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Vásquez-Venegas C, Sotomayor CG, Ramos B, Castañeda V, Pereira G, Cabrera-Vives G, Härtel S. Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients. J Clin Med 2024; 13:5231. [PMID: 39274444 PMCID: PMC11396404 DOI: 10.3390/jcm13175231] [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: 06/18/2024] [Revised: 08/02/2024] [Accepted: 09/02/2024] [Indexed: 09/16/2024] Open
Abstract
Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.
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Affiliation(s)
- Constanza Vásquez-Venegas
- Department of Computer Science, Faculty of Engineering, University of Concepción, Concepción 4030000, Chile
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Camilo G Sotomayor
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Radiology Department, University of Chile Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Baltasar Ramos
- School of Medicine, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Víctor Castañeda
- Center of Medical Informatics and Telemedicine & National Center of Health Information Systems, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Department of Medical Technology, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Gonzalo Pereira
- Radiology Department, University of Chile Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Guillermo Cabrera-Vives
- Department of Computer Science, Faculty of Engineering, University of Concepción, Concepción 4030000, Chile
| | - Steffen Härtel
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Center of Medical Informatics and Telemedicine & National Center of Health Information Systems, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- National Center for Health Information Systems, Santiago 8380453, Chile
- Center of Mathematical Modelling, University of Chile, Santiago 8380453, Chile
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Kooner HK, Sharma M, McIntosh MJ, Dhaliwal I, Nicholson JM, Kirby M, Svenningsen S, Parraga G. 129Xe MRI Ventilation Textures and Longitudinal Quality-of-Life Improvements in Long-COVID. Acad Radiol 2024; 31:3825-3836. [PMID: 38637239 DOI: 10.1016/j.acra.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/13/2024] [Accepted: 03/15/2024] [Indexed: 04/20/2024]
Abstract
RATIONALE AND OBJECTIVES It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129Xe MRI reflects airway dysfunction, measured using ventilation defect percent (VDP) and in long-COVID patients, MRI VDP was abnormal, suggestive of airways disease. While MRI VDP and quality-of-life improved 15-month post-COVID infection, both remained abnormal. To better understand the relationship of airways disease and quality-of-life improvements in patients with long-COVID, we extracted 129Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection. MATERIALS AND METHODS Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract 129Xe ventilation MRI texture features, which were ranked using a Random-Forest classifier. Top-ranking features were used in classification models to dichotomize patients based on St. George's Respiratory Questionnaire (SGRQ) score improvement greater than the minimal-clinically-important-difference (MCID). Classification performance was evaluated using the area under the receiver-operator-characteristic-curve (AUC), sensitivity, and specificity. RESULTS 120 texture features were extracted from 129Xe ventilation MRI in 44 long-COVID participants (54 ± 14 years), including 30 (52 ± 12 years) with ΔSGRQ≥MCID and 14 (58 ± 18 years) with ΔSGRQ CONCLUSION A machine learning model exclusively trained on 129Xe MRI ventilation textures explained improved SGRQ-scores 12 months later, and outperformed clinical models. Their unique spatial-intensity information helps build our understanding about long-COVID airway dysfunction.
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Affiliation(s)
- Harkiran K Kooner
- Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada
| | - Maksym Sharma
- Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada
| | - Marrissa J McIntosh
- Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada
| | - Inderdeep Dhaliwal
- Division of Respirology, Department of Medicine, Western University, London, Canada
| | - J Michael Nicholson
- Division of Respirology, Department of Medicine, Western University, London, Canada
| | - Miranda Kirby
- Department of Physics, Toronto Metropolitan University, Toronto, Canada
| | - Sarah Svenningsen
- Division of Respirology, Department of Medicine, McMaster University and Firestone Institute for Respiratory Health, St. Joseph's Health Care, Hamilton, Canada
| | - Grace Parraga
- Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada; Division of Respirology, Department of Medicine, Western University, London, Canada.
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Jiang J, Chen S, Zhang S, Zeng Y, Liu J, Lei W, Liu X, Chen X, Xiao Q. A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia. BMC Med Imaging 2024; 24:202. [PMID: 39103756 DOI: 10.1186/s12880-024-01370-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 07/18/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Community-Acquired Pneumonia (CAP) remains a significant global health concern, with a subset of cases progressing to Severe Community-Acquired Pneumonia (SCAP). This study aims to develop and validate a CT-based radiomics model for the early detection of SCAP to enable timely intervention and improve patient outcomes. METHODS A retrospective study was conducted on 115 CAP and SCAP patients at Southern Medical University Shunde Hospital from January to December 2021. Using the Pyradiomics package, 107 radiomic features were extracted from CT scans, refined via intra-class and inter-class correlation coefficients, and narrowed down using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The predictive performance of the radiomics-based model was assessed through receiver operating characteristic (ROC) analysis, employing machine learning classifiers such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), trained and validated on datasets split 7:3, with a training set (n = 80) and a validation set (n = 35). RESULTS The radiomics model exhibited robust predictive performance, with the RF classifier achieving superior precision and accuracy compared to LR, SVM, and KNN classifiers. Specifically, the RF classifier demonstrated a precision of 0.977 (training set) and 0.833 (validation set), as well as an accuracy of 0.925 (training set) and 0.857 (validation set), suggesting its superior performance in both metrics. Decision Curve Analysis (DCA) was utilized to evaluate the clinical efficacy of the RF classifier, demonstrating a favorable net benefit within the threshold ranges of 0.1 to 0.8 for the training set and 0.2 to 0.7 for the validation set. CONCLUSIONS The radiomics model developed in this study shows promise for early SCAP detection and can improve clinical decision-making.
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Affiliation(s)
- Jia Jiang
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Siqin Chen
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Shaofeng Zhang
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Yaling Zeng
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Jiayi Liu
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Wei Lei
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China
| | - Xiang Liu
- Departments of Hematology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China.
| | - Xin Chen
- Pulmonary and Critical Care Medicine, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510280, China.
| | - Qiang Xiao
- Pulmonary and Critical Care Medicine, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), No.1, Jiazi Road, Lunjiao Street, Shunde District, Foshan, Guangdong, 528300, China.
- Pulmonary and Critical Care Medicine, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510280, China.
