A Review of Fusion Methods for Omics and Imaging Data.
IEEE/ACM transactions on computational biology and bioinformatics
The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.
10.1109/TCBB.2022.3143900
Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data.
Emerging topics in life sciences
Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.
10.1042/ETLS20210249
Effects of Electronic Cigarette Constituents on the Human Lung: A Pilot Clinical Trial.
Song Min-Ae,Reisinger Sarah A,Freudenheim Jo L,Brasky Theodore M,Mathé Ewy A,McElroy Joseph P,Nickerson Quentin A,Weng Daniel Y,Wewers Mark D,Shields Peter G
Cancer prevention research (Philadelphia, Pa.)
Electronic cigarette (e-cig) use is continuing to increase, particularly among youth never-smokers, and is used by some smokers to quit. The acute and chronic toxicity of e-cig use is unclear generally in the context of increasing reports of inflammatory-type pneumonia in some e-cig users. To assess lung effects of e-cigs without nicotine or flavors, we conducted a pilot study with serial bronchoscopies over 4 weeks in 30 never-smokers, randomized either to a 4-week intervention with the use of e-cigs containing only 50% propylene glycol (PG) and 50% vegetable glycerine or to a no-use control group. Compliance to the e-cig intervention was assessed by participants sending daily puff counts and by urinary PG. Inflammatory cell counts and cytokines were determined in bronchoalveolar lavage (BAL) fluids. Genome-wide expression, miRNA, and mRNA were determined from bronchial epithelial cells. There were no significant differences in changes of BAL inflammatory cell counts or cytokines between baseline and follow-up, comparing the control and e-cig groups. However, in the intervention but not the control group, change in urinary PG as a marker of e-cig use and inhalation was significantly correlated with change in cell counts (cell concentrations, macrophages, and lymphocytes) and cytokines (IL8, IL13, and TNFα), although the absolute magnitude of changes was small. There were no significant changes in mRNA or miRNA gene expression. Although limited by study size and duration, this is the first experimental demonstration of an impact of e-cig use on inflammation in the human lung among never-smokers.
10.1158/1940-6207.CAPR-19-0400
Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data.
Holzinger Andreas,Haibe-Kains Benjamin,Jurisica Igor
European journal of nuclear medicine and molecular imaging
Artificial intelligence (AI) is currently regaining enormous interest due to the success of machine learning (ML), and in particular deep learning (DL). Image analysis, and thus radiomics, strongly benefits from this research. However, effectively and efficiently integrating diverse clinical, imaging, and molecular profile data is necessary to understand complex diseases, and to achieve accurate diagnosis in order to provide the best possible treatment. In addition to the need for sufficient computing resources, suitable algorithms, models, and data infrastructure, three important aspects are often neglected: (1) the need for multiple independent, sufficiently large and, above all, high-quality data sets; (2) the need for domain knowledge and ontologies; and (3) the requirement for multiple networks that provide relevant relationships among biological entities. While one will always get results out of high-dimensional data, all three aspects are essential to provide robust training and validation of ML models, to provide explainable hypotheses and results, and to achieve the necessary trust in AI and confidence for clinical applications.
10.1007/s00259-019-04382-9
Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images.
Scientific reports
The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support.
10.1038/s41598-021-91305-0
Early chest CT-scan in emergency patients affected by community-acquired pneumonia is associated with improved diagnosis consistency.
