Chronic Obstructive Pulmonary Disease Quantification Using CT Texture Analysis and Densitometry: Results From the Danish Lung Cancer Screening Trial.
Sørensen Lauge,Nielsen Mads,Petersen Jens,Pedersen Jesper H,Dirksen Asger,de Bruijne Marleen
AJR. American journal of roentgenology
The purpose of this study is to establish whether texture analysis and densitometry are complementary quantitative measures of chronic obstructive pulmonary disease (COPD) in a lung cancer screening setting. This was a retrospective study of data collected prospectively (in 2004-2010) in the Danish Lung Cancer Screening Trial. The texture score, relative area of emphysema, and percentile density were computed for 1915 baseline low-dose lung CT scans and were evaluated, both individually and in combination, for associations with lung function (i.e., forced expiratory volume in 1 second as a percentage of predicted normal [FEV% predicted]), diagnosis of mild to severe COPD, and prediction of a rapid decline in lung function. Multivariate linear regression models with lung function as the outcome were compared using the likelihood ratio test or the Vuong test, and AUC values for diagnostic and prognostic capabilities were compared using the DeLong test. Texture showed a significantly stronger association with lung function ( < 0.001 vs densitometric measures), a significantly higher diagnostic AUC value (for COPD, 0.696; for Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade 1, 0.648; for GOLD grade 2, 0.768; and for GOLD grade 3, 0.944; < 0.001 vs densitometric measures), and a higher but not significantly different association with lung function decline. In addition, only texture could predict a rapid decline in lung function (AUC value, 0.538; < 0.05 vs random guessing). The combination of texture and both densitometric measures strengthened the association with lung function and decline in lung function ( < 0.001 and < 0.05, respectively, vs texture) but did not improve diagnostic or prognostic performance. The present study highlights texture as a promising quantitative CT measure of COPD to use alongside, or even instead of, densitometric measures. Moreover, texture may allow early detection of COPD in subjects who undergo lung cancer screening.
Automated texture-based quantification of centrilobular nodularity and centrilobular emphysema in chest CT images.
Ginsburg Shoshana B,Lynch David A,Bowler Russell P,Schroeder Joyce D
RATIONALE AND OBJECTIVES:Characterization of smoking-related lung disease typically consists of visual assessment of chest computed tomographic (CT) images for the presence and extent of emphysema and centrilobular nodularity (CN). Quantitative analysis of emphysema and CN may improve the accuracy, reproducibility, and efficiency of chest CT scoring. The purpose of this study was to develop a fully automated texture-based system for the detection and quantification of centrilobular emphysema (CLE) and CN in chest CT images. MATERIALS AND METHODS:A novel approach was used to prepare regions of interest (ROIs) within the lung parenchyma for representation by texture features associated with the gray-level run-length and gray-level gap-length methods. These texture features were used to train a multiple logistic regression classifier to discriminate between normal lung tissue, CN or "smoker's lung," and CLE. This classifier was trained and evaluated on 24 and 71 chest CT scans, respectively. RESULTS:During training, the classifier correctly classified 89% of ROIs depicting normal lung tissue, 74% of ROIs depicting CN, and 95% of ROIs manifesting CLE. When the performance of the classifier in quantifying extent of CN and CLE was evaluated on 71 chest CT scans, 65% of ROIs in smokers without CLE were classified as CN, compared to 31% in nonsmokers (P < .001) and 28% in smokers with CLE (P < .001). CONCLUSIONS:The texture-based framework described herein facilitates successful discrimination among normal lung tissue, CN, and CLE and can be used for the automated quantification of smoking-related lung disease.
Artificial Intelligence in Diagnostic Imaging: Status Quo, Challenges, and Future Opportunities.
Sharma Puneet,Suehling Michael,Flohr Thomas,Comaniciu Dorin
Journal of thoracic imaging
In this review article, the current and future impact of artificial intelligence (AI) technologies on diagnostic imaging is discussed, with a focus on cardio-thoracic applications. The processing of imaging data is described at 4 levels of increasing complexity and wider implications. At the examination level, AI aims at improving, simplifying, and standardizing image acquisition and processing. Systems for AI-driven automatic patient iso-centering before a computed tomography (CT) scan, patient-specific adaptation of image acquisition parameters, and creation of optimized and standardized visualizations, for example, automatic rib-unfolding, are discussed. At the reading and reporting levels, AI focuses on automatic detection and characterization of features and on automatic measurements in the images. A recently introduced AI system for chest CT imaging is presented that reports specific findings such as nodules, low-attenuation parenchyma, and coronary calcifications, including automatic measurements of, for example, aortic diameters. At the prediction and prescription levels, AI focuses on risk prediction and stratification, as opposed to merely detecting, measuring, and quantifying images. An AI-based approach for individualizing radiation dose in lung stereotactic body radiotherapy is discussed. The digital twin is presented as a concept of individualized computational modeling of human physiology, with AI-based CT-fractional flow reserve modeling as a first example. Finally, at the cohort and population analysis levels, the focus of AI shifts from clinical decision-making to operational decisions.
