Lung nodules: size still matters

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules

Automatic 3D pulmonary nodule detection in CT images: a survey

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks

Impact of extracardiac findings during cardiac MR on patient management and outcome.

Cardiac magnetic resonance (CMR) is increasingly used to assess heart diseases. Relevant non-cardiac diseases may also be incidentally found on CMR images. The aim of this study was to determine the prevalence and nature of incidental extra-cardiac findings (IEF) and their clinical impact in non-selected patients referred for CMR. MR images of 762 consecutive patients (515 men, age: 56±18 years) referred for CMR were prospectively interpreted by 2 radiologists blinded for any previous imaging study. IEFs were classified as major when requiring treatment, follow-up, or further investigation. Clinical follow-up was performed by checking hospital information records and by calling referring physicians. The 2 endpoints were: 1) non-cardiac death and new treatment related to major IEFs, and 2) hospitalization related to major IEFs during follow-up. Major IEFs were proven in 129 patients (18.6% of the study population), 14% of those being unknown before CMR. During 15±6 month follow-up, treatment of confirmed major IEFs was initiated in 1.4%, and no non-cardiac deaths occurred. Hospitalization occurred in 8 patients (1.0% of the study population) with confirmed major IEFs and none occurred in the remaining 110 patients with unconfirmed/unexplored major IEFs (p<0.001). Screening for major IEFs in a population referred for routine CMR changed management in 1.4% of patients. Major IEFs unknown before CMR but without further exploration, however, carried a favorable prognosis over a follow-up period of 15 months

Massive training artificial immune recognition system for lung nodules detection

In the early detection and diagnosis of lung nodule, computer aided detection (CAD) has become crucial to assist radiologists in interpreting medical images and decision making. However, some limitations have been found in the existing CAD algorithms for detecting lung nodules, such as imprecision classification due to inaccurate segmentation and lengthy computation time. In this research, Massive Training Artificial Immune Recognition System (MTAIRS) is proposed to detect lung nodules on Computed Tomography (CT) scans. MTAIRS is developed based on the pixel machine learning and artificial immune-based system-Artificial Immune Recognition System (AIRS). Two versions of proposed algorithms have been investigated in the study: MTAIRS 1 and MTAIRS 2. Since segmentation and feature calculation are not implemented in the pixel-based machine learning, the loss of information can be avoided during the data training in MTAIRS 1 and MTAIRS 2. The experiment and analysis find that MTAIRS 1 and MTAIRS 2 have successfully reduced the computation time and accomplished good accuracy in the detection of lung nodules on CT scans compared to other well-known pixel-based classification algorithms. Furthermore, MTAIRS 1 and MTAIRS 2 are investigated to improve their performance in eliminating the false positives. A weighted non-linear affinity function is employed in the training of MTAIRS 1 and MTAIRS 2 to replace Euclidean distance in affinity measurement. The enhanced algorithms named, E-MTAIRS 1 and E-MTAIRS 2 are capable to reduce the false positives in the non-nodule classification while maintaining the accuracy in nodule detection. In order to further provide comparative analysis of pixel-based classification algorithms in lung nodules detection, a pixel-based evaluation method of Kullback Leibler (KL) divergence is proposed in this study. Based on the pixel-based quantitative analysis, MTAIRS 1 performs better in the elimination of false positives, while MTAIRS 2 in lung nodules detection. The average detection accuracy for both MTAIRS algorithms is 95%

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Maladies péritonéales : place et apport de l'imagerie par résonance magnétique

Magnetic resonance imaging (MRI) allows news approaches to improve diagnostic and therapeutic issues related to peritoneal diseases. This technique requires using dedicated protocols and has a learning curve. After a literature review on peritoneal imaging highlighting the role of MRI and its potential underutilization, the purpose of this work was to study the contribution of MRI in two models of diffuse peritoneal diseases. The first model concerned endometriosis. Using dedicated protocols, studies on digestive and diaphragmatic involvements reported high performance for providing a useful mapping of lesions for both diagnosis and surgical planning. The second model concerned peritoneal carcinomatosis. The purpose was to assess the contribution of MRI in selecting patients for curative surgery. The first study, performed in a large cohort, reported a very low impact of the different imaging techniques in the selection of non-resectable patients. Using a new approach combining MRI and computed tomography (CT), the second study demonstrated a substantial improvement in quantitative lesion assessment, although remaining sub optimal. With a qualitative approach evaluating signs of non-resectability, the third study showed MRI had better sensitivity than CT for the detection of non-resecable small bowel involvements in pseudomyxoma peritonei. MRI, thanks to its high contrast resolution, provides unique information. Used as reference technique or in addition to other techniques, MRI optimizes patient managementDe nouvelles approches sont possibles en imagerie par résonance magnétique (IRM) pour répondre aux principaux enjeux diagnostiques et thérapeutiques liés aux maladies péritonéales. Cette technique implique l'utilisation de protocoles dédiés et une courbe d'apprentissage. Après une revue de la littérature sur l'imagerie péritonéale mettant en perspective la place de l'IRM et sa potentielle sous utilisation, l'objectif de ce travail a été d'étudier l'apport de cette technique dans deux modèles de maladies péritonéales diffuses. Le premier modèle concernait l'endométriose. En utilisant des protocoles adaptés, les études sur les atteintes digestives et diaphragmatiques ont démontré qu'une cartographie lésionnelle utile au diagnostic et à la prise en charge chirurgicale gynécologique pouvait être obtenue avec de hauts niveaux de performance. Le second modèle concernait la carcinose. La problématique était d'évaluer l'apport de l'IRM dans la sélection des patients candidats à une chirurgie menée à visée curative. La première étude menée sur une grande cohorte a démontré un très faible impact des différentes techniques d'imagerie dans la sélection des patients non résécables. La seconde étude, proposant une nouvelle approche de la quantification des lésions en combinant l'IRM au scanner, a rapporté une amélioration relative du bilan lésionnel, bien qu'encore infra optimale. Avec une approche qualitative centrée sur la recherche de signes de non résécabilité, la troisième étude a démontré que l'IRM avait une meilleure sensibilité que le scanner pour détecter les atteintes non résécables de l'intestin grêle dans le pseudomyxome péritonéal. L'IRM, grâce à sa haute résolution en contraste, offre des informations uniques. Utilisée comme technique de référence ou en complément des autres techniques en fonction de la nature des lésions à explorer, elle permet d'optimiser la prise en charge des patient

