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Ciarmiello A, Tutino F, Giovannini E, Milano A, Barattini M, Yosifov N, Calvi D, Setti M, Sivori M, Sani C, Bastreri A, Staffiere R, Stefanini T, Artioli S, Giovacchini G. Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection. J Clin Med 2023; 12:7164. [PMID: 38002776 PMCID: PMC10672177 DOI: 10.3390/jcm12227164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/03/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
AIM To evaluate the performance of a machine learning model based on demographic variables, blood tests, pre-existing comorbidities, and computed tomography(CT)-based radiomic features to predict critical outcome in patients with acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS We retrospectively enrolled 694 SARS-CoV-2-positive patients. Clinical and demographic data were extracted from clinical records. Radiomic data were extracted from CT. Patients were randomized to the training (80%, n = 556) or test (20%, n = 138) dataset. The training set was used to define the association between severity of disease and comorbidities, laboratory tests, demographic, and CT-based radiomic variables, and to implement a risk-prediction model. The model was evaluated using the C statistic and Brier scores. The test set was used to assess model prediction performance. RESULTS Patients who died (n = 157) were predominantly male (66%) over the age of 50 with median (range) C-reactive protein (CRP) = 5 [1, 37] mg/dL, lactate dehydrogenase (LDH) = 494 [141, 3631] U/I, and D-dimer = 6.006 [168, 152.015] ng/mL. Surviving patients (n = 537) had median (range) CRP = 3 [0, 27] mg/dL, LDH = 484 [78, 3.745] U/I, and D-dimer = 1.133 [96, 55.660] ng/mL. The strongest risk factors were D-dimer, age, and cardiovascular disease. The model implemented using the variables identified using the LASSO Cox regression analysis classified 90% of non-survivors as high-risk individuals in the testing dataset. In this sample, the estimated median survival in the high-risk group was 9 days (95% CI; 9-37), while the low-risk group did not reach the median survival of 50% (p < 0.001). CONCLUSIONS A machine learning model based on combined data available on the first days of hospitalization (demographics, CT-radiomics, comorbidities, and blood biomarkers), can identify SARS-CoV-2 patients at risk of serious illness and death.
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Affiliation(s)
- Andrea Ciarmiello
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
| | - Francesca Tutino
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
| | - Elisabetta Giovannini
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
| | - Amalia Milano
- Oncology Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy;
| | - Matteo Barattini
- Radiology Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy; (M.B.); (T.S.)
| | - Nikola Yosifov
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
| | - Debora Calvi
- Infectius Diseases Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy; (D.C.); (S.A.)
| | - Maurizo Setti
- Internal Medicine Unit, Ospedale San Bartolomeo, 19138 Sarzana, Italy;
| | | | - Cinzia Sani
- Intensive Care Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy;
| | - Andrea Bastreri
- Emergency Department, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy;
| | | | - Teseo Stefanini
- Radiology Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy; (M.B.); (T.S.)
| | - Stefania Artioli
- Infectius Diseases Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy; (D.C.); (S.A.)
| | - Giampiero Giovacchini
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
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Abdel-Hamid L. Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-23. [PMID: 37362648 PMCID: PMC10175919 DOI: 10.1007/s11042-023-15485-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/01/2022] [Accepted: 04/18/2023] [Indexed: 06/28/2023]
Abstract
Chest computer tomography (CT) provides a readily available and efficient tool for COVID-19 diagnosis. Wavelet and contourlet transforms have the advantages of being localized in both space and time. In addition, multiresolution analysis allows for the separation of relevant image information in the different subbands. In the present study, transform-based features were investigated for COVID-19 classification using chest CT images. Several textural and statistical features were computed from the approximation and detail subbands in order to fully capture disease symptoms in the chest CT images. Initially, multiresolution analysis was performed considering three different wavelet and contourlet levels to determine the transform and decomposition level most suitable for feature extraction. Analysis showed that contourlet features computed from the first decomposition level (L1) led to the most reliable COVID-19 classification results. The complete feature vector was computed in less than 25 ms for a single image having of resolution 256 × 256 pixels. Next, particle swarm optimization (PSO) was implemented to find the best set of L1-Contourlet features for enhanced performance. Accuracy, sensitivity, specificity, precision, and F-score of a 100% were achieved by the reduced feature set using the support vector machine (SVM) classifier. The presented contourlet-based COVID-19 detection method was also shown to outperform several state-of-the-art deep learning approaches from literature. The present study demonstrates the reliability of transform-based features for COVID-19 detection with the advantage of reduced computational complexity. Transform-based features are thus suitable for integration within real-time automatic screening systems used for the initial screening of COVID-19.
