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Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel) 2024; 14:1456. [PMID: 39001346 PMCID: PMC11240935 DOI: 10.3390/diagnostics14131456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
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Affiliation(s)
- Jin Y Chang
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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2
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Xi Z, Ye Y, Yang Y, He Y, Song Z, Ma Q, Zeng H, Shao G. Radiomics analysis based on contrast-enhanced MRI for predicting short-term efficacy of drug-eluting beads transarterial chemoembolization in hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:2387-2400. [PMID: 39030402 DOI: 10.1007/s00261-024-04319-3] [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: 10/07/2023] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 07/21/2024]
Abstract
OBJECTIVE We developed and validated a clinical-radiomics model for preoperative prediction of the short-term efficacy of initial drug-eluting beads transarterial chemoembolization (D-TACE) treatment in patients with hepatocellular carcinoma (HCC). METHODS In this retrospective cohort study of 113 patients with intermediate and advanced HCC, 5343 features were extracted based on three sequences of the arterial phase (AP), diffusion-weighted imaging, and T2-weighted images based on contrast-enhanced magnetic resonance imaging, and minimum redundancy maximum correlation and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and model construction. Multifactor logistic regression was used to build a clinical-imaging model based on clinical factors and a clinical-radiomics model. The area under the curve (AUC) and calibration curves were used to assess model performance, and the clinical value of the model was analyzed using decision curve analysis. The relationship between the actual and predicted short-term efficacy of the combined model and progression-free survival (PFS) was evaluated using Kaplan-Meier survival curves and log-rank tests. RESULTS A total of 34 radiomics features were selected by LASSO, and the clinical-radiomics model had the best predictive performance (AUC = 0.902 and AUC = 0.845 for the training and testing sets, respectively), and the model based on AP had the best predictive performance among the four radiomics models (AUC = 0.89 for the training set and AUC = 0.85 for the testing set); the multifactorial logistic regression results showed that microsphere type (p = 0.042) and AP Rad-score (p = 0.01) were associated with short-term efficacy. In addition, a difference in PFS was observed in patients with HCC with different short-term efficacies predicted by the combined model. Moreover, prognosis was better in the objective versus non-objective response group. CONCLUSIONS The combined clinical-radiomics model is an effective predictor of the short-term efficacy of initial D-TACE in patients with HCC, contributing to clinical and economic benefits for patients.
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Affiliation(s)
- Zihan Xi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, 325035, China.
| | - Yuanxin Ye
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Yongbo Yang
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Yiwei He
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Ziyang Song
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Qian Ma
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, 325035, China
| | - Hui Zeng
- Department of Intervention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Guoliang Shao
- Department of Intervention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
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Thribhuvan Reddy D, Grewal I, García Pinzon LF, Latchireddy B, Goraya S, Ali Alansari B, Gadwal A. The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management. Cureus 2024; 16:e61523. [PMID: 38957241 PMCID: PMC11218716 DOI: 10.7759/cureus.61523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2024] [Indexed: 07/04/2024] Open
Abstract
This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.
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Affiliation(s)
| | - Inayat Grewal
- Department of Medicine, Government Medical College and Hospital, Chandigarh, IND
| | | | | | - Simran Goraya
- Department of Medicine, Kharkiv National Medical University, Kharkiv, UKR
| | | | - Aishwarya Gadwal
- Department of Radiodiagnosis, St. John's Medical College and Hospital, Bengaluru, IND
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Zhang Z, Wittenstein J. Advancing acute respiratory failure management through artificial intelligence: a call for thematic collection contributions. Intensive Care Med Exp 2024; 12:45. [PMID: 38713382 PMCID: PMC11076423 DOI: 10.1186/s40635-024-00629-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/02/2024] [Indexed: 05/08/2024] Open
Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
| | - Jakob Wittenstein
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden, TUD Dresden University of Technology, Dresden, Germany
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Milanese G, Silva M, Ledda RE, Iezzi E, Bortolotto C, Mauro LA, Valentini A, Reali L, Bottinelli OM, Ilardi A, Basile A, Palmucci S, Preda L, Sverzellati N. Study rationale and design of the PEOPLHE trial. LA RADIOLOGIA MEDICA 2024; 129:411-419. [PMID: 38319494 PMCID: PMC10943160 DOI: 10.1007/s11547-024-01764-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
PURPOSE Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. METHODS PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. CONCLUSION The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.