European journal of emergency medicine : official journal of the European Society for Emergency Medicine
Chest CT-scan (CT) exceeds chest X-ray (CXR) to diagnose community-acquired pneumonia (CAP) but actual use and results remain unclear. We examine whether CT performed at ED visit improved ED diagnosis of CAP as compared to a final diagnosis of CAP at hospital discharge (gold standard diagnosis for the study), and how it impacts relevant clinical outcomes. This retrospective monocenter observational study was based on the analysis of the hospital database. Patients with a diagnosis of CAP in the ED (ICD-10 codes: J110, J111, from J12- to J18-, J440, J690, U0710, and U0711) were included. We compared ED patients who were diagnosed with CAP using CXR and CT. We measured diagnostic consistency, duration of ED visit, percentage of CXR and CT during hospital stay, hospital length-of-stay, ICU admission, and in-hospital mortality. Multivariate analysis was adjusted for CRB65 score by multiple logistic regression analysis for binary outcomes and by multivariate analysis of variance for continuous outcomes. We included 994 ED patients with an initial diagnosis of CAP (751 receiving CXR, 243 receiving CT). CT prescription in the ED increased over time ( P < 0.001). In patients admitted after ED, CT improved diagnosis consistency for CAP [88.2% vs. 80.9%; difference 7.3% (95% confidence interval 1.2-13.3%)] with a trend for lower hospital length-of-stay [10.2 vs. 12.2 days; difference -2.0 (95% confidence interval -3.9 to -0.1)], but not ICU admission ( P = 0.09) and in-hospital mortality ( P = 0.056). Diagnosis of patients admitted with CAP improved when CT was obtained at ED visit. These results should be reproduced at a larger scale to test whether early CT conserves healthcare resources.
10.1097/MEJ.0000000000000955
Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT.
IEEE transactions on medical imaging
In our earlier study, we proposed a regional Markov random field type tissue-specific texture prior from previous full-dose computed tomography (FdCT) scan for current low-dose CT (LdCT) imaging, which showed clinical benefits through task-based evaluation. Nevertheless, two assumptions were made for early study. One assumption is that the center pixel has a linear relationship with its nearby neighbors and the other is previous FdCT scans of the same subject are available. To eliminate the two assumptions, we proposed a database assisted end-to-end LdCT reconstruction framework which includes a deep learning texture prior model and a multi-modality feature based candidate selection model. A convolutional neural network-based texture prior is proposed to eliminate the linear relationship assumption. And for scenarios in which the concerned subject has no previous FdCT scans, we propose to select one proper prior candidate from the FdCT database using multi-modality features. Features from three modalities are used including the subjects' physiological factors, the CT scan protocol, and a novel feature named Lung Mark which is deliberately proposed to reflect the z-axial property of human anatomy. Moreover, a majority vote strategy is designed to overcome the noise effect from LdCT scans. Experimental results showed the effectiveness of Lung Mark. The selection model has accuracy of 84% testing on 1,470 images from 49 subjects. The learned texture prior from FdCT database provided reconstruction comparable to the subjects having corresponding FdCT. This study demonstrated the feasibility of bringing clinically relevant textures from available FdCT database to perform Bayesian reconstruction of any current LdCT scan.
10.1109/TMI.2021.3139533
CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [F]FDG PET, T1 and FLAIR MRI, and CT images available for research.
EJNMMI research
We present a database of cerebral PET FDG and anatomical MRI for 37 normal adult human subjects (CERMEP-IDB-MRXFDG). Thirty-nine participants underwent static [F]FDG PET/CT and MRI, resulting in [F]FDG PET, T1 MPRAGE MRI, FLAIR MRI, and CT images. Two participants were excluded after visual quality control. We describe the acquisition parameters, the image processing pipeline and provide participants' individual demographics (mean age 38 ± 11.5 years, range 23-65, 20 women). Volumetric analysis of the 37 T1 MRIs showed results in line with the literature. A leave-one-out assessment of the 37 FDG images using Statistical Parametric Mapping (SPM) yielded a low number of false positives after exclusion of artefacts. The database is stored in three different formats, following the BIDS common specification: (1) DICOM (data not processed), (2) NIFTI (multimodal images coregistered to PET subject space), (3) NIFTI normalized (images normalized to MNI space). Bona fide researchers can request access to the database via a short form.
10.1186/s13550-021-00830-6
French Imaging Database Against Coronavirus (FIDAC): A large COVID-19 multi-center chest CT database.
