Differential Antitumor Effects of IgG and IgM Monoclonal Antibodies and Their Synthetic Complementarity-Determining Regions Directed to New Targets of B16F10-Nex2 Melanoma Cells

Malignant melanoma has increased incidence worldwide and causes most skin cancer-related deaths. A few cell surface antigens that can be targets of antitumor immunotherapy have been characterized in melanoma. This is an expanding field because of the ineffectiveness of conventional cancer therapy for the metastatic form of melanoma. In the present work, antimelanoma monoclonal antibodies (mAbs) were raised against B16F10 cells (subclone Nex4, grown in murine serum), with novel specificities and antitumor effects in vitro and in vivo. MAb A4 (IgG2ak) recognizes a surface antigen on B16F10-Nex2 cells identified as protocadherin beta(13). It is cytotoxic in vitro and in vivo to B16F10-Nex2 cells as well as in vitro to human melanoma cell lines. MAb A4M (IgM) strongly reacted with nuclei of permeabilized murine tumor cells, recognizing histone 1. Although it is not cytotoxic in vitro, similarly with mAb A4, mAb A4M significantly reduced the number of lung nodules in mice challenged intravenously with B16F10-Nex2 cells. The V(H) CDR3 peptide from mAb A4 and V(L) CDR1 and CDR2 from mAb A4M showed significant cytotoxic activities in vitro, leading tumor cells to apoptosis. A cyclic peptide representing A4 CDR H3 competed with mAb A4 for binding to melanoma cells. MAb A4M CDRs L1 and L2 in addition to the antitumor effect also inhibited angiogenesis of human umbilical vein endothelial cells in vitro. As shown in the present work, mAbs A4 and A4M and selected CDR peptides are strong candidates to be developed as drugs for antitumor therapy for invasive melanoma.

Cancer detective

The artile describes development of an automated system for detection of lung nodules in CT images.

Pulmonary nodule classification aided by clustering

Lung nodules can be detected through examining CT scans. An automated lung nodule classification system is presented in this paper. The system employs random forests as it base classifier. A unique architecture for classification-aided-by-clustering is presented. Four experiments are conducted to study the performance of the developed system. 5721 CT lung image slices from the LIDC database are employed in the experiments. According to the experimental results, the highest sensitivity of 97.92%, and specificty of 96.28% are achieved by the system. The results demonstrate that the system has improved the performances of its tested counterparts.

Is Acute Fibrinous and Organizing Pneumonia the Expression of Immune Dysregulation?

INTRODUCTION: Acute fibrinous and organizing pneumonia (AFOP) is a recently described histologic pattern of diffuse pulmonary disease. In children, all cases reported to date have been fatal. In this study, we describe the first nonfatal AFOP in a child and review the literature. DESCRIPTION: A 10-year-old boy developed very severe aplastic anemia (VSAA) after being admitted to our hospital with a fulminant hepatic failure of unknown origin. A chest computed tomography scan revealed multiple lung nodules and a biopsy of a pulmonary lesion showed all the signs of AFOP. Infectious workup remained negative. We started immunosuppressive therapy with antithymocyte globulin and cyclosporine to treat VSAA. Subsequent chest computed tomography scans showed a considerable diminution of the lung lesions but the VSAA did not improve until we performed hematopoietic stem cell transplantation 5 months later. CONCLUSIONS: Aplastic anemia is associated with a variety of autoimmune syndromes. The sequence of events in our patient suggests that the hepatic failure, AFOP, and the VSAA may all have been part of an autoimmune syndrome. AFOP could be the result of immune dysregulation in this pediatric case with favorable outcome after immunosuppressive therapy and hematopoietic stem cell transplantation.