Artificial intelligence and machine learning in respiratory medicine.
Mekov Evgeni,Miravitlles Marc,Petkov Rosen
Expert review of respiratory medicine
: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly relevant topic.: We aimed to identify and describe the studies published on the use of AI and ML in the field of respiratory diseases. The string '(((pulmonary) OR respiratory)) AND ((artificial intelligence) OR machine learning)' was used in PubMed as a search strategy. The majority of studies identified corresponded to the area of chronic obstructive pulmonary disease (COPD), in particular to COPD and chest computed tomography scans, interpretation of pulmonary function tests, exacerbations and treatment. Another field of interest is the application of AI and ML to the diagnosis of interstitial lung disease, and a few other studies were identified on the fields of mechanical ventilation, interpretation of images on chest X-ray and diagnosis of bronchial asthma.: ML may help to make clinical decisions but will not replace the physician completely. Human errors in medicine are associated with large financial losses, and many of them could be prevented with the help of AI and ML. AI is particularly useful in the absence of conclusive evidence of decision-making.
Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.
Lee Sang Min,Seo Joon Beom,Yun Jihye,Cho Young-Hoon,Vogel-Claussen Jens,Schiebler Mark L,Gefter Warren B,van Beek Edwin J R,Goo Jin Mo,Lee Kyung Soo,Hatabu Hiroto,Gee James,Kim Namkug
Journal of thoracic imaging
Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional machine learning methods, is that it can generate appropriate models for tasks directly from the raw data, removing the need for human-led feature extraction. Medical images are particularly suited for deep learning applications. Deep learning techniques have already demonstrated high performance in the detection of diabetic retinopathy on fundoscopic images and metastatic breast cancer cells on pathologic images. In radiology, deep learning has the opportunity to provide improved accuracy of image interpretation and diagnosis. Many groups are exploring the possibility of using deep learning-based applications to solve unmet clinical needs. In chest imaging, there has been a large effort to develop and apply computer-aided detection systems for the detection of lung nodules on chest radiographs and chest computed tomography. The essential limitation to computer-aided detection is an inability to learn from new information. To overcome these deficiencies, many groups have turned to deep learning approaches with promising results. In addition to nodule detection, interstitial lung disease recognition, lesion segmentation, diagnosis and patient outcomes have been addressed by deep learning approaches. The purpose of this review article was to cover the current state of the art for deep learning approaches and its limitations, and some of the potential impact on the field of radiology, with specific reference to chest imaging.
Survey on deep learning for pulmonary medical imaging.
Ma Jiechao,Song Yang,Tian Xi,Hua Yiting,Zhang Rongguo,Wu Jianlin
Frontiers of medicine
As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.
Computer analysis of computed tomography scans of the lung: a survey.
Sluimer Ingrid,Schilham Arnold,Prokop Mathias,van Ginneken Bram
IEEE transactions on medical imaging
Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.
Role of imaging in progressive-fibrosing interstitial lung diseases.
Walsh Simon L F,Devaraj Anand,Enghelmayer Juan Ignacio,Kishi Kazuma,Silva Rafael S,Patel Nina,Rossman Milton D,Valenzuela Claudia,Vancheri Carlo
European respiratory review : an official journal of the European Respiratory Society
Imaging techniques are an essential component of the diagnostic process for interstitial lung diseases (ILDs). Chest radiography is frequently the initial indicator of an ILD, and comparison of radiographs taken at different time points can show the rate of disease progression. However, radiography provides only limited specificity and sensitivity and is primarily used to rule out other diseases, such as left heart failure. High-resolution computed tomography (HRCT) is a more sensitive method and is considered central in the diagnosis of ILDs. Abnormalities observed on HRCT can help identify specific ILDs. HRCT also can be used to evaluate the patient's prognosis, while disease progression can be assessed through serial imaging. Other imaging techniques such as positron emission tomography-computed tomography and magnetic resonance imaging have been investigated, but they are not commonly used to assess patients with ILDs. Disease severity may potentially be estimated using quantitative methods, as well as visual analysis of images. For example, comprehensive assessment of disease staging and progression in patients with ILDs requires visual analysis of pulmonary features that can be performed in parallel with quantitative analysis of the extent of fibrosis. New approaches to image analysis, including the application of machine learning, are being developed.
Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease.
Li Mingzhi,Lei Pinggui,Zeng Bingliang,Li Zongliang,Yu Peng,Fan Bing,Wang Chuanhong,Li Zicong,Zhou Jian,Hu Shaobo,Liu Hao
Coronavirus disease is an emerging infection caused by a novel coronavirus that is moving rapidly. High resolution computed tomography (CT) allows objective evaluation of the lung lesions, thus enabling us to better understand the pathogenesis of the disease. With serial CT examinations, the occurrence, development, and prognosis of the disease can be better understood. The imaging can be sorted into four phases: early phase, progressive phase, severe phase, and dissipative phase. The CT appearance of each phase and temporal progression of the imaging findings are demonstrated.
The place of high-resolution computed tomography imaging in the investigation of interstitial lung disease.