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Affiliation(s)
- Lamiaa Abdel-Hamid
- Department of Electronics & Communication, Faculty of Engineering, Misr International University (MIU), Cairo, Egypt
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3
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Di Napoli A, Tagliente E, Pasquini L, Cipriano E, Pietrantonio F, Ortis P, Curti S, Boellis A, Stefanini T, Bernardini A, Angeletti C, Ranieri SC, Franchi P, Voicu IP, Capotondi C, Napolitano A. 3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients. J Digit Imaging 2023; 36:603-616. [PMID: 36450922 PMCID: PMC9713092 DOI: 10.1007/s10278-022-00734-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 10/08/2022] [Accepted: 10/30/2022] [Indexed: 12/02/2022] Open
Abstract
Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external validation groups included 608, 153, and 290 patients, respectively. Images, clinical, and laboratory data were fed into different customizations of a dense neural network to choose the best performing architecture for the prediction of mortality, intubation, and intensive care unit (ICU) admission. The AI model tested on CT and clinical features displayed accuracy, sensitivity, specificity, and ROC-AUC, respectively, of 91.7%, 90.5%, 92.4%, and 95% for the prediction of patient's mortality; 91.3%, 91.5%, 89.8%, and 95% for intubation; and 89.6%, 90.2%, 86.5%, and 94% for ICU admission (internal validation) in the testing cohort. The performance was lower in the validation cohort for mortality (71.7%, 55.6%, 74.8%, 72%), intubation (72.6%, 74.7%, 45.7%, 64%), and ICU admission (74.7%, 77%, 46%, 70%) prediction. The addition of the available laboratory data led to an increase in sensitivity for patient's mortality (66%) and specificity for intubation and ICU admission (50%, 52%, respectively), while the other metrics maintained similar performance results. We present a deep-learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients. KEY POINTS: • 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (> 90%) mortality, ICU admittance, and intubation in COVID-19 patients. • The model slightly increased prediction results when laboratory data were added to the analysis, despite data imbalance. However, the model accuracy dropped when CT images were not considered in the analysis, implying an important role of CT in predicting outcomes.
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Affiliation(s)
- Alberto Di Napoli
- Radiology Department, Castelli Hospital, 00040, Ariccia, Italy
- NESMOS Department, Neuroradiology Unit, Sant'Andrea Hospital, Sapienza University, Via Grottarossa 1035, 00189, 00165, Rome, Italy
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children's Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165, Rome, Italy
| | - Luca Pasquini
- NESMOS Department, Neuroradiology Unit, Sant'Andrea Hospital, Sapienza University, Via Grottarossa 1035, 00189, 00165, Rome, Italy.
- Radiology Department, Neuroradiology Service, Memorial Sloan Kettering Cancer Center, New York, NY, 1275, USA.
| | - Enrica Cipriano
- COVID Medicine Department, Castelli Hospital, 00040, Ariccia, Italy
| | | | - Piermaria Ortis
- COVID Intensive Care Unit, Castelli Hospital, 00040, Ariccia, Italy
| | - Simona Curti
- Emergency Department, Castelli Hospital, 00040, Ariccia, Italy
| | - Alessandro Boellis
- Radiology Department, Sant'Andrea Civil Hospital, 19121, La Spezia, Italy
| | - Teseo Stefanini
- Radiology Department, Sant'Andrea Civil Hospital, 19121, La Spezia, Italy
| | - Antonio Bernardini
- Radiology Department, Giuseppe Mazzini Civil Hospital, 64100, Teramo, Italy
| | - Chiara Angeletti
- Anestesiology, Intensive Care and Pain Medicine, Emergency Department, Giuseppe Mazzini Civil Hospital, 64100, Teramo, Italy
| | | | - Paola Franchi
- Radiology Department, Giuseppe Mazzini Civil Hospital, 64100, Teramo, Italy
| | - Ioan Paul Voicu
- Radiology Department, Giuseppe Mazzini Civil Hospital, 64100, Teramo, Italy
| | - Carlo Capotondi
- Radiology Department, Castelli Hospital, 00040, Ariccia, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children's Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165, Rome, Italy
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Alsaaidah B, Al-Hadidi MR, Al-Nsour H, Masadeh R, AlZubi N. Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging 2022; 8:267. [PMID: 36286361 PMCID: PMC9604704 DOI: 10.3390/jimaging8100267] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/11/2022] [Accepted: 09/20/2022] [Indexed: 01/14/2023] Open
Abstract
The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.
