lung (CCAML) in relation to clinical spectrum, operative experience, and postoperative course. A retrospective hospital record review was done on surgically treated cases of CCAML over a 10-year period, focusing on number with antenatal diagnosis, spectrum of postnatal presentation, type of surgery performed, and outcome. Forty-seven patients from birth to 14 years of age underwent surgery for CCAML. Antenatal diagnosis (ante) was made in 30 cases. Of these, 10 became symptomatic before surgery. Six of the 17 postnatally-diagnosed (pnd) cases were an asymptomatic incidental finding. Overall, 16 were symptomatic in the 1st year of life, and five were symptomatic beyond 1 year of age. Symptoms varied from respiratory distress (seven ante, six pnd) to chronic cough (three, and recurrent chest infection (three ante, two pnd). All preoperative diagnoses were confirmed with chest CT. Most patients (25) were operated on before 3 months of age. Eleven were operated on in the first 2 weeks of life as emergency surgery for respiratory distress. The most common lobe involved was the right upper lobe (16), and lobectomy was performed in 42 cases, segmentectomy in four, and pneumonectomy in one. Seventeen cases were extubated immediately postoperatively; 29 required postoperative ventilation overnight, and nine needed more prolonged ventilation. Early postoperative complications included pneumothorax (two), pleural effusion (one), and chylous effusion (one). Late complications included recurrence in three cases (all segmentectomy), who then subsequently underwent lobectomy. There was one death from respiratory failure. Because there is an increasing trend in the detection of asymptomatic antenatally-diagnosed CCAML, consideration of early surgical excision to prevent complications is suggested by our series. CT scanning is mandatory for postnatal evaluation because chest x-ray could be normal. Safe elective excision after 3 months is supported by our low morbidity and less need for postoperative ventilation. Lobectomy is the procedure of choice to prevent recurrence.

Motion correction algorithm of lung tumors for respiratory gated PET images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

Motion Correction Algorithm of Lung Tumors for Respiratory Gated PET Images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

Metastatic meningioma: positron emission tomography CT imaging findings

The imaging findings of a case of metastasing meningioma are described. The case illustrates a number of rare and interesting features. The patient presented with haemoptysis 22 years after the initial resection of an intracranial meningioma. CT demonstrated heterogeneous masses with avid peripheral enhancement without central enhancement. Blood supply to the larger lesion was partially from small feeding vessels from the inferior pulmonary vein. These findings correlate with a previously published case in which there was avid uptake of fluoro-18-deoxyglucose peripherally with lesser uptake centrally. The diagnosis of metastasing meningioma was confirmed on percutaneous lung tissue biopsy.

Structural and Functional Lung Impairment in Adult Survivors of Bronchopulmonary Dysplasia

RATIONALE: As more preterm infants recover from severe bronchopulmonary dysplasia (BPD), it is critical to understand the clinical consequences of this condition on the lung health of adult survivors.

OBJECTIVES: To assess structural and functional lung parameters in young adult BPD survivors and preterm and term controls Methods: Young adult survivors of BPD (mean age 24) underwent spirometry, lung volumes, transfer factor, lung clearance index and fractional exhaled nitric oxide measurements together with high-resolution chest tomographic (CT) imaging and cardiopulmonary exercise testing.

MEASUREMENTS AND MAIN RESULTS: 25 adult BPD survivors, (mean ± SD gestational age 26.8 ± 2.3 weeks; birth weight 866 ± 255 g), 24 adult prematurely born non-BPD controls (gestational age 30.6 ± 1.9 weeks; birth weight 1234 ± 207 g) and 25 adult term birth control subjects (gestational age 38.5 ± 0.9 weeks; and birth weight 3569 ± 2979 g) were studied. BPD subjects were more likely to be wakened by cough (OR 9.7, 95% CI: 1.8 to 52.6), p<0.01), wheeze and breathlessness (OR 12.2, 95%CI: 1.3 to 112), p<0.05) than term controls after adjusting for sex and current smoking. Preterm subjects had greater airways obstruction than term subjects. BPD subjects had significantly lower values for FEV1 and FEF25-75 (% predicted and z scores) than term controls (both p<0.001). Although non-BPD subjects also had lower spirometric values than term controls, none of the differences reached statistical significance. More BPD subjects (25%) had fixed airflow obstruction than non-BPD (12.5%) and term (0%) subjects (p=0.004). Both BPD and non-BPD subjects had significantly greater impairment in gas transfer (KCO % predicted) than term subjects (both p<0.05). Eighteen (37%) preterm participants were classified as small for gestational age (birth weight < 10th percentile for gestational age). These subjects had significantly greater impairment in FEV1 (% predicted and z scores) than those born appropriate for gestational age. BPD survivors had significantly more severe radiographic structural lung impairment than non-BPD subjects. Both preterm groups had impaired exercise capacity compared to term controls. There was a trend for greater limitation and leg discomfort in BPD survivors.

CONCLUSIONS: Adult preterm birth survivors, especially those who developed BPD, continue to experience respiratory symptoms and exhibit clinically important levels of pulmonary impairment.

PET e PET/CT com FDG: comparação de duas bases de dados de referência do normal com SUV

Mestrado em Medicina Nuclear - Área de especialização: Tomografia por Emissão de Positrões

ESR/ERS white paper on lung cancer screening

Lung cancer is the most frequently fatal cancer, with poor survival once the disease is advanced. Annual low-dose computed tomography has shown a survival benefit in screening individuals at high risk for lung cancer. Based on the available evidence, the European Society of Radiology and the European Respiratory Society recommend lung cancer screening in comprehensive, quality-assured, longitudinal programmes within a clinical trial or in routine clinical practice at certified multidisciplinary medical centres. Minimum requirements include: standardised operating procedures for low-dose image acquisition, computer-assisted nodule evaluation, and positive screening results and their management; inclusion/exclusion criteria; expectation management; and smoking cessation programmes. Further refinements are recommended to increase quality, outcome and cost-effectiveness of lung cancer screening: inclusion of risk models, reduction of effective radiation dose, computer-assisted volumetric measurements and assessment of comorbidities (chronic obstructive pulmonary disease and vascular calcification). All these requirements should be adjusted to the regional infrastructure and healthcare system, in order to exactly define eligibility using a risk model, nodule management and a quality assurance plan. The establishment of a central registry, including a biobank and an image bank, and preferably on a European level, is strongly encouraged. Key points: • Lung cancer screening using low dose computed tomography reduces mortality. • Leading US medical societies recommend large scale screening for high-risk individuals. • There are no lung cancer screening recommendations or reimbursed screening programmes in Europe as of yet. • The European Society of Radiology and the European Respiratory Society recommend lung cancer screening within a clinical trial or in routine clinical practice at certified multidisciplinary medical centres. • High risk, eligible individuals should be enrolled in comprehensive, quality-controlled longitudinal programmes.