Jeny Florence,Brillet Pierre-Yves,Kim Young-Wouk,Freynet Olivia,Nunes Hilario,Valeyre Dominique
Expert review of respiratory medicine
INTRODUCTION:High-resolution computed tomography (HRCT) has revolutionized the diagnosis, prognosis and in some cases the prediction of therapeutic response in interstitial lung disease (ILD). HRCT represents an essential second step to a patient's clinical history, before considering any other investigation, including lung biopsy. Areas covered: This review describes the current place of HRCT in the diagnosis, prognosis and monitoring of ILD. It also lists some perspectives for the near future. Expert commentary: Since the 1980s, HRCT and its interpretation have improved, the diagnosis value of patterns, and the integration of bio-clinical elements to HRCT have been better standardized. The interobserver agreement has been investigated, allowing a better use of some limits in the interpretation of various signs. It not only takes into account one particular predominant sign, but the combination of patterns and the distribution of findings. Thanks to HRCT, the range of diagnoses and their probability are more accurately identified. The contribution of HRCT has been optimized during the multidisciplinary discussion that a difficult diagnosis calls for. HRCT quantification of the extent of diffuse lung disease becomes possible and is linked to prognosis. In the future, artificial intelligence may significantly modify the practice of radiology.
The Emerging Role of Radiomics in COPD and Lung Cancer.
Refaee Turkey,Wu Guangyao,Ibrahim Abdallah,Halilaj Iva,Leijenaar Ralph T H,Rogers William,Gietema Hester A,Hendriks Lizza E L,Lambin Philippe,Woodruff Henry C
Respiration; international review of thoracic diseases
Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While this process has already been widely researched in lung oncology, the evaluation of COPD in this fashion remains in its infancy. Here we outline the main applications of radiomics in lung cancer and briefly review the workflow from image acquisition to the evaluation of model performance. Finally, we discuss the current assessments of COPD and the potential application of radiomics in COPD.
Computer-assisted detection of infectious lung diseases: a review.
Bağcı Ulaş,Bray Mike,Caban Jesus,Yao Jianhua,Mollura Daniel J
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.
Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges.
Seijo Luis M,Peled Nir,Ajona Daniel,Boeri Mattia,Field John K,Sozzi Gabriella,Pio Ruben,Zulueta Javier J,Spira Avrum,Massion Pierre P,Mazzone Peter J,Montuenga Luis M
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography-based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.
Chest CT in COVID-19 pneumonia: A review of current knowledge.
Jalaber C,Lapotre T,Morcet-Delattre T,Ribet F,Jouneau S,Lederlin M
Diagnostic and interventional imaging
The current COVID-19 pandemic has highlighted the essential role of chest computed tomography (CT) examination in patient triage in the emergency departments, allowing them to be referred to "COVID" or "non-COVID" wards. Initial chest CT examination must be performed without intravenous administration of iodinated contrast material, but contrast material administration is required when pulmonary embolism is suspected, which seems to be frequent in severe forms of the disease. Typical CT features consist of bilateral ground-glass opacities with peripheral, posterior and basal predominance. Lung disease extent on CT correlates with clinical severity. Artificial intelligence could assist radiologists for diagnosis and prognosis evaluation.
[Artificial intelligence-based algorithms : Decision-making support for computed tomography of the chest].
Manava Panagiota,Galster Marco,Heinen Henrik,Stebner Alexander,Lell Michael
Artificial intelligence (AI) algorithms are increasingly used in radiology. The main areas of application are, for example, the detection of lung lesions and the diagnosis of chronic obstructive and interstitial lung diseases. The aim of our study was to train and evaluate a package of algorithms that analyze data from computed tomographic (CT) images of the chest and provide quantitative measurements to the radiologist. The following algorithms were trained: lung lesion detection and measurement, lung lobe segmentation, vessel segmentation and measurement, coronary calcium scoring, measurement and density analysis of vertebral bodies. AI-supported algorithms will become part of daily routine of the radiologist in the future. Tasks that do not require medical expertise can be performed by AI. However, our results show that, based on the current accuracy, verification by an experienced radiologist is necessary.
Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential.
Das Nilakash,Topalovic Marko,Janssens Wim
Current opinion in pulmonary medicine
PURPOSE OF REVIEW:The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases. RECENT FINDINGS:Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies. SUMMARY:Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.
Artificial intelligence in cancer imaging: Clinical challenges and applications.
Bi Wenya Linda,Hosny Ahmed,Schabath Matthew B,Giger Maryellen L,Birkbak Nicolai J,Mehrtash Alireza,Allison Tavis,Arnaout Omar,Abbosh Christopher,Dunn Ian F,Mak Raymond H,Tamimi Rulla M,Tempany Clare M,Swanton Charles,Hoffmann Udo,Schwartz Lawrence H,Gillies Robert J,Huang Raymond Y,Aerts Hugo J W L
CA: a cancer journal for clinicians
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
Computer-aided detection in chest radiography based on artificial intelligence: a survey.
Qin Chunli,Yao Demin,Shi Yonghong,Song Zhijian
Biomedical engineering online
As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.