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Affiliation(s)
- Bayan Alsaaidah
- Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan
| | - Moh’d Rasoul Al-Hadidi
- Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan
| | - Heba Al-Nsour
- Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan
| | - Raja Masadeh
- Computer Science Department, The World Islamic Sciences and Education University, Amman 11947, Jordan
| | - Nael AlZubi
- Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan
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Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA). Diagnostics (Basel) 2022; 12:diagnostics12071660. [PMID: 35885564 PMCID: PMC9318450 DOI: 10.3390/diagnostics12071660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/23/2022] [Accepted: 07/01/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.
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Mehrpouyan M, Zamanian H, Mehri-Kakavand G, Pursamimi M, Shalbaf A, Ghorbani M, Abbaskhani Davanloo A. Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach. Phys Eng Sci Med 2022; 45:747-755. [PMID: 35796865 PMCID: PMC9261171 DOI: 10.1007/s13246-022-01140-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 05/16/2022] [Indexed: 11/22/2022]
Abstract
The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.
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Affiliation(s)
- Mohammad Mehrpouyan
- Non-Communicable Diseases Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran.,Medical Physics and Radiological Sciences Department, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Hamed Zamanian
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran
| | - Ghazal Mehri-Kakavand
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Mohamad Pursamimi
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Ahmad Shalbaf
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran.
| | - Mahdi Ghorbani
- Biomedical Engineering and Medical Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, 19857-17443, Tehran, Iran.
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Kao YS, Lin KT. A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity. LA RADIOLOGIA MEDICA 2022; 127:754-762. [PMID: 35731375 PMCID: PMC9213649 DOI: 10.1007/s11547-022-01510-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/30/2022] [Indexed: 10/25/2022]
Abstract
INTRODUCTION According to the Chinese Health Commission guidelines, coronavirus disease 2019 (COVID-19) severity is classified as mild, moderate, severe, or critical. The mortality rate of COVID-19 is higher among patients with severe and critical diseases; therefore, early identification of COVID-19 prevents disease progression and improves patient survival. Computed tomography (CT) radiomics, as a machine learning method, provides an objective and mathematical evaluation of COVID-19 pneumonia. As CT-based radiomics research has recently focused on COVID-19 diagnosis and severity analysis, this meta-analysis aimed to investigate the predictive power of a CT-based radiomics model in determining COVID-19 severity. MATERIALS AND METHODS This study followed the diagnostic version of PRISMA guidelines. PubMed, Embase databases and the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews were searched to identify relevant articles in the meta-analysis from inception until July 16, 2021. The sensitivity and specificity were analyzed using forest plots. The overall predictive power was calculated using the summary receiver operating characteristic curve. The bias was evaluated using a funnel plot. The quality of the included literature was assessed using the radiomics quality score and quality assessment of diagnostic accuracy studies tool. RESULTS The radiomics quality scores ranged from 7 to 16 (achievable score: 2212 8 to 36). The pooled sensitivity and specificity were 0.800 (95% confidence interval [CI] 0.662-0.891) and 0.874 (95% CI 0.773-0.934), respectively. The pooled area under the receiver operating characteristic curve was 0.908. The quality assessment tool showed favorable results. CONCLUSION This meta-analysis demonstrated that CT-based radiomics models might be helpful for predicting the severity of COVID-19 pneumonia.
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Affiliation(s)
- Yung-Shuo Kao
- Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan
| | - Kun-Te Lin
- Department of Emergency Medicine, Changhua Christian Hospital, Changhua, Taiwan.
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Gillman AG, Lunardo F, Prinable J, Belous G, Nicolson A, Min H, Terhorst A, Dowling JA. Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review. Phys Eng Sci Med 2022; 45:13-29. [PMID: 34919204 PMCID: PMC8678975 DOI: 10.1007/s13246-021-01093-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/13/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVES To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work. METHODS The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test. FINDINGS Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified. INTERPRETATION A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools.