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Netprasert SA, Khongwirotphan S, Seangsawang R, Patipipittana S, Jantarabenjakul W, Puthanakit T, Chintanapakdee W, Sriswasdi S, Rakvongthai Y. Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics. Radiol Phys Technol 2024; 17:467-475. [PMID: 38668939 DOI: 10.1007/s12194-024-00803-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 04/10/2024] [Accepted: 04/12/2024] [Indexed: 05/27/2024]
Abstract
The objective is to evaluate the performance of blood test results, radiomics, and a combination of the two data types on the prediction of the 24-h oxygenation support need for the Coronavirus disease 2019 (COVID-19) patients. In this retrospective cohort study, COVID-19 patients with confirmed real-time reverse transcription-polymerase chain reaction assay (RT-PCR) test results between February 2020 and August 2021 were investigated. Initial blood cell counts, chest radiograph, and the status of oxygenation support used within 24 h were collected (n = 290; mean age, 45 ± 19 years; 125 men). Radiomics features from six lung zones were extracted. Logistic regression and random forest models were developed using the clinical-only, radiomics-only, and combined data. Ten repeats of fivefold cross-validation with bootstrapping were used to identify the input features and models with the highest area under the receiver operating characteristic curve (AUC). Higher AUCs were achieved when using only radiomics features compared to using only clinical features (0.94 ± 0.03 vs. 0.88 ± 0.04). The best combined model using both radiomics and clinical features achieved highest in the cross-validation (0.95 ± 0.02) and test sets (0.96 ± 0.02). In comparison, the best clinical-only model yielded AUCs of 0.88 ± 0.04 in cross-validation and 0.89 ± 0.03 in test set. Both radiomics and clinical data can be used to predict 24-h oxygenation support need for COVID-19 patients with AUC > 0.88. Moreover, the combination of both data types further improved the performance.
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Affiliation(s)
- Sa-Angtip Netprasert
- Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn, University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sararas Khongwirotphan
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Roongprai Seangsawang
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Supanuch Patipipittana
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Watsamon Jantarabenjakul
- Division of Infectious Diseases, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence for Pediatric Infectious Diseases and Vaccines, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Emerging Infectious Diseases Clinical Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Thanyawee Puthanakit
- Division of Infectious Diseases, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence for Pediatric Infectious Diseases and Vaccines, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Wariya Chintanapakdee
- Department of Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Sira Sriswasdi
- Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand.
- Center for Artificial Intelligence in Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
| | - Yothin Rakvongthai
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
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Henao JAG, Depotter A, Bower DV, Bajercius H, Todorova PT, Saint-James H, de Mortanges AP, Barroso MC, He J, Yang J, You C, Staib LH, Gange C, Ledda RE, Caminiti C, Silva M, Cortopassi IO, Dela Cruz CS, Hautz W, Bonel HM, Sverzellati N, Duncan JS, Reyes M, Poellinger A. A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans. Invest Radiol 2023; 58:882-893. [PMID: 37493348 PMCID: PMC10662611 DOI: 10.1097/rli.0000000000001005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 05/26/2023] [Indexed: 07/27/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans. MATERIALS AND METHODS The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19-induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19-positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19-positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion. RESULTS AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95. CONCLUSIONS A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.
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Elmahdy M, Sebro R. Radiomics analysis in medical imaging research. J Med Radiat Sci 2023; 70:3-7. [PMID: 36762402 PMCID: PMC9977659 DOI: 10.1002/jmrs.662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/21/2023] [Indexed: 02/11/2023] Open
Abstract
This article discusses the current research in the field of radiomics in medical imaging with emphasis on its role in fighting coronavirus disease 2019 (COVID-19). This article covers the building of radiomic models in a simple straightforward manner, while discussing radiomic models potential to help us face this pandemic.
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Affiliation(s)
- Mahmoud Elmahdy
- Department of RadiologyMayo ClinicJacksonvilleFloridaUSA,Center for Augmented IntelligenceMayo ClinicJacksonvilleFloridaUSA
| | - Ronnie Sebro
- Department of RadiologyMayo ClinicJacksonvilleFloridaUSA,Center for Augmented IntelligenceMayo ClinicJacksonvilleFloridaUSA,Department of Orthopedic SurgeryMayo ClinicJacksonvilleFloridaUSA,Department of BiostatisticsCentre for Quantitative Health SciencesJacksonvilleFloridaUSA
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7
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Aminu M, Yadav D, Hong L, Young E, Edelkamp P, Saad M, Salehjahromi M, Chen P, Sujit SJ, Chen MM, Sabloff B, Gladish G, de Groot PM, Godoy MCB, Cascone T, Vokes NI, Zhang J, Brock KK, Daver N, Woodman SE, Tawbi HA, Sheshadri A, Lee JJ, Jaffray D, Wu CC, Chung C, Wu J. Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19. Cancers (Basel) 2022; 15:275. [PMID: 36612278 PMCID: PMC9818576 DOI: 10.3390/cancers15010275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVES Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. METHODS We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. RESULTS These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. CONCLUSIONS Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.
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Affiliation(s)
- Muhammad Aminu
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Divya Yadav
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Lingzhi Hong
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Elliana Young
- Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Paul Edelkamp
- Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Maliazurina Saad
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Morteza Salehjahromi
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Pingjun Chen
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Sheeba J. Sujit
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Melissa M. Chen
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Bradley Sabloff
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Gregory Gladish
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Patricia M. de Groot
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Myrna C. B. Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Kristy K. Brock
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Naval Daver
- Department of Leukemia, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Scott E. Woodman
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Hussein A. Tawbi
- Department of Melanoma Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - David Jaffray
- Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX 77054, USA
| | | | - Carol C. Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Caroline Chung
- Office of the Chief Data Officer, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
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Shiri I, Mostafaei S, Haddadi Avval A, Salimi Y, Sanaat A, Akhavanallaf A, Arabi H, Rahmim A, Zaidi H. High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms. Sci Rep 2022; 12:14817. [PMID: 36050434 PMCID: PMC9437017 DOI: 10.1038/s41598-022-18994-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/23/2022] [Indexed: 12/11/2022] Open
Abstract
We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805-0.887) and 0.807 (0.752-0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | | | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 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
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 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, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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9
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Waldman CE, Hermel M, Hermel JA, Allinson F, Pintea MN, Bransky N, Udoh E, Nicholson L, Robinson A, Gonzalez J, Suhar C, Nayak K, Wesbey G, Bhavnani SP. Artificial intelligence in healthcare: a primer for medical education in radiomics. Per Med 2022; 19:445-456. [PMID: 35880428 DOI: 10.2217/pme-2022-0014] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.