Estudio metabólico de los nódulos pulmonares solitarios mediante PET (Tomografía de Emisión de Positrones) con 18F-FDG

Los nódulos pulmonares solitarios (NPS) son un hallazgo radiológico común, frecuentemente detectado de forma casual. Es fundamental establecer un diagnóstico lo más precoz posible de los mismos, ya que, aunque la mayoría son de origen benigno pueden representar el estadio inicial de un cáncer de pulmón. La Tomografía por Emisión de Positrones (PET) con 18F-FDG es una técnica de diagnóstico que ayuda en la diferenciación de los nódulos pulmonares benignos con respecto a los malignos. Este trabajo tiene como objetivo analizar nuestra experiencia en la caracterización de los NPS de origen indeterminado mediante el estudio FDG-PET, evaluar el comportamiento metabólico de estas lesiones a través del valor SUVmáx estudiando una posible correlación con otros parámetros clínico-radiológicos, así como determinar la utilidad diagnóstica de esta técnica en nuestro medio definiendo, además, las posibles causas de falsos positivos y negativos de la prueba

Optimización paramétrica de técnicas avanzadas de segmentación de imágenes biomédicas : una contextualización a la patología

[Resumen] El cáncer de pulmón produce una alta mortandad susceptible de reducirse en un 20% al detectar en los primeros estadios posibles nódulos cancerígenos utilizando CT de tórax. Si los algoritmos de procesado de las tomografías no están ajustados para la detección de nódulos se pueden perder un 17% de ellos. Se busca una parametrización óptima de los algoritmos de segmentación, basados en clusterización difusa, para delimitar píxeles de la imagen constituyentes de un posible nódulo pulmonar. Sobre un conjunto prototipo de imágenes del LIDC se han realizado segmentaciones con diversas combinaciones de precondiciones en los algoritmos, así como definido y utilizado índices de rendimiento basados en métodos de validación empírica por discrepancia que sometidos a una estadística descriptiva permiten comparar y obtener los valores de parámetros de entrada adecuados al problema. Demostrándose que fundamentalmente el tipo de densidad del Nódulo Pulmonar Solitario contenido en las imágenes junto con el parámetro de los algoritmos, número de clústeres, son los factores más influyentes en el rendimiento de todos los métodos empleados. Siendo óptimo segmentar entre ocho y diez clases aquellas imágenes con nódulo de densidad deslustrada y en tres clases todas las demás, para tener en un único clúster al nódulo cancerígeno.[Resumo] O cancro de pulmón produce unha alta mortaldade susceptible de diminuírse nun 20% ao detectar nos primeiros estadios posibles nodosidades canceríxenas empregando CT de peito. Se os algoritmos de procesado das tomografías non están axustados para a detección de nodosidades pódense perder un 17% delas. Búscase unha parametrización óptima dos algoritmos de segmentación, baseados en clusterización difusa, para delimitar píxels da imaxe constituíntes dunha posible nodosidade pulmonar. Sobre un mesmo conxunto prototipo de imaxes do LIDC realizáronse segmentacións con diversas combinacións de precondicións nos algoritmos, e definíronse e empregáronse índices de rendemento baseados en métodos de validación empírica por diverxencia que sometidos a unha estatística descritiva permiten comparar e obter os valores de parámetros de entrada axeitados ao problema. Demostrándose que fundamentalmente o tipo de densidade da Nodosidade Pulmonar Senlleira contida nas imaxes xunto co parámetro dos algoritmos, número de clústers, son os factores máis influentes no rendemento de todos os métodos empregados. Sendo óptimo segmentar entre oito e dez clases aquelas imaxes con nodosidade de densidade deslustrada e en tres clases todas as demais para ter nun único clúster a nodosidade canceríxena.[Abstract] Lung cancer produces high mortality susceptible of being reduced by 20% detecting carcinogenic nodules in the early stages using chest CT. If processing algorithms are unadjusted scans for detecting nodules may lose 17% of them. We are looking for optimal parameterization of segmentation algorithms based on fuzzy clustering is, to delimit the constituent pixel image of a possible pulmonary nodule. On the same set of prototype images of LIDC we have realised segmentations with various combinations of preconditions of algorithms, and defined and performance measures used methods based on empirical validation discrepancy subjected to descriptive statistics allow you to compare and get the suitable parameter values input to the problem. Showing that essentially the kind of solitary pulmonary nodule density written in the picture along with the parameter algorithms, number of clusters, are the most influential in the performance of all methods factors. Being optimal segment between eight and ten classes those images with node density tarnished and all other three classes to take to a single cluster node carcinogen