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Affiliation(s)
- Gianluca Milanese
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | - Mario Silva
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | | | - Chandra Bortolotto
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Letizia Antonella Mauro
- Radiology Unit 1, University Hospital Policlinico G. Rodolico-San Marco, Catania, Catania, Italy
| | - Adele Valentini
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Linda Reali
- Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Olivia Maria Bottinelli
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
| | - Adriana Ilardi
- Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Antonio Basile
- Radiology Unit 1-Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Stefano Palmucci
- UOSD I.P.T.R.A.-Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Lorenzo Preda
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Nicola Sverzellati
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy.
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Bousse A, Kandarpa VSS, Rit S, Perelli A, Li M, Wang G, Zhou J, Wang G. Systematic Review on Learning-based Spectral CT. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2024; 8:113-137. [PMID: 38476981 PMCID: PMC10927029 DOI: 10.1109/trpms.2023.3314131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
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Affiliation(s)
- Alexandre Bousse
- LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale, 29238 Brest, France
| | | | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
| | - Alessandro Perelli
- Department of Biomedical Engineering, School of Science and Engineering, University of Dundee, DD1 4HN, UK
| | - Mengzhou Li
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, USA
| | - Jian Zhou
- CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA
| | - Ge Wang
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
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Bousse A, Kandarpa VSS, Rit S, Perelli A, Li M, Wang G, Zhou J, Wang G. Systematic Review on Learning-based Spectral CT. ARXIV 2024:arXiv:2304.07588v8. [PMID: 37461421 PMCID: PMC10350100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
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Affiliation(s)
| | | | - Simon Rit
- Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
| | - Alessandro Perelli
- School of Science and Engineering, University of Dundee, DD1 4HN Dundee, U.K
| | - Mengzhou Li
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, CA 95817 USA
| | - Jian Zhou
- CTIQ, Canon Medical Research USA, Inc., Vernon Hills, IL 60061 USA
| | - Ge Wang
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
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Booz C. Innovations in Chest Imaging: How Can Patients Benefit? Diagnostics (Basel) 2024; 14:141. [PMID: 38248018 PMCID: PMC10814789 DOI: 10.3390/diagnostics14020141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
This Special Issue of Diagnostics entitled "Leading Diagnosis on Chest Imaging" provides an overview of recent technical developments in chest imaging and their clinical relevance, with a special focus on dual-energy CT (DECT) imaging [...].
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Affiliation(s)
- Christian Booz
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, 60590 Frankfurt, Germany
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9
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Yu H, Yang Z, Wei Y, Shi W, Zhu M, Liu L, Wang M, Wang Y, Zhu Q, Liang Z, Zhao W, Chen LA. Computed tomography-based radiomics improves non-invasive diagnosis of Pneumocystis jirovecii pneumonia in non-HIV patients: a retrospective study. BMC Pulm Med 2024; 24:11. [PMID: 38167022 PMCID: PMC10762815 DOI: 10.1186/s12890-023-02827-4] [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: 02/02/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Pneumocystis jirovecii pneumonia (PCP) could be fatal to patients without human immunodeficiency virus (HIV) infection. Current diagnostic methods are either invasive or inaccurate. We aimed to establish an accurate and non-invasive radiomics-based way to identify the risk of PCP infection in non-HIV patients with computed tomography (CT) manifestation of pneumonia. METHODS This is a retrospective study including non-HIV patients hospitalized for suspected PCP from January 2010 to December 2022 in one hospital. The patients were randomized in a 7:3 ratio into training and validation cohorts. Computed tomography (CT)-based radiomics features were extracted automatically and used to construct a radiomics model. A diagnostic model with traditional clinical and CT features was also built. The area under the curve (AUC) were calculated and used to evaluate the diagnostic performance of the models. The combination of the radiomics features and serum β-D-glucan levels was also evaluated for PCP diagnosis. RESULTS A total of 140 patients (PCP: N = 61, non-PCP: N = 79) were randomized into training (N = 97) and validation (N = 43) cohorts. The radiomics model consisting of nine radiomic features performed significantly better (AUC = 0.954; 95% CI: 0.898-1.000) than the traditional model consisting of serum β-D-glucan levels (AUC = 0.752; 95% CI: 0.597-0.908) in identifying PCP (P = 0.002). The combination of radiomics features and serum β-D-glucan levels showed an accuracy of 95.8% for identifying PCP infection (positive predictive value: 95.7%, negative predictive value: 95.8%). CONCLUSIONS Radiomics showed good diagnostic performance in differentiating PCP from other types of pneumonia in non-HIV patients. A combined diagnostic method including radiomics and serum β-D-glucan has the potential to provide an accurate and non-invasive way to identify the risk of PCP infection in non-HIV patients with CT manifestation of pneumonia. TRIAL REGISTRATION ClinicalTrials.gov (NCT05701631).