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Affiliation(s)
- Carly E Waldman
- Division of Internal Medicine, Scripps Clinic, La Jolla, CA 92037, USA
| | - Melody Hermel
- Division of Cardiology, Scripps Clinic, La Jolla, CA 92037, USA
| | - Jonathan A Hermel
- Medical Student, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Francis Allinson
- Division of Internal Medicine, Scripps Clinic, La Jolla, CA 92037, USA
| | - Mark N Pintea
- Medical Student, California University of Science & Medicine, Colton, CA 95757, USA
| | - Natalie Bransky
- Medical Student, University of California, San Diego School of Medicine, San Diego, CA 92037, USA
| | - Emem Udoh
- Division of Internal Medicine, Scripps Clinic, La Jolla, CA 92037, USA
| | - Laura Nicholson
- Associate Program Director for Resident Research, Division of Internal Medicine, Scripps Clinic, La Jolla, CA 92037, USA
| | - Austin Robinson
- Advanced Cardiovascular Imaging, Divisions of Cardiology & Radiology, Scripps Clinic, La Jolla, CA 92037, USA
| | - Jorge Gonzalez
- Advanced Cardiovascular Imaging, Divisions of Cardiology & Radiology, Scripps Clinic, La Jolla, CA 92037, USA
| | - Christopher Suhar
- Fellowship Program Co-Director, Division of Cardiology, Scripps Clinic, La Jolla, CA 92037, USA
| | - Keshav Nayak
- Director, Structural Heart Program, Division of Cardiology, Scripps Mercy, San Diego, CA 92037, USA
| | - George Wesbey
- Advanced Cardiovascular Imaging, Divisions of Cardiology & Radiology, Scripps Clinic, La Jolla, CA 92037, USA
| | - Sanjeev P Bhavnani
- Principal Investigator Healthcare Innovation & Practice Transformation Laboratory, Division of Cardiology, Scripps Clinic, La Jolla, CA 92037, USA
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10
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Bermejo-Peláez D, San José Estépar R, Fernández-Velilla M, Palacios Miras C, Gallardo Madueño G, Benegas M, Gotera Rivera C, Cuerpo S, Luengo-Oroz M, Sellarés J, Sánchez M, Bastarrika G, Peces Barba G, Seijo LM, Ledesma-Carbayo MJ. Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT. Sci Rep 2022; 12:9387. [PMID: 35672437 PMCID: PMC9172615 DOI: 10.1038/s41598-022-13298-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/12/2022] [Indexed: 12/15/2022] Open
Abstract
The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score.
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Affiliation(s)
- David Bermejo-Peláez
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av Complutense 30, 28040, Madrid, Spain
- CIBER-BBN, Madrid, Spain
- , Spotlab, Madrid, Spain
| | | | | | | | | | | | | | - Sandra Cuerpo
- Hospital Clinic de Barcelona-IDIBPAS, Barcelona, Spain
- CIBER-ES, Madrid, Spain
| | | | - Jacobo Sellarés
- Hospital Clinic de Barcelona-IDIBPAS, Barcelona, Spain
- CIBER-ES, Madrid, Spain
- Universidad de Vic (UVIC), Vic, Spain
| | | | | | - German Peces Barba
- Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
- CIBER-ES, Madrid, Spain
| | - Luis M Seijo
- Clínica Universidad de Navarra, Pamplona, Spain
- CIBER-ES, Madrid, Spain
| | - María J Ledesma-Carbayo
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av Complutense 30, 28040, Madrid, Spain.
- CIBER-BBN, Madrid, Spain.
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11
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Gülbay M, Baştuğ A, Özkan E, Öztürk BY, Mendi BAR, Bodur H. Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care? BMC Med Imaging 2022; 22:110. [PMID: 35672719 PMCID: PMC9172094 DOI: 10.1186/s12880-022-00833-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 05/27/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. METHODS Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. RESULTS No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. CONCLUSION By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.
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Affiliation(s)
- Mutlu Gülbay
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey.
| | - Aliye Baştuğ
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Turkey, Gülhane Faculty of Medicine, Ankara City Hospital, Ankara, Turkey
| | - Erdem Özkan
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey
| | - Büşra Yüce Öztürk
- Department of Clinical Microbiology and Infectious Diseases, Ankara City Hospital, Ankara, Turkey
| | - Bökebatur Ahmet Raşit Mendi
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey
| | - Hürrem Bodur
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Turkey, Gülhane Faculty of Medicine, Ankara City Hospital, Ankara, Turkey
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12
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Bakhtiarvand N, Khashei M, Mahnam M, Hajiahmadi S. A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients. BMC Med Inform Decis Mak 2022; 22:123. [PMID: 35513811 PMCID: PMC9069125 DOI: 10.1186/s12911-022-01861-2] [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: 08/21/2021] [Accepted: 04/25/2022] [Indexed: 11/28/2022] Open
Abstract
Background Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention. Methods This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients’ outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model. Results The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88–0.98) and AUC 0.90 (95% CI 0.85–0.96) for classic regression models, respectively. Conclusions Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.
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Affiliation(s)
- Negar Bakhtiarvand
- Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | - Mehdi Khashei
- Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.,Center for Optimization and Intelligent Decision Making in Healthcare Systems (COID-Health), Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | - Mehdi Mahnam
- Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran. .,Center for Optimization and Intelligent Decision Making in Healthcare Systems (COID-Health), Isfahan University of Technology, Isfahan, 84156-83111, Iran.
| | - Somayeh Hajiahmadi
- Department of Radiology, Isfahan University of Medical Sciences, Isfahan, Iran
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13
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Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094493] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
(1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in chest CT images was investigated regarding the ability to predict the mortality of SARS-CoV-2 patients. (2) Methods: A total of 179 RFs extracted from 436 chest CT images of SARS-CoV-2 patients, and 8 clinical and 6 radiological variables, were used to train and evaluate three ML methods (Least Absolute Shrinkage and Selection Operator [LASSO] regularized regression, Random Forest Classifier [RFC], and the Fully connected Neural Network [FcNN]) for their ability to predict mortality using the Area Under the Curve (AUC) of Receiver Operator characteristic (ROC) Curves. These three groups of variables were used separately and together as input for constructing and comparing the final performance of ML models. (3) Results: All the ML models using only RFs achieved an informative level regarding predictive ability, outperforming radiological assessment, without however reaching the performance obtained with ML based on clinical variables. The LASSO regularized regression and the FcNN performed equally, both being superior to the RFC. (4) Conclusions: Radiomic features based on semi-automatically segmented CT images and ML approaches can aid in identifying patients with a high risk of mortality, allowing a fast, objective, and generalizable method for improving prognostic assessment by providing a second expert opinion that outperforms human evaluation.