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Affiliation(s)
- Ashley G Gillman
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia.
| | - Febrio Lunardo
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
- College of Science and Engineering, James Cook University, Australian Tropical Science Innovation Precinct, Townsville, QLD, 4814, Australia
| | - Joseph Prinable
- ACRF Image X Institute, University of Sydney, Level 2, Biomedical Building (C81), 1 Central Ave, Australian Technology Park, Eveleigh, Sydney, NSW, 2015, Australia
| | - Gregg Belous
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Aaron Nicolson
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Hang Min
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Andrew Terhorst
- Data61, Commonwealth Scientific and Industrial Research Organisation, College Road, Sandy Bay, Hobart, TAS, 7005, Australia
| | - Jason A Dowling
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
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Stammes MA, Lee JH, Meijer L, Naninck T, Doyle-Meyers LA, White AG, Borish HJ, Hartman AL, Alvarez X, Ganatra S, Kaushal D, Bohm RP, le Grand R, Scanga CA, Langermans JAM, Bontrop RE, Finch CL, Flynn JL, Calcagno C, Crozier I, Kuhn JH. Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates. Trends Mol Med 2022; 28:123-142. [PMID: 34955425 PMCID: PMC8648672 DOI: 10.1016/j.molmed.2021.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/01/2021] [Accepted: 12/01/2021] [Indexed: 12/11/2022]
Abstract
Chest X-ray (CXR), computed tomography (CT), and positron emission tomography-computed tomography (PET-CT) are noninvasive imaging techniques widely used in human and veterinary pulmonary research and medicine. These techniques have recently been applied in studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-exposed non-human primates (NHPs) to complement virological assessments with meaningful translational readouts of lung disease. Our review of the literature indicates that medical imaging of SARS-CoV-2-exposed NHPs enables high-resolution qualitative and quantitative characterization of disease otherwise clinically invisible and potentially provides user-independent and unbiased evaluation of medical countermeasures (MCMs). However, we also found high variability in image acquisition and analysis protocols among studies. These findings uncover an urgent need to improve standardization and ensure direct comparability across studies.
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Affiliation(s)
- Marieke A Stammes
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands.
| | - Ji Hyun Lee
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - Lisette Meijer
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands
| | - Thibaut Naninck
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, 92260 Fontenay-aux-Roses, France
| | - Lara A Doyle-Meyers
- Tulane National Primate Research Center, Covington, LA 70433, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Alexander G White
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - H Jacob Borish
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Amy L Hartman
- Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Infectious Diseases and Microbiology, School of Public Health, University of Pittsburgh, Pitt Public Health, Pittsburgh, PA 15261, USA
| | - Xavier Alvarez
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
| | | | - Deepak Kaushal
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
| | - Rudolf P Bohm
- Tulane National Primate Research Center, Covington, LA 70433, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Roger le Grand
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, 92260 Fontenay-aux-Roses, France
| | - Charles A Scanga
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jan A M Langermans
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands; Department Population Health Sciences, Division of Animals in Science and Society, Faculty of Veterinary Medicine, Utrecht University, 3584 CL, Utrecht, The Netherlands
| | - Ronald E Bontrop
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands; Department of Biology, Theoretical Biology and Bioinformatics, Utrecht University, 3584 CH, Utrecht, The Netherlands
| | - Courtney L Finch
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - JoAnne L Flynn
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Claudia Calcagno
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - Ian Crozier
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
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10
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Pal A, Ali A, Young TR, Oostenbrink J, Prabhakar A, Prabhakar A, Deacon N, Arnold A, Eltayeb A, Yap C, Young DM, Tang A, Lakshmanan S, Lim YY, Pokarowski M, Kakodkar P. Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic. World J Radiol 2021; 13:258-282. [PMID: 34630913 PMCID: PMC8473437 DOI: 10.4329/wjr.v13.i9.258] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/28/2021] [Accepted: 08/04/2021] [Indexed: 02/06/2023] Open
Abstract
Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, over 103214008 cases have been reported, with more than 2231158 deaths as of January 31, 2021. Although the gold standard for diagnosis of this disease remains the reverse-transcription polymerase chain reaction of nasopharyngeal and oropharyngeal swabs, its false-negative rates have ignited the use of medical imaging as an important adjunct or alternative. Medical imaging assists in identifying the pathogenesis, the degree of pulmonary damage, and the characteristic features in each imaging modality. This literature review collates the characteristic radiographic findings of COVID-19 in various imaging modalities while keeping the preliminary focus on chest radiography, computed tomography (CT), and ultrasound scans. Given the higher sensitivity and greater proficiency in detecting characteristic findings during the early stages, CT scans are more reliable in diagnosis and serve as a practical method in following up the disease time course. As research rapidly expands, we have emphasized the CO-RADS classification system as a tool to aid in communicating the likelihood of COVID-19 suspicion among healthcare workers. Additionally, the utilization of other scoring systems such as MuLBSTA, Radiological Assessment of Lung Edema, and Brixia in this pandemic are reviewed as they integrate the radiographic findings into an objective scoring system to risk stratify the patients and predict the severity of disease. Furthermore, current progress in the utilization of artificial intelligence via radiomics is evaluated. Lastly, the lesson from the first wave and preparation for the second wave from the point of view of radiology are summarized.