Diagnostic and interventional imaging
PURPOSE:During the first wave of the COVID-19 pandemic, the French Society of Radiology and the French College of Radiology, in partnership with NEHS Digital, have set up a system to collect chest computed tomography (CT) examinations with clinical, virological and radiological metadata, from patients clinically suspected of COVID-19 pneumonia. This allowed the constitution of an anonymized multicenter database, named FIDAC (French Imaging Database Against Coronavirus). The aim of this report was to describe the content of this public database. MATERIALS AND METHODS:Twenty-two French radiology centers participated to the data collection. The data collected were chest CT examinations in DICOM format associated with the following metadata: patient age and sex, originating facility identifier, originating facility region, time from symptom onset to CT examination, indication for CT examination, reverse transcription-polymerase chain reaction (RT-PCR) results and normalized CT report performed by a senior radiologist. All the data were anonymized and sent through a NEHS Digital system to a centralized data center. RESULTS:A total of 5944 patients were included from the 22 centers aggregated into 8 regions with a mean number of patients of 743 ± 603.3 [SD] per region (range: 102-1577 patients). Reasons for CT examination and normalized CT reports were provided for all patients. RT-PCR results were provided in 5574 patients (93.77%) with a positive result of RT-PCR in 44.6% of patients. CONCLUSION:The FIDAC project allowed the creation of a large database of chest CT images and metadata available, under conditions, in open access through the CERF-SFR website.
10.1016/j.diii.2022.05.006
Low-dose CT image and projection dataset.
Moen Taylor R,Chen Baiyu,Holmes David R,Duan Xinhui,Yu Zhicong,Yu Lifeng,Leng Shuai,Fletcher Joel G,McCollough Cynthia H
Medical physics
PURPOSE:To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. ACQUISITION AND VALIDATION METHODS:The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. Scans were performed on CT systems from two different CT manufacturers using routine clinical protocols. Projection data were validated by reconstructing the data using several different reconstruction algorithms and through use of the data in the 2016 Low Dose CT Grand Challenge. Reduced dose projection data were simulated for each scan using a validated noise-insertion method. Radiologists marked location and diagnosis for detected pathologies. Reference truth was obtained from the patient medical record, either from histology or subsequent imaging. DATA FORMAT AND USAGE NOTES:Projection datasets were converted into the previously developed DICOM-CT-PD format, which is an extended DICOM format created to store CT projections and acquisition geometry in a nonproprietary format. Image data are stored in the standard DICOM image format and clinical data in a spreadsheet. Materials are provided to help investigators use the DICOM-CT-PD files, including a dictionary file, data reader, and user manual. The library is publicly available from The Cancer Imaging Archive (https://doi.org/10.7937/9npb-2637). POTENTIAL APPLICATIONS:This CT data library will facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence. The provided clinical information allows evaluation of task-based diagnostic performance.
10.1002/mp.14594
An Open Library of CT Patient Projection Data.
Chen Baiyu,Leng Shuai,Yu Lifeng,Holmes David,Fletcher Joel,McCollough Cynthia
Proceedings of SPIE--the International Society for Optical Engineering
Lack of access to projection data from patient CT scans is a major limitation for development and validation of new reconstruction algorithms. To meet this critical need, we are building a library of CT patient projection data in an open and vendor-neutral format, DICOM-CT-PD, which is an extended DICOM format that contains sinogram data, acquisition geometry, patient information, and pathology identification. The library consists of scans of various types, including head scans, chest scans, abdomen scans, electrocardiogram (ECG)-gated scans, and dual-energy scans. For each scan, three types of data are provided, including DICOM-CT-PD projection data at various dose levels, reconstructed CT images, and a free-form text file. Several instructional documents are provided to help the users extract information from DICOM-CT-PD files, including a dictionary file for the DICOM-CT-PD format, a DICOM-CT-PD reader, and a user manual. Radiologist detection performance based on the reconstructed CT images is also provided. So far 328 head cases, 228 chest cases, and 228 abdomen cases have been collected for potential inclusion. The final library will include a selection of 50 head, chest, and abdomen scans each from at least two different manufacturers, and a few ECG-gated scans and dual-source, dual-energy scans. It will be freely available to academic researchers, and is expected to greatly facilitate the development and validation of CT reconstruction algorithms.