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14
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Vaidya P, Alilou M, Hiremath A, Gupta A, Bera K, Furin J, Armitage K, Gilkeson R, Yuan L, Fu P, Lu C, Ji M, Madabhushi A. An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study. FRONTIERS IN RADIOLOGY 2022; 2:781536. [PMID: 36437821 PMCID: PMC9696643 DOI: 10.3389/fradi.2022.781536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models-radiomics (MRM), clinical (MCM), and combined clinical-radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. METHODS We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D 1 T ( N = 473 ) , and 40% test set D 1 V ( N = 314 ) . The patients from institution-2 were used for an independent validation test set D 2 V ( N = 110 ) . A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D 1 T . RESULTS The three out of the top five features identified using D 1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709-0.799) on D 1 T , 0.836 on D 1 V , and 0.748 D 2 V . The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743-0.825) on D 1 T , 0.813 on D 1 V , and 0.688 on D 2 V . Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774-0.853) on D 1 T , 0.847 on D 1 V , and 0.771 on D 2 V . The MRCM had an overall improvement in the performance of ~5.85% ( D 1 T : p = 0.0031; D 1 V p = 0.0165; D 2 V : p = 0.0369) over MCM. CONCLUSION The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.
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Affiliation(s)
- Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Amogh Hiremath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY, United States
| | - Jennifer Furin
- Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Keith Armitage
- Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Robert Gilkeson
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Lei Yuan
- Department of Information Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mengyao Ji
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
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15
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Nagaraj Y, de Jonge G, Andreychenko A, Presti G, Fink MA, Pavlov N, Quattrocchi CC, Morozov S, Veldhuis R, Oudkerk M, van Ooijen PMA. Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting. Eur Radiol 2022; 32:6384-6396. [PMID: 35362751 PMCID: PMC8973680 DOI: 10.1007/s00330-022-08730-6] [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: 12/02/2021] [Revised: 02/13/2022] [Accepted: 03/08/2022] [Indexed: 11/29/2022]
Abstract
Objective To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. Methods This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. Results The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. Conclusion Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. Keypoints • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08730-6.
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Affiliation(s)
- Yeshaswini Nagaraj
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands. .,Machine Learning Lab, DASH, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Gonda de Jonge
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anna Andreychenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Gabriele Presti
- Unit of Diagnostic Imaging and Interventional Radiology, Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Matthias A Fink
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg, member of the German Center for Lung Research, Heidelberg, Germany
| | - Nikolay Pavlov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Carlo C Quattrocchi
- Unit of Diagnostic Imaging and Interventional Radiology, Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Sergey Morozov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Healthcare Department, Moscow, Russia
| | - Raymond Veldhuis
- Faculty of Electrical Engineering, Mathematics Computer Science (EWI), Data management Biometrics (DMB), University of Twente, Enschede, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands.,Institute for DiagNostic Accuracy Research, Groningen, The Netherlands
| | - Peter M A van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.,Machine Learning Lab, DASH, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Cai S, Chen Y, Zhao S, He D, Li Y, Xiong N, Li Z, Hu S. Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images. Eur Radiol 2022; 32:4760-4770. [PMID: 35094118 PMCID: PMC8800423 DOI: 10.1007/s00330-021-08533-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/26/2021] [Accepted: 07/31/2021] [Indexed: 01/08/2023]
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Yamada D, Ohde S, Imai R, Ikejima K, Matsusako M, Kurihara Y. Visual classification of three computed tomography lung patterns to predict prognosis of COVID-19: a retrospective study. BMC Pulm Med 2022; 22:1. [PMID: 34980061 PMCID: PMC8721943 DOI: 10.1186/s12890-021-01813-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/22/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. METHODS This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission. RESULTS Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration. CONCLUSIONS Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.
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Affiliation(s)
- Daisuke Yamada
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan.
| | - Sachiko Ohde
- Graduate School of Public Health, St. Luke's International University, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| | - Ryosuke Imai
- Department of Pulmonary Medicine, Thoracic Center, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| | - Kengo Ikejima
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| | - Masaki Matsusako
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
| | - Yasuyuki Kurihara
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-cho, Chuo-ku, Tokyo, 104-8560, Japan
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Tong X, Li J. Noninvasively predict the micro-vascular invasion and histopathological grade of hepatocellular carcinoma with CT-derived radiomics. Eur J Radiol Open 2022; 9:100424. [PMID: 35600083 PMCID: PMC9120240 DOI: 10.1016/j.ejro.2022.100424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 11/01/2022] Open
Abstract
Objectives Methods Results Conclusion
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Yu Q, Liu J, Lin H, Lei P, Fan B. Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith. Int J Clin Pract 2022; 2022:5478908. [PMID: 36474549 PMCID: PMC9678460 DOI: 10.1155/2022/5478908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/24/2022] [Accepted: 11/05/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. METHOD Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. RESULTS The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96-1.00) and testing set (AUC = 0.98; 95%CI: 0.95-1.00). The decision curve thus demonstrated the clinical application of the radiomics model. CONCLUSIONS The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.
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Affiliation(s)
- Qiuyue Yu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha 410005, China
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550000, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
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20
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Varghese BA, Shin H, Desai B, Gholamrezanezhad A, Lei X, Perkins M, Oberai A, Nanda N, Cen S, Duddalwar V. Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs. Br J Radiol 2021; 94:20210221. [PMID: 34520246 PMCID: PMC9328073 DOI: 10.1259/bjr.20210221] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Objectives For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. Methods In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. Results Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. Conclusions: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. Advances in knowledge We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.