Immune response to regressor and progressor tumor variants

Most skin cancers induced in mice by Ultraviolet (UV) radiation express highly immunogenic Tumor specific transplantation antigens (TSTAs) and thus exhibit a regressor phenotype. In this study, I have used cloned genes encoding tumor antigens and oncogenes in conjunction with DNA transfection technique to isolate and characterize regressor variants from progressor tumors and vice versa. The purpose of this study was (1) to determine whether the product of a cloned gene (216) from UV-1591 tumor, which encodes a novel MHC class I antigen can function as a tumor rejection antigen when expressed on unrelated, nonantigenic, murine tumor cells or whether its function is restricted to UV-induced tumors, and (2) to determine the processes by which progressor variants derived from a regressor UV-2240 cell line by transfection with an activated Ha-ras oncogene escape the immune defenses of the normal immunocompetent host.^ To answer the first question, a spontaneously transformed, nonimmunogenic cell line (10T-1) was cotransfected with DNA from p216 and pSV2-neo plasmids. Results demonstrate that the product of a cloned TSTA gene from a UV-induced murine tumor is capable of functioning as a tumor rejection antigen when expressed on unrelated, nonantigenic tumor cells. In addition, these results indicate that this approach could be used to augment the immune response against poorly antigenic tumors.^ To answer the second question, progressor variants were isolated from a highly antigenic UV radiation-induced C3H mouse regressor fibrosarcoma cell line, UV-2240, by transfection with an activated Ha-ras oncogene. Subcutaneous injection of Ha-ras-transfected UV-2240 cells into immunocompetent C3H mice produced tumors in 4 of 36 animals. In addition, the Ha-ras-induced progressor variants produced experimental lung metastasis in both normal C3H and nude mice, although they induced more lung nodules in nude mice than in normal C3H mice. Results indicate that the progressor phenotype of the Ha-ras-induced tumor variants is not due to loss of TSTAs or MHC class I antigens. This implies that some tumors can escape the immune defenses of the normal immunocompetent host by mechanisms other than the loss of TSTAs and MHC class I antigens. (Abstract shortened with permission of author.) ^

LCD-OpenPACS: sistema integrado de telerradiologia com auxílio ao diagnóstico de nódulos pulmonares em exames de tomografia computadorizada

Lung cancer is one of the most common types of cancer and has the highest mortality rate. Patient survival is highly correlated with early detection. Computed Tomography technology services the early detection of lung cancer tremendously by offering aminimally invasive medical diagnostic tool. However, the large amount of data per examination makes the interpretation difficult. This leads to omission of nodules by human radiologist. This thesis presents a development of a computer-aided diagnosis system (CADe) tool for the detection of lung nodules in Computed Tomography study. The system, called LCD-OpenPACS (Lung Cancer Detection - OpenPACS) should be integrated into the OpenPACS system and have all the requirements for use in the workflow of health facilities belonging to the SUS (Brazilian health system). The LCD-OpenPACS made use of image processing techniques (Region Growing and Watershed), feature extraction (Histogram of Gradient Oriented), dimensionality reduction (Principal Component Analysis) and classifier (Support Vector Machine). System was tested on 220 cases, totaling 296 pulmonary nodules, with sensitivity of 94.4% and 7.04 false positives per case. The total time for processing was approximately 10 minutes per case. The system has detected pulmonary nodules (solitary, juxtavascular, ground-glass opacity and juxtapleural) between 3 mm and 30 mm.

Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Um caso pediátrico de tuberculose miliar: achados raros e evolução atípica