10.1117/12.2216823
Automatic quantitative analysis of pulmonary vascular morphology in CT images.
Medical physics
PURPOSE:Vascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study was therefore to develop and validate an automatic method for quantifying pulmonary vascular morphology in CT images. METHODS:The proposed method consists of pulmonary vessel extraction and quantification. For extracting pulmonary vessels, a graph-cuts-based method is proposed which considers appearance (CT intensity) and shape (vesselness from a Hessian-based filter) features, and incorporates distance to the airways into the cost function to prevent false detection of airway walls. For quantifying the extracted pulmonary vessels, a radius histogram is generated by counting the occurrence of vessel radii, calculated from a distance transform-based method. Subsequently, two biomarkers, slope α and intercept β, are calculated by linear regression on the radius histogram. A public data set from the VESSEL12 challenge was used to independently evaluate the vessel extraction. The quantitative analysis method was validated using images of a three-dimensional (3D) printed vessel phantom, scanned by a clinical CT scanner and a micro-CT scanner (to obtain a gold standard). To confirm the association between imaging biomarkers and pulmonary function, 77 scleroderma patients were investigated with the proposed method. RESULTS:In the independent evaluation with the public data set, our vessel segmentation method obtained an area under the receiver operating characteristic (ROC) curve of 0.976. The median radius difference between clinical and micro-CT scans of a 3D printed vessel phantom was 0.062 ± 0.020 mm, with interquartile range of 0.199 ± 0.050 mm. In the studied patient group, a significant correlation between diffusion capacity for carbon monoxide and the biomarkers, α (R = -0.27, P = 0.018) and β (R = 0.321, P = 0.004), was obtained. CONCLUSION:In conclusion, the proposed method was validated independently using a public data set resulting in an area under the ROC curve of 0.976 and using a 3D printed vessel phantom data set, showing a vessel sizing error of 0.062 mm (0.16 in-plane pixel units). The correlation between imaging biomarkers and diffusion capacity in a clinical data set confirmed an association between lung structure and function. This quantification of pulmonary vascular morphology may be helpful in understanding the pathophysiology of pulmonary vascular diseases.
10.1002/mp.13659
Single-Center Analysis of Pegfilgrastim-induced Aortitis Using a Drug Prescription Database and CT Findings.
Radiology
Background Pegfilgrastim-induced aortitis is a rare but serious adverse event in patients undergoing anticancer therapy with granulocyte colony-stimulating factor analogs. Despite previous case series and systemic reviews, the exact incidence, clinical presentation, and CT manifestations of pegfilgrastim-induced aortitis remain unclear. Purpose To clarify the incidence and clinicoradiologic characteristics of pegfilgrastim-induced aortitis. Materials and Methods Pegfilgrastim administration records from January 2015 to March 2021 were retrospectively collected from the drug prescription database of a single center and were matched with the relevant findings in the CT database. Corresponding CT images within 6 months were available for a total of 1462 doses of pegfilgrastim in 674 patients. Four radiologists reviewed the CT images for the presence of aortitis in two steps. Clinical information and the distribution of aortitis on CT images were examined for patients with a diagnosis of pegfilgrastim-induced aortitis. Results Pegfilgrastim-induced aortitis was observed in 18 of 674 patients (mean age, 62 years ± 13 [SD]; 424 men), resulting in incidence rates of 2.7% per patient (95% CI: 1.6, 4.2) and 1.2% per dose (95% CI: 0.7, 1.9). The most common original primary malignancies were esophageal cancer ( = 10, 9%), breast cancer ( = 3, 4%), and pancreatic cancer ( = 2, 2%). The most common anticancer drugs used at onset were 5-fluorouracil, cisplatin, and docetaxel. Seven cases were symptomatic, while the remaining 11 (61%) were asymptomatic. CT findings indicated that aortitis involved branches of the aortic arch in 13 cases (72%), aortic arch in 10 cases (56%), and abdominal aorta in two cases (11%). Conclusion Pegfilgrastim-induced aortitis may be more prevalent than previously reported and may be more common in patients with esophageal cancer and those who received 5-fluorouracil, cisplatin, and docetaxel as anticancer drugs. The findings also suggest that pegfilgrastim-induced aortitis is often characterized by aortic arch and proximal branch involvement at CT. © RSNA, 2022 See also the editorial by Krinsky in this issue.