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Affiliation(s)
| | - Heeseop Shin
- Keck School of Medicine, University of Southern California, CA, USA
| | - Bhushan Desai
- Keck School of Medicine, University of Southern California, CA, USA
| | | | - Xiaomeng Lei
- Keck School of Medicine, University of Southern California, CA, USA
| | - Melissa Perkins
- Keck School of Medicine, University of Southern California, CA, USA
| | - Assad Oberai
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Neha Nanda
- Keck School of Medicine, University of Southern California, CA, USA
| | - Steven Cen
- Keck School of Medicine, University of Southern California, CA, USA
| | - Vinay Duddalwar
- Keck School of Medicine, University of Southern California, CA, USA
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21
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Uemura T, Näppi JJ, Watari C, Hironaka T, Kamiya T, Yoshida H. Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT. Med Image Anal 2021; 73:102159. [PMID: 34303892 PMCID: PMC8272947 DOI: 10.1016/j.media.2021.102159] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 06/27/2021] [Accepted: 06/29/2021] [Indexed: 12/23/2022]
Abstract
Because of the rapid spread and wide range of the clinical manifestations of the coronavirus disease 2019 (COVID-19), fast and accurate estimation of the disease progression and mortality is vital for the management of the patients. Currently available image-based prognostic predictors for patients with COVID-19 are largely limited to semi-automated schemes with manually designed features and supervised learning, and the survival analysis is largely limited to logistic regression. We developed a weakly unsupervised conditional generative adversarial network, called pix2surv, which can be trained to estimate the time-to-event information for survival analysis directly from the chest computed tomography (CT) images of a patient. We show that the performance of pix2surv based on CT images significantly outperforms those of existing laboratory tests and image-based visual and quantitative predictors in estimating the disease progression and mortality of COVID-19 patients. Thus, pix2surv is a promising approach for performing image-based prognostic predictions.
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Affiliation(s)
- Tomoki Uemura
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan
| | - Janne J Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Chinatsu Watari
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Toru Hironaka
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Tohru Kamiya
- Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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22
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Zhang M, Zeng X, Huang C, Liu J, Liu X, Xie X, Wang R. An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators. Int J Med Inform 2021; 154:104545. [PMID: 34464848 PMCID: PMC8353975 DOI: 10.1016/j.ijmedinf.2021.104545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 07/07/2021] [Accepted: 07/29/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND This study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia. METHODS COVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram. RESULTS Two clinical indicators that were age and chronic lung disease or asthma and 21 radiomics features were selected to build the radiomics nomogram. The radiomics nomogram yielded an Area Under The Curve1 (AUC) of 0.88 and accuracy of 0.80 in the training set, an AUC of 0.85 and accuracy of 0.77 in internal testing validation set and an AUC of 0.84 and accuracy of 0.75 in independent external validation set. The performance of radiomics nomogram was better than clinical model (AUC = 0.77, p < 0.001) and radiomics model (AUC = 0.72, p = 0.025) in independent external validation set. CONCLUSIONS The radiomics nomogram may be used to assess the deterioration of COVID-19 pneumonia.
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Affiliation(s)
- Mudan Zhang
- Medical College of Guizhou University, Guiyang, Guizhou Province 550000, China,Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s Hospital, Guiyang 550002, China
| | - Xianchun Zeng
- Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s Hospital, Guiyang 550002, China
| | - Chencui Huang
- AI Lab, Deepwise & League of PhD Technology Co.LTD, Beijing, China
| | - Jun Liu
- Department of Radiology, the Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, China,Department of Radiology Quality Control Center, Changsha, Hunan Province 410011, China
| | - Xinfeng Liu
- Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s Hospital, Guiyang 550002, China
| | - Xingzhi Xie
- Department of Radiology, the Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Rongpin Wang
- Medical College of Guizhou University, Guiyang, Guizhou Province 550000, China,Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s Hospital, Guiyang 550002, China,Corresponding author at: Department of Medical Imaging, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s Hospital. No. 83 Zhongshan East Road, Nan Ming District, Guiyang, Guizhou Province 550002, China
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23
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Alqahtani M, Abbas M, Alqahtani A, Alshahrani M, Alkulib A, Alelyani M, Almarhaby A. A Novel Multicolour-thresholding Auto-detection Method to Detect the Location and Severity of Inflammation in Confirmed SARS-COV-2 Cases Using Chest X-ray Images. Curr Med Imaging 2021; 18:563-569. [PMID: 34515009 DOI: 10.2174/1573405617666210910150119] [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: 03/02/2021] [Revised: 07/14/2021] [Accepted: 07/27/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVES Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause acute respiratory distress (ARD). Medical imaging has the potential to help identify, detect, and quantify the severity of this infection. This work seeks to develop a novel auto-detection technique for verified COVID-19 cases that can detect aberrant alterations in traditional X-ray pictures. METHODS Nineteen separate-colored layers were created from X ray scans of patients diagnosed with COVID-19. Each layer represents objects that have a similar contrast and can be represented by a single color. On a single layer, objects with similar contrasts are formed. A single color image was created by extracting all the objects from all the layers. The prototype model could recognize a wide range of abnormal changes in the image texture based on color differentiation. This was true even when the contrast values of the detected uncleared abnormalities varied a little. RESULTS The results indicate that the proposed novel method is 91% accurate in detecting and grading COVID-19 lung infection when compared to the opinions of three experienced radiologists evaluating chest X-ray images. Additionally, the method can be used to determine the infection site and severity of the disease by categorizing the X-rays into five severity levels. CONCLUSION By comparing affected tissue to healthy tissue, the proposed COVID-19 auto-detection method can identify locations and indicate the severity of the disease, as well as predict where the disease may spread.