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Affiliation(s)
- Hang Yu
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Zhen Yang
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yuanhui Wei
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Wenjia Shi
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Minghui Zhu
- Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Lu Liu
- Department of Nutrition, the First Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Miaoyu Wang
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Yueming Wang
- Department of Respiratory and Critical Care Medicine, Medical School of Chinese People's Liberation Army, Beijing, China
| | - Qiang Zhu
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Zhixin Liang
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Wei Zhao
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Liang-An Chen
- Department of Respiratory and Critical Care Medicine, the Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China.
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Gutor SS, Miller RF, Blackwell TS, Polosukhin VV. Environmental and occupational bronchiolitis obliterans: new reality. EBioMedicine 2023; 95:104760. [PMID: 37598462 PMCID: PMC10458287 DOI: 10.1016/j.ebiom.2023.104760] [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: 02/03/2023] [Revised: 07/10/2023] [Accepted: 08/02/2023] [Indexed: 08/22/2023] Open
Abstract
Patients diagnosed with environmental/occupational bronchiolitis obliterans (BO) over the last 2 decades often present with an indolent evolution of respiratory symptoms without a history of high-level, acute exposure to airborne toxins. Exertional dyspnea is the most common symptom and standard clinical and radiographic evaluation can be non-diagnostic. Lung biopsies often reveal pathological abnormalities affecting all distal lung compartments. These modern cases of BO typically exhibit the constrictive bronchiolitis phenotype of small airway remodeling, along with lymphocytic inflammation. In addition, hypertensive-type remodeling of intrapulmonary vasculature, diffuse fibroelastosis of alveolar tissue, and fibrous thickening of visceral pleura are frequently present. The diagnosis of environmental/occupational BO should be considered in patients who present with subacute onset of exertional dyspnea and a history compatible with prolonged or recurrent exposure to environmental toxins. Important areas for future studies include development of less invasive diagnostic approaches and testing of novel agents for disease prevention and treatment.
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Affiliation(s)
- Sergey S Gutor
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert F Miller
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy S Blackwell
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Veterans Affairs Medical Center, Nashville, TN, USA
| | - Vasiliy V Polosukhin
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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Siddharthan T, Grealis K, Kirkness JP, Ötvös T, Stefanovski D, Tombleson A, Dalzell M, Gonzalez E, Nakrani KB, Wenger D, Lester MG, Richmond BW, Fouras A, Punjabi NM. Quantifying ventilation by X-ray velocimetry in healthy adults. Respir Res 2023; 24:215. [PMID: 37649012 PMCID: PMC10469820 DOI: 10.1186/s12931-023-02517-z] [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: 08/18/2023] [Indexed: 09/01/2023] Open
Abstract
RATIONALE X-ray velocimetry (XV) has been utilized in preclinical models to assess lung motion and regional ventilation, though no studies have compared XV-derived physiologic parameters to measures derived through conventional means. OBJECTIVES To assess agreement between XV-analysis of fluoroscopic lung images and pitot tube flowmeter measures of ventilation. METHODS XV- and pitot tube-derived ventilatory parameters were compared during tidal breathing and with bilevel-assisted breathing. Levels of agreement were assessed using the Bland-Altman analysis. Mixed models were used to characterize the association between XV- and pitot tube-derived values and optimize XV-derived values for higher ventilatory volumes. MEASUREMENTS AND MAIN RESULTS Twenty-four healthy volunteers were assessed during tidal breathing and 11 were reassessed with increased minute ventilation with bilevel-assisted breathing. No clinically significant differences were observed between the two methods for respiratory rate (average Δ: 0.58; 95% limits of agreement: -1.55, 2.71) or duty cycle (average Δ: 0.02; 95% limits of agreement: 0.01, 0.03). Tidal volumes and flow rates measured using XV were lower than those measured using the pitot tube flowmeter, particularly at the higher volume ranges with bilevel-assisted breathing. Under these conditions, a mixed-model based adjustment was applied to the XV-derived values of tidal volume and flow rate to obtain closer agreement with the pitot tube-derived values. CONCLUSION Radiographically obtained measures of ventilation with XV demonstrate a high degree of correlation with parameters of ventilation. If the accuracy of XV were also confirmed for assessing the regional distribution of ventilation, it would provide information that goes beyond the scope of conventional pulmonary function tests or static radiographic assessments.