10.1148/radiol.220357
[Performance of Deep-learning-based Artificial Intelligence on Detection of Pulmonary Nodules in Chest CT].
Li Xinling,Guo Fangfang,Zhou Zhen,Zhang Fandong,Wang Qin,Peng Zhijun,Su Datong,Fan Yaguang,Wang Ying
Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND:The detection of pulmonary nodules is a key step to achieving the early diagnosis and therapy of lung cancer. Deep learning based Artificial intelligence (AI) presents as the state of the art in the area of nodule detection, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the performance of AI in the detection of malignant and non-calcified nodules in chest CT. METHODS:Two hundred chest computed tomography (CT) data were randomly selected from a self-built nodule database from Tianjin Medical University General Hospital. Both the pathology confirmed lung cancers and the nodules in the process of follow-up were included. All CTs were processed by AI and the results were compared with that of radiologists retrieved from the original medical reports. The ground truths were further determined by two experienced radiologists. The size and characteristics of the nodules were evaluated as well. The sensitivity and false positive rate were used to evaluate the effectiveness of AI and radiologists in detecting nodules. The McNemar test was used to determine whether there was a significant difference. RESULTS:A total of 889 non-calcified nodules were determined by experts on chest CT, including 133 lung cancers. Of them, 442 nodules were less than 5 mm. The cancer detection rates of AI and radiologists are 100%. The sensitivity of AI on nodule detection was significantly higher than that of radiologists (99.1% vs 43%, P<0.001). The false-positive rate of AI was 4.9 per CT and decreased to 1.5 when nodules less than 5 mm were excluded. CONCLUSIONS:AI achieves the detection of all malignancies and improve the sensitivity of pulmonary nodules detection beyond radiologists, with a low false positive rate after excluding small nodules.
10.3779/j.issn.1009-3419.2019.06.02
Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning.
Chest
BACKGROUND:Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal form of interstitial lung disease (ILD) characterized by the absence of a known cause and usual interstitial pneumonitis (UIP) pattern on chest CT imaging and/or histopathology. Distinguishing UIP/IPF from other ILD subtypes is essential given different treatments and prognosis. Lung biopsy is necessary when noninvasive data are insufficient to render a confident diagnosis. RESEARCH QUESTION:Can we improve noninvasive diagnosis of UIP be improved by predicting ILD histopathology from CT scans by using deep learning? STUDY DESIGN AND METHODS:This study retrospectively identified a cohort of 1,239 patients in a multicenter database with pathologically proven ILD who had chest CT imaging. Each case was assigned a label based on histopathologic diagnosis (UIP or non-UIP). A custom deep learning model was trained to predict class labels from CT images (training set, n = 894) and was evaluated on a 198-patient test set. Separately, two subspecialty-trained radiologists manually labeled each CT scan in the test set according to the 2018 American Thoracic Society IPF guidelines. The performance of the model in predicting histopathologic class was compared against radiologists' performance by using area under the receiver-operating characteristic curve as the primary metric. Deep learning model reproducibility was compared against intra-rater and inter-rater radiologist reproducibility. RESULTS:For the entire cohort, mean patient age was 62 ± 12 years, and 605 patients were female (49%). Deep learning performance was superior to visual analysis in predicting histopathologic diagnosis (area under the receiver-operating characteristic curve, 0.87 vs 0.80, respectively; P < .05). Deep learning model reproducibility was significantly greater than radiologist inter-rater and intra-rater reproducibility (95% CI for difference in Krippendorff's alpha did not include zero). INTERPRETATION:Deep learning may be superior to visual assessment in predicting UIP/IPF histopathology from CT imaging and may serve as an alternative to invasive lung biopsy.