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Affiliation(s)
- Mohammed Alqahtani
- Department of Radiological Sciences, College of Applied Medical Sciences, King Khalid University, Abha. Saudi Arabia
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, Abha. Saudi Arabia
| | - Ali Alqahtani
- Medical and Clinical Affairs Department, King Faisal Medical City, Abha. Saudi Arabia
| | - Mohammad Alshahrani
- Department of Radiological Sciences, College of Applied Medical Sciences, King Khalid University, Abha. Saudi Arabia
| | - Abdulhadi Alkulib
- Medical and Clinical Affairs Department, King Faisal Medical City, Abha. Saudi Arabia
| | - Magbool Alelyani
- Department of Radiological Sciences, College of Applied Medical Sciences, King Khalid University, Abha. Saudi Arabia
| | - Awad Almarhaby
- Radiology Department, King Fahd General Hospital, Postcode: 23325, Jeddah. Saudi Arabia
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24
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Morris MF, Pershad Y, Kang P, Ridenour L, Lavon B, Lanclus M, Godon R, De Backer J, Glassberg MK. Altered pulmonary blood volume distribution as a biomarker for predicting outcomes in COVID-19 disease. Eur Respir J 2021; 58:2004133. [PMID: 33632795 PMCID: PMC7908189 DOI: 10.1183/13993003.04133-2020] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/03/2021] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Evidence suggests that vascular inflammation and thrombosis may be important drivers of poor clinical outcomes in patients with COVID-19. We hypothesised that a significant decrease in the percentage of blood volume in vessels with a cross-sectional area between 1.25 and 5 mm2 relative to the total pulmonary blood volume (BV5%) on chest computed tomography (CT) in COVID-19 patients is predictive of adverse clinical outcomes. METHODS We performed a retrospective analysis of chest CT scans from 10 hospitals across two US states in 313 COVID-19-positive and 195 COVID-19-negative patients seeking acute medical care. RESULTS BV5% was predictive of outcomes in COVID-19 patients in a multivariate model, with a BV5% threshold below 25% associated with OR 5.58 for mortality, OR 3.20 for intubation and OR 2.54 for the composite of mortality or intubation. A model using age and BV5% had an area under the receiver operating characteristic curve of 0.85 to predict the composite of mortality or intubation in COVID-19 patients. BV5% was not predictive of clinical outcomes in patients without COVID-19. CONCLUSIONS The data suggest BV5% as a novel biomarker for predicting adverse outcomes in patients with COVID-19 seeking acute medical care.
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Affiliation(s)
- Michael F Morris
- Dept of Radiology, Banner - University Medical Center Phoenix, Phoenix, AZ, USA
- Dept of Medicine, Banner - University Medical Center Phoenix, Phoenix, AZ, USA
| | - Yash Pershad
- Dept of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Paul Kang
- Dept of Biostatistics, University of Arizona College of Public Health, Phoenix, AZ, USA
| | - Lauren Ridenour
- Dept of Medicine, Banner - University Medical Center Phoenix, Phoenix, AZ, USA
| | | | | | | | | | - Marilyn K Glassberg
- Dept of Medicine, Banner - University Medical Center Phoenix, Phoenix, AZ, USA
- Division of Pulmonary Medicine, Critical Care, and Sleep Medicine, University of Arizona College of Medicine - Phoenix, Phoenix, AZ, USA
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25
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Aljouie AF, Almazroa A, Bokhari Y, Alawad M, Mahmoud E, Alawad E, Alsehawi A, Rashid M, Alomair L, Almozaai S, Albesher B, Alomaish H, Daghistani R, Alharbi NK, Alaamery M, Bosaeed M, Alshaalan H. Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning. J Multidiscip Healthc 2021; 14:2017-2033. [PMID: 34354361 PMCID: PMC8331117 DOI: 10.2147/jmdh.s322431] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/15/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. OBJECTIVE The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). METHODS Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. RESULTS In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. CONCLUSION The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.
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Affiliation(s)
- Abdulrhman Fahad Aljouie
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Ahmed Almazroa
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Imaging Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Yahya Bokhari
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Mohammed Alawad
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Ebrahim Mahmoud
- Department of Medicine, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Eman Alawad
- Department of Medicine, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Ali Alsehawi
- Department of Medical Imaging, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Mamoon Rashid
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Lamya Alomair
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Shahad Almozaai
- College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Bedoor Albesher
- College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Hassan Alomaish
- Department of Medical Imaging, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Rayyan Daghistani
- Department of Medical Imaging, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Naif Khalaf Alharbi
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Infectious Disease Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Manal Alaamery
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Developmental Medicine, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- KACST-BWH Center of Excellence for Biomedicine, Joint Centers of Excellence Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
- King Abdulaziz City for Science and Technology (KACST)-Saudi Human Genome Satellite Lab at Abdulaziz Medical City, Ministry of National Guard Health Affairs (MNGHA), Riyadh, Saudi Arabia
| | - Mohammad Bosaeed
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Imaging Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Medicine, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Hesham Alshaalan
- Department of Medical Imaging, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
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Yousefi B, Kawakita S, Amini A, Akbari H, Advani SM, Akhloufi M, Maldague XPV, Ahadian S. Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics. J Clin Med 2021; 10:3100. [PMID: 34300266 PMCID: PMC8304336 DOI: 10.3390/jcm10143100] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/01/2021] [Accepted: 07/07/2021] [Indexed: 12/31/2022] Open
Abstract
The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson-Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4-74.4%) for multiclass and 89.6% (88.4-90.7%) for binary-class classification.
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Affiliation(s)
- Bardia Yousefi
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Satoru Kawakita
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
| | - Arya Amini
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA;
| | - Hamed Akbari
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Shailesh M. Advani
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
| | - Moulay Akhloufi
- Department of Computer Science, Perception Robotics and Intelligent Machines (PRIME) Research Group, University of Moncton, New Brunswick, NB E1A 3E9, Canada;
| | - Xavier P. V. Maldague
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Samad Ahadian
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
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Wang R, Jiao Z, Yang L, Choi JW, Xiong Z, Halsey K, Tran TML, Pan I, Collins SA, Feng X, Wu J, Chang K, Shi LB, Yang S, Yu QZ, Liu J, Fu FX, Jiang XL, Wang DC, Zhu LP, Yi XP, Healey TT, Zeng QH, Liu T, Hu PF, Huang RY, Li YH, Sebro RA, Zhang PJL, Wang J, Atalay MK, Liao WH, Fan Y, Bai HX. Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data. Eur Radiol 2021; 32:205-212. [PMID: 34223954 PMCID: PMC8256200 DOI: 10.1007/s00330-021-08049-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/11/2021] [Accepted: 05/05/2021] [Indexed: 12/26/2022]
Abstract
OBJECTIVES Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.
