852 research outputs found

    Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements using Radiomics

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    Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics. © 2019 Wolters Kluwer Health, Inc. All rights reserved

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

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    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

    Computed Tomography Imaging of Primary Lung Cancer in Mice Using a Liposomal-Iodinated Contrast Agent

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    To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT) imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.Primary lung cancers with mutations in K-ras alone (Kras(LA1)) or in combination with p53 (LSL-Kras(G12D);p53(FL/FL)) were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose of 16 µl/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at 0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.A good correlation was seen between volume and diameter-based assessment of nodules (R(2)>0.8) for both lung cancer models. The LSL-Kras(G12D);p53(FL/FL) model showed rapid growth as demonstrated by systemically higher volume changes compared to the lung nodules in Kras(LA1) mice (p<0.05). Early phase imaging using the nanoparticle contrast agent enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal enhancement in the lung nodules of LSL-Kras(G12D);p53(FL/FL) mice compared to nodules in Kras(LA1) mice (p<0.05) indicating higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as have implications on treatment response and monitoring

    Focal Spot, Spring 2004

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    https://digitalcommons.wustl.edu/focal_spot_archives/1096/thumbnail.jp

    Development and application in clinical practice of Computer-aided Diagnosis systems for the early detection of lung cancer

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    Lung cancer is the main cause of cancer-related deaths both in Europe and United States, because often it is diagnosed at late stages of the disease, when the survival rate is very low if compared to first asymptomatic stage. Lung cancer screening using annual low-dose Computed Tomography (CT) reduces lung cancer 5-year mortality by about 20% in comparison to annual screening with chest radiography. However, the detection of pulmonary nodules in low-dose chest CT scans is a very difficult task for radiologists, because of the large number (300/500) of slices to be analyzed. In order to support radiologists, researchers have developed Computer aided Detection (CAD) algorithms for the automated detection of pulmonary nodules in chest CT scans. Despite proved benefits of those systems on the radiologists detection sensitivity, the usage of CADs in clinical practice has not spread yet. The main objective of this thesis is to investigate and tackle the issues underlying this inconsistency. In particular, in Chapter 2 we introduce M5L, a fully automated Web and Cloud-based CAD for the automated detection of pulmonary nodules in chest CT scans. This system introduces a new paradigm in clinical practice, by making available CAD systems without requiring to radiologists any additional software and hardware installation. The proposed solution provides an innovative cost-effective approach for clinical structures. In Chapter 3 we present our international challenge aiming at a large-scale validation of state-of-the-art CAD systems. We also investigate and prove how the combination of different CAD systems reaches performances much higher than any best stand-alone system developed so far. Our results open the possibility to introduce in clinical practice very high-performing CAD systems, which miss a tiny fraction of clinically relevant nodules. Finally, we tested the performance of M5L on clinical data-sets. In chapter 4 we present the results of its clinical validation, which prove the positive impact of CAD as second reader in the diagnosis of pulmonary metastases on oncological patients with extra-thoracic cancers. The proposed approaches have the potential to exploit at best the features of different algorithms, developed independently, for any possible clinical application, setting a collaborative environment for algorithm comparison, combination, clinical validation and, if all of the above were successful, clinical practice

    Development, Implementation and Pre-clinical Evaluation of Medical Image Computing Tools in Support of Computer-aided Diagnosis: Respiratory, Orthopedic and Cardiac Applications