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Affiliation(s)
- Aman Pal
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Abulhassan Ali
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Timothy R Young
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Juan Oostenbrink
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Akul Prabhakar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Amogh Prabhakar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Nina Deacon
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Amar Arnold
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Ahmed Eltayeb
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Charles Yap
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - David M Young
- Department of Computer Science, Yale University, New Haven, CO 06520, United States
| | - Alan Tang
- Department of Health Science, Duke University, Durham, NC 27708, United States
| | - Subramanian Lakshmanan
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Ying Yi Lim
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Martha Pokarowski
- The Hospital for Sick Kids, University of Toronto, Toronto M5S, Ontario, Canada
| | - Pramath Kakodkar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
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11
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Chen HJ, Mao L, Chen Y, Yuan L, Wang F, Li X, Cai Q, Qiu J, Chen F. Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia. BMC Infect Dis 2021; 21:931. [PMID: 34496794 PMCID: PMC8424152 DOI: 10.1186/s12879-021-06614-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 08/24/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19). METHODS In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS For the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model. CONCLUSION The machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.
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Affiliation(s)
- Hui Juan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Li Mao
- Deepwise AI Lab, Deepwise Inc., No. 8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China
| | - Yang Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Li Yuan
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Fei Wang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Xiuli Li
- Deepwise AI Lab, Deepwise Inc., No. 8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China
| | - Qinlei Cai
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Jie Qiu
- Department of Ultrasound, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), No. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, People's Republic of China.
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12
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Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, Valdesi C, Croce P, Mastrodicasa D, Villani M, Trebeschi S, Serafini FL, Rosa C, Cocco G, Luberti R, Conte S, Mazzamurro L, Mereu M, Patea RL, Panara V, Marinari S, Vecchiet J, Caulo M. Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease. Sci Rep 2021; 11:17237. [PMID: 34446812 PMCID: PMC8390673 DOI: 10.1038/s41598-021-96755-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 08/09/2021] [Indexed: 12/31/2022] Open
Abstract
Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden's test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden's index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10-7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.
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Affiliation(s)
- Andrea Delli Pizzi
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
| | - Piero Chiacchiaretta
- Center of Advanced Studies and Technology (CAST), "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
- Department of Psychological, Health and Territory Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
| | - Cristina Valdesi
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
| | | | - Michela Villani
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Consuelo Rosa
- Department of Radiation Oncology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via Dei Vestini, 66100, Chieti, Italy
| | - Giulio Cocco
- Unit of Ultrasound in Internal Medicine, Department of Medicine and Science of Aging, "G. D'Annunzio" University, Chieti, Italy
| | - Riccardo Luberti
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Sabrina Conte
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Lucia Mazzamurro
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Manuela Mereu
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Rosa Lucia Patea
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Valentina Panara
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Stefano Marinari
- Department of Pneumology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via Dei Vestini, 66100, Chieti, Italy
| | - Jacopo Vecchiet
- Clinic of Infectious Diseases, Department of Medicine and Science of Aging, University 'G. d'Annunzio' Chieti-Pescara, 66100, Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
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13
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Kao YS, Lin KT. A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia. Diagnostics (Basel) 2021; 11:991. [PMID: 34072573 PMCID: PMC8229671 DOI: 10.3390/diagnostics11060991] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/28/2021] [Accepted: 05/28/2021] [Indexed: 12/26/2022] Open
Abstract
INTRODUCTION Coronavirus disease 2019 (COVID-19) led to a global pandemic. Although reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is the gold standard for COVID-19 diagnosis, its sensitivity was found to not be high enough in many reports. As radiomics-based diagnosis research has recently emerged, we aimed to use computerized tomography (CT)-based radiomics models to differentiate COVID-19 pneumonia from other viral pneumonia infections. MATERIALS AND METHODS This study was performed according to the preferred reporting items for systematic review and meta-analysis diagnostic test accuracy studies (PRISMA-DTA) guidelines. The Pubmed, Cochrane, and Embase databases were searched. The pooled sensitivity and pooled specificity were calculated. A summary receiver operating characteristic (sROC) curve was constructed. The study quality was evaluated based on the radiomics quality score. RESULTS A total of 10,300 patients were involved in this meta-analysis. The radiomics quality score ranged from 13 to 16 (maximum score: 36). The pooled sensitivity was 0.885 (95% CI: 0.818-0.929), and the pooled specificity was 0.811 (95% CI: 0.667-0.902). The pooled AUC was 906. Conclusion: Our meta-analysis showed that CT-based radiomics feature models can successfully differentiate COVID-19 from other viral pneumonias.