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Affiliation(s)
- Robin Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhicheng Jiao
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Li Yang
- Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Ji Whae Choi
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Zeng Xiong
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Kasey Halsey
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Thi My Linh Tran
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Ian Pan
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
| | - Scott A Collins
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
| | - Xue Feng
- Carina Medical, Carina, Australia
| | - Jing Wu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Ken Chang
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lin-Bo Shi
- Department of Radiology, Yongzhou Central Hospital, Yongzhou, China
| | - Shuai Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Qi-Zhi Yu
- Department of Radiology, The First Hospital of Changsha, Changsha, China
| | - Jie Liu
- Department of Radiology, Changde Second People's Hospital, Changde, China
| | - Fei-Xian Fu
- Department of Radiology, Yiyang City Center Hospital, Yiyang, China
| | - Xiao-Long Jiang
- Department of Radiology, Affiliated Nan Hua Hospital, University of South China, Hengyang, China
| | - Dong-Cui Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Li-Ping Zhu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiao-Ping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Terrance T Healey
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
| | - Qiu-Hua Zeng
- Department of Radiology, Loudi Central Hospital, Loudi, China
| | - Tao Liu
- Brown University School of Public Health, Providence, RI, USA
| | - Ping-Feng Hu
- Department of Radiology, Chenzhou Second People's Hospital, Chenzhou, China
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Yi-Hui Li
- Department of Radiology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Ronnie A Sebro
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Paul J L Zhang
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Michael K Atalay
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA
| | - Wei-Hua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Harrison X Bai
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA. .,Warren Alpert Medical School at Brown University, Providence, RI, USA.
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Xie Z, Sun H, Wang J, Xu H, Li S, Zhao C, Gao Y, Wang X, Zhao T, Duan S, Hu C, Ao W. A novel CT-based radiomics in the distinction of severity of coronavirus disease 2019 (COVID-19) pneumonia. BMC Infect Dis 2021; 21:608. [PMID: 34171991 PMCID: PMC8231742 DOI: 10.1186/s12879-021-06331-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Convenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia. METHODS A total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed. RESULTS In both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775-0.918] and the test set (AUC, 0.867; 95% CI, 0.732-949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts. CONCLUSIONS The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.
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Affiliation(s)
- Zongyu Xie
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287 Changhuai Road, Bengbu, 233004, Anhui, China
| | - Haitao Sun
- Shanghai Institute of Medical Imaging, and Department of Interventional Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, No.234, Gucui Road, Hangzhou, 310012, Zhejiang Province, China
| | - He Xu
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287 Changhuai Road, Bengbu, 233004, Anhui, China
| | - Shuhua Li
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287 Changhuai Road, Bengbu, 233004, Anhui, China
| | - Cancan Zhao
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287 Changhuai Road, Bengbu, 233004, Anhui, China
| | - Yuqing Gao
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287 Changhuai Road, Bengbu, 233004, Anhui, China
| | - Xiaolei Wang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287 Changhuai Road, Bengbu, 233004, Anhui, China
| | - Tongtong Zhao
- Department of Radiology, Fuyang Second People's Hospital, No. 450 Linquan Road, Fuyang, 236000, Anhui, China
| | - Shaofeng Duan
- GE Healthcare China, Pudong new town, No1, Huatuo road, Shanghai, 210000, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188, Street of Shizi, Suzhou, 200000, China.
| | - Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No.234, Gucui Road, Hangzhou, 310012, Zhejiang Province, China.
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29
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Pang B, Li H, Liu Q, Wu P, Xia T, Zhang X, Le W, Li J, Lai L, Ou C, Ma J, Liu S, Zhou F, Wang X, Xie J, Zhang Q, Jiang M, Liu Y, Zeng Q. CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study. Front Med (Lausanne) 2021; 8:689568. [PMID: 34222293 PMCID: PMC8245676 DOI: 10.3389/fmed.2021.689568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/10/2021] [Indexed: 01/10/2023] Open
Abstract
Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis. Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = −0.535, p < 0.001). Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.
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Affiliation(s)
- Baoguo Pang
- Department of Radiology, Huangpi District Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Haijun Li
- Department of Radiology, Han Kou Hospital of Wuhan, Wuhan, China
| | - Qin Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Penghui Wu
- Pulmonary and Critical Care Medicine, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Tingting Xia
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoxian Zhang
- Pulmonary and Critical Care Medicine, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenjun Le
- Department of Respiratory, First Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, China
| | - Jianyu Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lihua Lai
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Changxing Ou
- Pulmonary and Critical Care Medicine, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jianjuan Ma
- Department of Pediatric Hematology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Shuai Liu
- Department of Hematology, Dawu County People's Hospital, Wuhan, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xinlu Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jiaxing Xie
- National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Diseases, Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qingling Zhang
- Pulmonary and Critical Care Medicine, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Min Jiang
- Department of Pediatrics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yumei Liu
- Department of Respiratory, Hankou Hospital of Wuhan, Wuhan, China
| | - Qingsi Zeng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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30
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Starosolski Z, Courtney AN, Srivastava M, Guo L, Stupin I, Metelitsa LS, Annapragada A, Ghaghada KB. A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:6641384. [PMID: 34220380 PMCID: PMC8216795 DOI: 10.1155/2021/6641384] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/26/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022]
Abstract
Objective Tumor-associated macrophages (TAMs) within the tumor immune microenvironment (TiME) of solid tumors play an important role in treatment resistance and disease recurrence. The purpose of this study was to investigate if nanoradiomics (radiomic analysis of nanoparticle contrast-enhanced images) can differentiate tumors based on TAM burden. Materials and Methods In vivo studies were performed in transgenic mouse models of neuroblastoma with low (N = 11) and high (N = 10) tumor-associated macrophage (TAM) burden. Animals underwent delayed nanoparticle contrast-enhanced CT (n-CECT) imaging at 4 days after intravenous administration of liposomal-iodine agent (1.1 g/kg). CT imaging-derived conventional tumor metrics (tumor volume and CT attenuation) were computed for segmented tumor CT datasets. Nanoradiomic analysis was performed using a PyRadiomics workflow implemented in the quantitative image feature pipeline (QIFP) server containing 900 radiomic features (RFs). RF selection was performed under supervised machine learning using a nonparametric neighborhood component method. A 5-fold validation was performed using a set of linear and nonlinear classifiers for group separation. Statistical analysis was performed using the Kruskal-Wallis test. Results N-CECT imaging demonstrated heterogeneous patterns of signal enhancement in low and high TAM tumors. CT imaging-derived conventional tumor metrics showed no significant differences (p > 0.05) in tumor volume between low and high TAM tumors. Tumor CT attenuation was not significantly different (p > 0.05) between low and high TAM tumors. Machine learning-augmented nanoradiomic analysis revealed two RFs that differentiated (p < 0.002) low TAM and high TAM tumors. The RFs were used to build a linear classifier that demonstrated very high accuracy and further confirmed by 5-fold cross-validation. Conclusions Imaging-derived conventional tumor metrics were unable to differentiate tumors with varying TAM burden; however, nanoradiomic analysis revealed texture differences and enabled differentiation of low and high TAM tumors.