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    Over the last decade, image processing tools have become crucial components of all clinical and research efforts involving medical imaging and associated applications. The imaging data available to the radiologists continue to increase their workload, raising the need for efficient identification and visualization of the required image data necessary for clinical assessment. Computer-aided diagnosis (CAD) in medical imaging has evolved in response to the need for techniques that can assist the radiologists to increase throughput while reducing human error and bias without compromising the outcome of the screening, diagnosis or disease assessment. More intelligent, but simple, consistent and less time-consuming methods will become more widespread, reducing user variability, while also revealing information in a more clear, visual way. Several routine image processing approaches, including localization, segmentation, registration, and fusion, are critical for enhancing and enabling the development of CAD techniques. However, changes in clinical workflow require significant adjustments and re-training and, despite the efforts of the academic research community to develop state-of-the-art algorithms and high-performance techniques, their footprint often hampers their clinical use. Currently, the main challenge seems to not be the lack of tools and techniques for medical image processing, analysis, and computing, but rather the lack of clinically feasible solutions that leverage the already developed and existing tools and techniques, as well as a demonstration of the potential clinical impact of such tools. Recently, more and more efforts have been dedicated to devising new algorithms for localization, segmentation or registration, while their potential and much intended clinical use and their actual utility is dwarfed by the scientific, algorithmic and developmental novelty that only result in incremental improvements over already algorithms. In this thesis, we propose and demonstrate the implementation and evaluation of several different methodological guidelines that ensure the development of image processing tools --- localization, segmentation and registration --- and illustrate their use across several medical imaging modalities --- X-ray, computed tomography, ultrasound and magnetic resonance imaging --- and several clinical applications: Lung CT image registration in support for assessment of pulmonary nodule growth rate and disease progression from thoracic CT images. Automated reconstruction of standing X-ray panoramas from multi-sector X-ray images for assessment of long limb mechanical axis and knee misalignment. Left and right ventricle localization, segmentation, reconstruction, ejection fraction measurement from cine cardiac MRI or multi-plane trans-esophageal ultrasound images for cardiac function assessment. When devising and evaluating our developed tools, we use clinical patient data to illustrate the inherent clinical challenges associated with highly variable imaging data that need to be addressed before potential pre-clinical validation and implementation. In an effort to provide plausible solutions to the selected applications, the proposed methodological guidelines ensure the development of image processing tools that help achieve sufficiently reliable solutions that not only have the potential to address the clinical needs, but are sufficiently streamlined to be potentially translated into eventual clinical tools provided proper implementation. G1: Reducing the number of degrees of freedom (DOF) of the designed tool, with a plausible example being avoiding the use of inefficient non-rigid image registration methods. This guideline addresses the risk of artificial deformation during registration and it clearly aims at reducing complexity and the number of degrees of freedom. G2: The use of shape-based features to most efficiently represent the image content, either by using edges instead of or in addition to intensities and motion, where useful. Edges capture the most useful information in the image and can be used to identify the most important image features. As a result, this guideline ensures a more robust performance when key image information is missing. G3: Efficient method of implementation. This guideline focuses on efficiency in terms of the minimum number of steps required and avoiding the recalculation of terms that only need to be calculated once in an iterative process. An efficient implementation leads to reduced computational effort and improved performance. G4: Commence the workflow by establishing an optimized initialization and gradually converge toward the final acceptable result. This guideline aims to ensure reasonable outcomes in consistent ways and it avoids convergence to local minima, while gradually ensuring convergence to the global minimum solution. These guidelines lead to the development of interactive, semi-automated or fully-automated approaches that still enable the clinicians to perform final refinements, while they reduce the overall inter- and intra-observer variability, reduce ambiguity, increase accuracy and precision, and have the potential to yield mechanisms that will aid with providing an overall more consistent diagnosis in a timely fashion

    Innovations in thoracic imaging:CT, radiomics, AI and x-ray velocimetry

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    In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation

    REVIEW ON LUNG CANCER DETECTION USING IMAGE PROCESSING TECHNIQUE

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    This paper presents a review on the lung cancer detection method using image processing. In recent years the image processing mechanisms are used widely in several medical areas for improving earlier detection and treatment stages.Also the different procedures and design methodologies for the same have also been discusse

    Quantitative Evaluation of Pulmonary Emphysema Using Magnetic Resonance Imaging and x-ray Computed Tomography

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    Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality affecting at least 600 million people worldwide. The most widely used clinical measurements of lung function such as spirometry and plethysmography are generally accepted for diagnosis and monitoring of the disease. However, these tests provide only global measures of lung function and they are insensitive to early disease changes. Imaging tools that are currently available have the potential to provide regional information about lung structure and function but at present are mainly used for qualitative assessment of disease and disease progression. In this thesis, we focused on the application of quantitative measurements of lung structure derived from 1H magnetic resonance imaging (MRI) and high resolution computed tomography (CT) in subjects diagnosed with COPD by a physician. Our results showed that significant and moderately strong relationship exists between 1H signal intensity (SI) and 3He apparent diffusion coefficient (ADC), as well as between 1H SI and CT measurements of emphysema. This suggests that these imaging methods may be quantifying the same tissue changes in COPD, and that pulmonary 1H SI may be used effectively to monitor emphysema as a complement to CT and noble gas MRI. Additionally, our results showed that objective multi-threshold analysis of CT images for emphysema scoring that takes into account the frequency distribution of each Hounsfield unit (HU) threshold was effective in correctly classifying the patient into COPD and healthy subgroups. Finally, we found a significant correlation between whole lung average subjective and objective emphysema scores with high inter-observer agreement. It is concluded that 1H MRI and high resolution CT can be used to quantitatively evaluate lung tissue alterations in COPD subjects
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