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Affiliation(s)
- Yung-Shuo Kao
- Department of Radiation Oncology, China Medical University Hospital, Taichung 404, Taiwan;
| | - Kun-Te Lin
- Department of Emergency Medicine, Changhua Christian Hospital, Changhua 500, Taiwan
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14
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Xie CY, Pang CL, Chan B, Wong EYY, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers (Basel) 2021; 13:2469. [PMID: 34069367 PMCID: PMC8158761 DOI: 10.3390/cancers13102469] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 05/12/2021] [Accepted: 05/15/2021] [Indexed: 11/16/2022] Open
Abstract
Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.
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Affiliation(s)
- Chen-Yi Xie
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;
| | - Chun-Lap Pang
- Department of Radiology, The Christies’ Hospital, Manchester M20 4BX, UK;
- Division of Dentistry, School of Medical Sciences, University of Manchester, Manchester M15 6FH, UK
| | - Benjamin Chan
- Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; (B.C.); (E.Y.-Y.W.)
| | - Emily Yuen-Yuen Wong
- Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; (B.C.); (E.Y.-Y.W.)
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China;
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;
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15
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Moezzi M, Shirbandi K, Shahvandi HK, Arjmand B, Rahim F. The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. INFORMATICS IN MEDICINE UNLOCKED 2021; 24:100591. [PMID: 33977119 PMCID: PMC8099790 DOI: 10.1016/j.imu.2021.100591] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/17/2021] [Accepted: 04/29/2021] [Indexed: 01/08/2023] Open
Abstract
Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90–0.91), specificity was 0.91 (95% CI, 0.90–0.92) and the AUC was 0.96 (95% CI, 0.91–0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.88 (95% CI, 0.87–0.88) and the AUC was 0.96 (95% CI, 0.93–0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.95 (95% CI, 0.94–0.95) and the AUC was 0.97 (95% CI, 0.96–0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.
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Affiliation(s)
- Meisam Moezzi
- Department of Emergency Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Kiarash Shirbandi
- International Affairs Department (IAD), Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Hassan Kiani Shahvandi
- Allied Health Science, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Babak Arjmand
- Research Assistant Professor of Applied Cellular Sciences (By Research), Cellular and Molecular Institute, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Fakher Rahim
- Health Research Institute, Thalassemia and Hemoglobinopathies Research Centre, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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16
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Shiri I, Sorouri M, Geramifar P, Nazari M, Abdollahi M, Salimi Y, Khosravi B, Askari D, Aghaghazvini L, Hajianfar G, Kasaeian A, Abdollahi H, Arabi H, Rahmim A, Radmard AR, Zaidi H. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput Biol Med 2021; 132:104304. [PMID: 33691201 PMCID: PMC7925235 DOI: 10.1016/j.compbiomed.2021.104304] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 02/26/2021] [Accepted: 02/27/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. METHODS Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. RESULTS For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)). CONCLUSION Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Majid Sorouri
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Abdollahi
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Bardia Khosravi
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Leila Aghaghazvini
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Amir Kasaeian
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran,Hematology, Oncology and Stem Cell Transplantation Research Center, Research Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran,Inflammation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Kerman University of Medical Sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran,Corresponding author. Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland,Geneva University Neurocenter, Geneva University, Geneva, Switzerland,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark,Corresponding author. Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, CH-1211, Geneva, Switzerland
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Xiong F, Wang Y, You T, Li HH, Fu TT, Tan H, Huang W, Jiang Y. The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT. Medicine (Baltimore) 2021; 100:e25307. [PMID: 33761733 PMCID: PMC9282117 DOI: 10.1097/md.0000000000025307] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 03/06/2021] [Indexed: 01/05/2023] Open
Abstract
In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.
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Affiliation(s)
- Fei Xiong
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Ye Wang
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Tao You
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Han han Li
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Ting ting Fu
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Huibin Tan
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Weicai Huang
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
| | - Yuanliang Jiang
- Department of Radiology, PLA Central Theater General Hospital, Wuhan, Hubei, China
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