Introdução: A tuberculose miliar resulta da disseminação linfohematogénica do Mycobacterium tuberculosis, sendo uma manifestação grave da infeção. Caso clínico: Criança de 9 anos, género feminino, com his¬tória de febre prolongada. O diagnóstico de tuberculose miliar foi colocado após telerradiografia torácica com infiltrado reticu¬lonodular difuso bilateral, e corroborado pelo achado de tubér¬culos coroideus no olho direito e visualização de bacilos álcool¬ ¬ácido resistentes em amostra de suco gástrico. Detetaram¬-se tuberculomas cerebrais na ressonância magnética. Isolou¬se Mycobacterium tuberculosis multissensível em amostras de suco gástrico. Após mais de 40 dias de tratamento, persistia a febre e baciloscopia positiva. Foi excluída infeção pelo vírus da imunodeficiência humana. Não foram detetadas complicações. Posteriormente, a evolução clínica foi favorável. Discussão/Conclusão: A tuberculose mantém-¬se um diag¬nóstico relevante na criança com febre prolongada. A associa¬ção da imagem torácica, baciloscopias positivas e tubérculos coroideus foram fundamentais para a celeridade do diagnóstico e implementação do tratamento. Reforça¬-se a importância de manter elevado índice de suspeição para uma patologia que tem tratamento.

Automatic detection of lung nodule from computer tomography images

Lung nodule refers to lung tissue abnormalities that may become cancerous. An automated system that detects nodules of common sizes within lung images is developed. It consists of acquisition, pre-processing, background removal, nodule detection, and false positives reduction. The system can assist expert radiologists in their decision making.

Random forest based lung nodule classification aided by clustering

An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) A_z of 0.9786 has been achieved.

The influence of physical activity in the progression of experimental lung cancer in mice

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Brain and lung cryptococcoma and concurrent corynebacterium pseudotuberculosis infection in a goat: a case report

A four-year-old male goat with a history of neurological disorder was euthanized. It presented uncommon nodules in the brain and lungs associated with multiple abscesses, predominantly in the spleen and liver. Histological examination of brain and lung sections revealed yeast forms confirmed to be Cryptococcus gattii after a combination of isolation and polymerase chain reaction (PCR) procedures. Moreover, Corynebacterium pseudotuberculosis infection was diagnosed by PCR of samples from the lung, spleen and liver. The present report highlights the rare concurrent infection of C. gatti and C. pseudotuberculosis in an adult goat from São Paulo state, Brazil, and indicates the necessity of surveillance in the treatment of goats with atypical pulmonary infections associated with neurological disorders.

Toward an early diagnosis of lung cancer: an autoantibody signature for squamous cell lung carcinoma

Serum-based diagnosis offers the prospect of early lung carcinoma detection and of differentiation between benign and malignant nodules identified by CT. One major challenge toward a future blood-based diagnostic consists in showing that seroreactivity patterns allow for discriminating lung cancer patients not only from normal controls but also from patients with non-tumor lung pathologies. We addressed this question for squamous cell lung cancer, one of the most common lung tumor types. Using a panel of 82 phage-peptide clones, which express potential autoantigens, we performed serological spot assay. We screened 108 sera, including 39 sera from squamous cell lung cancer patients, 29 sera from patients with other non-tumor lung pathologies, and 40 sera from volunteers without known disease. To classify the serum groups, we employed the standard Naïve Bayesian method combined with a subset selection approach. We were able to separate squamous cell lung carcinoma and normal sera with an accuracy of 93%. Low-grade squamous cell lung carcinoma were separated from normal sera with an accuracy of 92.9%. We were able to distinguish squamous cell lung carcinoma from non-tumor lung pathologies with an accuracy of 83%. Three phage-peptide clones with sequence homology to ROCK1, PRKCB1 and KIAA0376 reacted with more than 15% of the cancer sera, but neither with normal nor with non-tumor lung pathology sera. Our study demonstrates that seroreactivity profiles combined with statistical classification methods have great potential for discriminating patients with squamous cell lung carcinoma not only from normal controls but also from patients with non-tumor lung pathologies.

Geochemistry on manganese nodules from Timor

Fossil Mn nodules of Cretaceous age from western Timor exhibit chemical, structural and radioisotope compositions consistent with their being of deep-sea origin. These nodules show characteristics similar to nodules now found at depths of 3,500-5,000 m in the Pacific and Indian Oceans. Slight differences in the fine structure and chemistry of these nodules and modern deep-sea nodules are attributed to diagenetic alteration after uplift of enclosing sediments.