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Open accessJournal ArticleDOI: 10.1186/S12916-021-01928-3

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value

04 Mar 2021-BMC Medicine (BioMed Central)-Vol. 19, Iss: 1, pp 55-55
Abstract: Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen’s kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen’s kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

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Journal ArticleDOI: 10.1007/S11096-020-01176-0
Woo-Youn Kim1, Suhyun Lee1, Kwanghee Jun1, Young-Mi Ah2  +1 moreInstitutions (2)
Abstract: Background The increasing use of antithrombotic therapies in older patients has led to an increased risk of gastrointestinal (GI) bleeding in chronic nonsteroidal anti-inflammatory drug (NSAID) users. Therefore, there is a pressing need for GI prophylaxis in these high-risk patients. Objective To analyze prescribing patterns and factors associated with the use of gastroprotective agents (GPAs) among high-risk, chronic NSAID users. Setting National claims database including 20% of the total Korean population aged ≥ 65 years. Method In this cross-sectional study, we identified older adults prescribed traditional NSAIDs for > 90 days and classified them into high- and ultra-high-risk groups if they had one or two or more GI risk factors, respectively. Proton pump inhibitors or misoprostol prescribed for more than 80% of traditional NSAID treatment days was regarded as appropriate GI prophylaxis. Main outcome measure Prevalence and associated factors with appropriate GI prophylaxis. Results Among 69,992 chronic traditional NSAID users, 38.8% and 9.4% belonged to the high and ultra-high-risk groups; 13.2% and 19.9% received appropriate GI prophylaxis, respectively. The most frequently used GPA was histamine H2 antagonists. Multiple NSAID use, concomitant antiplatelets and anticoagulants, and prior GI ulcer history increased the likelihood of receiving appropriate GI prophylaxis. Advanced age (≥ 85 years), indications other than arthritis, and neurology specialists negatively affected appropriate GI prophylaxis use. Conclusion Approximately one in five chronic NSAID users, considered ultra-high risk, are prescribed appropriate GI prophylaxis in Korea. Advanced age, indications, and specialties of the prescriber all need to be considered when selecting target populations for interventions.

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1 Citations


Open accessJournal ArticleDOI: 10.3390/CLINPRACT11030056
Mandeep Singh Rahi1, Jay Parekh1, Prachi Pednekar1, Gaurav Parmar1  +5 moreInstitutions (3)
Abstract: Radiotherapy plays an important role in the treatment of localized primary malignancies involving the chest wall or intrathoracic malignancies. Secondary effects of radiotherapy on the lung result in radiation-induced lung disease. The phases of lung injury from radiation range from acute pneumonitis to chronic pulmonary fibrosis. Radiation pneumonitis is a clinical diagnosis based on the history of radiation, imaging findings, and the presence of classic symptoms after exclusion of infection, pulmonary embolism, heart failure, drug-induced pneumonitis, and progression of the primary tumor. Computed tomography (CT) is the preferred imaging modality as it provides a better picture of parenchymal changes. Lung biopsy is rarely required for the diagnosis. Treatment is necessary only for symptomatic patients. Mild symptoms can be treated with inhaled steroids while subacute to moderate symptoms with impaired lung function require oral corticosteroids. Patients who do not tolerate or are refractory to steroids can be considered for treatment with immunosuppressive agents such as azathioprine and cyclosporine. Improvements in radiation technique, as well as early diagnosis and appropriate treatment with high-dose steroids, will lead to lower rates of pneumonitis and an overall good prognosis.

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Topics: Pneumonitis (71%), Lung injury (65%), Radiation-induced lung injury (63%) ... read more

1 Citations


Open accessJournal ArticleDOI: 10.1016/J.CPET.2021.09.010
Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar1, Su Ruan  +2 moreInstitutions (3)
01 Jan 2022-Pet Clinics
Abstract: Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

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Open accessPosted Content
Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar1, Su Ruan  +2 moreInstitutions (3)
Abstract: Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of radiomics and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

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Open accessJournal ArticleDOI: 10.3390/IJTM1030016
Abstract: The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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Open accessProceedings ArticleDOI: 10.1109/CVPR.2016.90
Kaiming He1, Xiangyu Zhang1, Shaoqing Ren1, Jian Sun1Institutions (1)
27 Jun 2016-
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

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Topics: Deep learning (53%), Residual (53%), Convolutional neural network (53%) ... read more

93,356 Citations


Open accessJournal ArticleDOI: 10.1109/TPAMI.2016.2577031
Shaoqing Ren1, Kaiming He2, Ross Girshick3, Jian Sun2Institutions (3)
Abstract: State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps ( including all steps ) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

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Topics: Object detection (58%)

10,415 Citations


Open accessJournal ArticleDOI: 10.1161/01.CIR.97.18.1837
01 May 1998-Circulation
Abstract: Background—The objective of this study was to examine the association of Joint National Committee (JNC-V) blood pressure and National Cholesterol Education Program (NCEP) cholesterol categories with coronary heart disease (CHD) risk, to incorporate them into coronary prediction algorithms, and to compare the discrimination properties of this approach with other noncategorical prediction functions. Methods and Results—This work was designed as a prospective, single-center study in the setting of a community-based cohort. The patients were 2489 men and 2856 women 30 to 74 years old at baseline with 12 years of follow-up. During the 12 years of follow-up, a total of 383 men and 227 women developed CHD, which was significantly associated with categories of blood pressure, total cholesterol, LDL cholesterol, and HDL cholesterol (all P,.001). Sex-specific prediction equations were formulated to predict CHD risk according to age, diabetes, smoking, JNC-V blood pressure categories, and NCEP total cholesterol and LDL cholesterol categories. The accuracy of this categorical approach was found to be comparable to CHD prediction when the continuous variables themselves were used. After adjustment for other factors, ’28% of CHD events in men and 29% in women were attributable to blood pressure levels that exceeded high normal ($130/85). The corresponding multivariable-adjusted attributable risk percent associated with elevated total cholesterol ($200 mg/dL) was 27% in men and 34% in women. Conclusions—Recommended guidelines of blood pressure, total cholesterol, and LDL cholesterol effectively predict CHD risk in a middle-aged white population sample. A simple coronary disease prediction algorithm was developed using categorical variables, which allows physicians to predict multivariate CHD risk in patients without overt CHD. (Circulation. 1998;97:1837-1847.)

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8,777 Citations


Open accessJournal ArticleDOI: 10.3322/CAAC.21590
Abstract: Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.

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Topics: Cancer Death Rate (64%), Mortality rate (56%), Population (52%) ... read more

8,578 Citations


Open accessJournal ArticleDOI: 10.1056/NEJMOA1102873
Abstract: Background The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer. Methods From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. Results The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02). Conclusions Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.).

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Topics: National Lung Screening Trial (72%), Lung cancer screening (71%), Lung cancer (59%) ... read more

6,212 Citations