10.1016/j.chest.2022.03.044
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pneumonia, early diagnosis is essential for correct treatment, as well as to reduce the stress on the healthcare system. Along with COVID-19, other etiologies of pneumonia and Tuberculosis (TB) constitute additional challenges to the medical system. Pneumonia (viral as well as bacterial) kills about 2 million infants every year and is consistently estimated as one of the most important factor of childhood mortality (according to the World Health Organization). Chest X-ray (CXR) and computed tomography (CT) scans are the primary imaging modalities for diagnosing respiratory diseases. Although CT scans are the gold standard, they are more expensive, time consuming, and are associated with a small but significant dose of radiation. Hence, CXR have become more widespread as a first line investigation. In this regard, the objective of this work is to develop a new deep transfer learning pipeline, named DenResCov-19, to diagnose patients with COVID-19, pneumonia, TB or healthy based on CXR images. The pipeline consists of the existing DenseNet-121 and the ResNet-50 networks. Since the DenseNet and ResNet have orthogonal performances in some instances, in the proposed model we have created an extra layer with convolutional neural network (CNN) blocks to join these two models together to establish superior performance as compared to the two individual networks. This strategy can be applied universally in cases where two competing networks are observed. We have tested the performance of our proposed network on two-class (pneumonia and healthy), three-class (COVID-19 positive, healthy, and pneumonia), as well as four-class (COVID-19 positive, healthy, TB, and pneumonia) classification problems. We have validated that our proposed network has been able to successfully classify these lung-diseases on our four datasets and this is one of our novel findings. In particular, the AUC-ROC are 99.60, 96.51, 93.70, 96.40% and the F1 values are 98.21, 87.29, 76.09, 83.17% on our Dataset X-Ray 1, 2, 3, and 4 (DXR1, DXR2, DXR3, DXR4), respectively.
10.1016/j.compmedimag.2021.102008
Longitudinal Modeling of Lung Function Trajectories in Smokers with and without Chronic Obstructive Pulmonary Disease.
American journal of respiratory and critical care medicine
RATIONALE:The relationship between longitudinal lung function trajectories, chest computed tomography (CT) imaging, and genetic predisposition to chronic obstructive pulmonary disease (COPD) has not been explored. OBJECTIVES:1) To model trajectories using a data-driven approach applied to longitudinal data spanning adulthood in the Normative Aging Study (NAS), and 2) to apply these models to demographically similar subjects in the COPDGene (Genetic Epidemiology of COPD) Study with detailed phenotypic characterization including chest CT. METHODS:We modeled lung function trajectories in 1,060 subjects in NAS with a median follow-up time of 29 years. We assigned 3,546 non-Hispanic white males in COPDGene to these trajectories for further analysis. We assessed phenotypic and genetic differences between trajectories and across age strata. MEASUREMENTS AND MAIN RESULTS:We identified four trajectories in NAS with differing levels of maximum lung function and rate of decline. In COPDGene, 617 subjects (17%) were assigned to the lowest trajectory and had the greatest radiologic burden of disease (P < 0.01); 1,283 subjects (36%) were assigned to a low trajectory with evidence of airway disease preceding emphysema on CT; 1,411 subjects (40%) and 237 subjects (7%) were assigned to the remaining two trajectories and tended to have preserved lung function and negligible emphysema. The genetic contribution to these trajectories was as high as 83% (P = 0.02), and membership in lower lung function trajectories was associated with greater parental histories of COPD, decreased exercise capacity, greater dyspnea, and more frequent COPD exacerbations. CONCLUSIONS:Data-driven analysis identifies four lung function trajectories. Trajectory membership has a genetic basis and is associated with distinct lung structural abnormalities.