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Affiliation(s)
- Zbigniew Starosolski
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Amy N. Courtney
- Texas Children's Cancer Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Mayank Srivastava
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
| | - Linjie Guo
- Texas Children's Cancer Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Igor Stupin
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
| | - Leonid S. Metelitsa
- Texas Children's Cancer Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
| | - Ananth Annapragada
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Ketan B. Ghaghada
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
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31
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Homayounieh F, Doda Khera R, Bizzo BC, Ebrahimian S, Primak A, Schmidt B, Saini S, Kalra MK. Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study. Abdom Radiol (NY) 2021; 46:2097-2106. [PMID: 33242099 PMCID: PMC7690335 DOI: 10.1007/s00261-020-02865-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/06/2020] [Accepted: 11/11/2020] [Indexed: 12/19/2022]
Abstract
Purpose To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi. Methods The local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal calculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output. Results Among 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85–0.92). Higher-order radiomics (gray-level size zone matrix – GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89–0.92). Conclusion Automated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.
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32
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Lan L, Sun W, Xu D, Yu M, Xiao F, Hu H, Xu H, Wang X. Artificial intelligence-based approaches for COVID-19 patient management. INTELLIGENT MEDICINE 2021; 1:10-15. [PMID: 34447600 PMCID: PMC8189732 DOI: 10.1016/j.imed.2021.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/27/2021] [Accepted: 05/21/2021] [Indexed: 01/08/2023]
Abstract
During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.
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33
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Näppi JJ, Uemura T, Watari C, Hironaka T, Kamiya T, Yoshida H. U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19. Sci Rep 2021; 11:9263. [PMID: 33927287 PMCID: PMC8084966 DOI: 10.1038/s41598-021-88591-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/13/2021] [Indexed: 12/23/2022] Open
Abstract
The rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P < 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan-Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P < 3 × 10-14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients.
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Affiliation(s)
- Janne J Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA, 02114, USA
| | - Tomoki Uemura
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA, 02114, USA
- Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
| | - Chinatsu Watari
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA, 02114, USA
| | - Toru Hironaka
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA, 02114, USA
| | - Tohru Kamiya
- Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA, 02114, USA.
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Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Wu J, Tran TML, Hsieh B, Choi JW, Wang D, Vallières M, Wang R, Collins S, Feng X, Feldman M, Zhang PJ, Atalay M, Sebro R, Yang L, Fan Y, Liao WH, Bai HX. Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data. Korean J Radiol 2021; 22:1213-1224. [PMID: 33739635 PMCID: PMC8236359 DOI: 10.3348/kjr.2020.1104] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 01/04/2021] [Accepted: 01/06/2021] [Indexed: 01/08/2023] Open
Abstract
Objective To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.
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Affiliation(s)
| | - Yanhe Xiao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhicheng Jiao
- Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Kasey Halsey
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Jing Wu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Thi My Linh Tran
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Ben Hsieh
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Ji Whae Choi
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Robin Wang
- Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott Collins
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA
| | - Xue Feng
- Carina Medical, Lexington, KY, USA
| | - Michael Feldman
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Paul J Zhang
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Atalay
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA
| | - Ronnie Sebro
- Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Fan
- Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Wei Hua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
| | - Harrison X Bai
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.,Warren Alpert Medical School at Brown University, Providence, RI, USA.
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35
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Mottaqi MS, Mohammadipanah F, Sajedi H. Contribution of machine learning approaches in response to SARS-CoV-2 infection. INFORMATICS IN MEDICINE UNLOCKED 2021; 23:100526. [PMID: 33869730 PMCID: PMC8044633 DOI: 10.1016/j.imu.2021.100526] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 12/19/2022] Open
Abstract
PROBLEM The lately emerged SARS-CoV-2 infection, which has put the whole world in an aberrant demanding situation, has generated an urgent need for developing effective responses through artificial intelligence (AI). AIM This paper aims to overview the recent applications of machine learning techniques contributing to prevention, diagnosis, monitoring, and treatment of coronavirus disease (SARS-CoV-2). METHODS A progressive investigation of the recent publications up to November 2020, related to AI approaches towards managing the challenges of COVID-19 infection was made. RESULTS For patient diagnosis and screening, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are broadly applied for classification purposes. Moreover, Deep Neural Network (DNN) and homology modeling are the most used SARS-CoV-2 drug repurposing models. CONCLUSION While the fields of diagnosis of the SARS-CoV-2 infection by medical image processing and its dissemination pattern through machine learning have been sufficiently studied, some areas such as treatment outcome in patients and drug development need to be further investigated using AI approaches.
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Affiliation(s)
- Mohammad Sadeq Mottaqi
- Department of Microbial Biotechnology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, 14155-6455, Tehran, Iran
| | - Fatemeh Mohammadipanah
- Department of Microbial Biotechnology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, 14155-6455, Tehran, Iran
| | - Hedieh Sajedi
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, 14155-6455, Tehran, Iran
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36
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Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186425] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features.
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37
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Giraudo C. Special Issue "COVID-19: Diagnostic Imaging and Beyond". J Clin Med 2020; 9:E2721. [PMID: 32846885 PMCID: PMC7565391 DOI: 10.3390/jcm9092721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 08/20/2020] [Indexed: 12/02/2022] Open
Abstract
Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-COV2) outbreak in China at the end of 2019, clinicians relied on diagnostic imaging to characterize the disease's extension and severity [...].
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Affiliation(s)
- Chiara Giraudo
- Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35122 Padova, Italy
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