10.1164/rccm.201707-1405OC
Cigarette smoking is associated with amplified age-related volume loss in subcortical brain regions.
Durazzo Timothy C,Meyerhoff Dieter J,Yoder Karmen K,Murray Donna E
Drug and alcohol dependence
BACKGROUND:Magnetic resonance imaging studies of cigarette smoking-related effects on human brain structure have primarily employed voxel-based morphometry, and the most consistently reported finding was smaller volumes or lower density in anterior frontal regions and the insula. Much less is known about the effects of smoking on subcortical regions. We compared smokers and non-smokers on regional subcortical volumes, and predicted that smokers demonstrate greater age-related volume loss across subcortical regions than non-smokers. METHODS:Non-smokers (n=43) and smokers (n=40), 22-70 years of age, completed a 4T MRI study. Bilateral total subcortical lobar white matter (WM) and subcortical nuclei volumes were quantitated via FreeSurfer. In smokers, associations between smoking severity measures and subcortical volumes were examined. RESULTS:Smokers demonstrated greater age-related volume loss than non-smokers in the bilateral subcortical lobar WM, thalamus, and cerebellar cortex, as well as in the corpus callosum and subdivisions. In smokers, higher pack-years were associated with smaller volumes of the bilateral amygdala, nucleus accumbens, total corpus callosum and subcortical WM. CONCLUSIONS:Results provide novel evidence that chronic smoking in adults is associated with accelerated age-related volume loss in subcortical WM and GM nuclei. Greater cigarette quantity/exposure was related to smaller volumes in regions that also showed greater age-related volume loss in smokers. Findings suggest smoking adversely affected the structural integrity of subcortical brain regions with increasing age and exposure. The greater age-related volume loss in smokers may have implications for cortical-subcortical structural and/or functional connectivity, and response to available smoking cessation interventions.
10.1016/j.drugalcdep.2017.04.012
Clinical 7T MRI for epilepsy care: Value, patient selection, technical issues, and outlook.
Journal of neuroimaging : official journal of the American Society of Neuroimaging
Ultra-high-field 7.0 Tesla (T) MRI offers substantial gains in signal-to-noise ratio (SNR) over 3T and 1.5T, but for over two decades has remained a research tool, while 3T scanners have achieved widespread clinical use. This much slower translation of 7T relates to daunting technical challenges encountered in ultra-high-field human MR imaging. The recent introduction of United States Food and Drug Administration (FDA)-approved clinical 7T scanners promises to be a watershed for many 7T neuroimaging applications, including epilepsy imaging. The high SNR of 7T allows clinical imaging of fine neuroanatomic detail at unprecedented spatial resolution, helping with detection and differentiation of subtle, potentially treatable lesions undetectable or suboptimally assessed at 3T. The accompanying research paper reports our group's analysis of 7T MRI efficacy in epilepsy treatment planning. Here, we introduce the technical background and clinical approach we currently use, in order to assist clinical epileptologists and neuroimagers contemplating, creating, or referring patients to a clinical 7T epilepsy imaging service. We describe a tiered epilepsy imaging strategy and protocols designed to optimize 7T value and work around signal intensity variation and signal loss artifacts, which remain significant challenges to full exploitation of 7T clinical value. We describe FDA-approved techniques for mitigating these artifacts and briefly outline techniques currently under development, but not yet FDA approved. Finally, we discuss the major issues in 7T patient safety and toleration, outlining their physical causes and effects on workflow, and provide references to more comprehensive technical reviews for readers seeking greater technical detail.
10.1111/jon.12974