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Affiliation(s)
- Trishul Siddharthan
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA.
| | - Kyle Grealis
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | | | | | | | - Alex Tombleson
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | - Molly Dalzell
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | - Ernesto Gonzalez
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | - Kinjal Bhatt Nakrani
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
| | | | - Michael G Lester
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bradley W Richmond
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | | | - Naresh M Punjabi
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, USA
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12
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Kirkness JP, Dusting J, Eikelis N, Pirakalathanan P, DeMarco J, Shiao SL, Fouras A. Association of x-ray velocimetry (XV) ventilation analysis compared to spirometry. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 5:1148310. [PMID: 37440838 PMCID: PMC10335741 DOI: 10.3389/fmedt.2023.1148310] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 05/29/2023] [Indexed: 07/15/2023] Open
Abstract
Introduction X-ray Velocimetry (XV) ventilation analysis is a 4-dimensional imaging-based method for quantifying regional ventilation, aiding in the assessment of lung function. We examined the performance characteristics of XV ventilation analysis by examining correlation to spirometry and measurement repeatability. Methods XV analysis was assessed in 27 patients receiving thoracic radiotherapy for non-lung cancer malignancies. Measurements were obtained pre-treatment and at 4 and 12-months post-treatment. XV metrics such as ventilation defect percent (VDP) and regional ventilation heterogeneity (VH) were compared to spirometry at each time point, using correlation analysis. Repeatability was assessed between multiple runs of the analysis algorithm, as well as between multiple breaths in the same patient. Change in VH and VDP in a case series over 12 months was used to determine effect size and estimate sample sizes for future studies. Results VDP and VH were found to significantly correlate with FEV1 and FEV1/FVC (range: -0.36 to -0.57; p < 0.05). Repeatability tests demonstrated that VDP and VH had less than 2% variability within runs and less than 8% change in metrics between breaths. Three cases were used to illustrate the advantage of XV over spirometry, where XV indicated a change in lung function that was either undetectable or delayed in detection by spirometry. Case A demonstrated an improvement in XV metrics over time despite stable spirometric values. Case B demonstrated a decline in XV metrics as early as 4-months, although spirometric values did not change until 12-months. Case C demonstrated a decline in XV metrics at 12 months post-treatment while spirometric values remained normal throughout the study. Based on the effect sizes in each case, sample sizes ranging from 10 to 38 patients would provide 90% power for future studies aiming to detect similar changes. Conclusions The performance and safety of XV analysis make it ideal for both clinical and research applications across most lung indications. Our results support continued research and provide a basis for powering future studies using XV as an endpoint to examine lung health and determine therapeutic efficacy.
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Affiliation(s)
| | | | | | | | - John DeMarco
- Department of Radiation Oncology and Biomedical Sciences, Cedar-Sinai Medical Center, Los Angeles, CA, United States
| | - Stephen L. Shiao
- Department of Radiation Oncology and Biomedical Sciences, Cedar-Sinai Medical Center, Los Angeles, CA, United States
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Karmali D, Sowho M, Bose S, Pearce J, Tejwani V, Diamant Z, Yarlagadda K, Ponce E, Eikelis N, Otvos T, Khan A, Lester M, Fouras A, Kirkness J, Siddharthan T. Functional imaging for assessing regional lung ventilation in preclinical and clinical research. Front Med (Lausanne) 2023; 10:1160292. [PMID: 37261124 PMCID: PMC10228734 DOI: 10.3389/fmed.2023.1160292] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/17/2023] [Indexed: 06/02/2023] Open
Abstract
Dynamic heterogeneity in lung ventilation is an important measure of pulmonary function and may be characteristic of early pulmonary disease. While standard indices like spirometry, body plethysmography, and blood gases have been utilized to assess lung function, they do not provide adequate information on regional ventilatory distribution nor function assessments of ventilation during the respiratory cycle. Emerging technologies such as xenon CT, volumetric CT, functional MRI and X-ray velocimetry can assess regional ventilation using non-invasive radiographic methods that may complement current methods of assessing lung function. As a supplement to current modalities of pulmonary function assessment, functional lung imaging has the potential to identify respiratory disease phenotypes with distinct natural histories. Moreover, these novel technologies may offer an optimal strategy to evaluate the effectiveness of novel therapies and therapies targeting localized small airways disease in preclinical and clinical research. In this review, we aim to discuss the features of functional lung imaging, as well as its potential application and limitations to adoption in research.
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Affiliation(s)
- Dipan Karmali
- Division of Pulmonary and Critical Care, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
| | - Mudiaga Sowho
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Sonali Bose
- Division of Pulmonary and Critical Care, Icahn School of Medicine, Mount Sinai, NY, United States
| | - Jackson Pearce
- School of Medicine, Medical University of South Carolina, Charleston, SC, United States
| | - Vickram Tejwani
- Respiratory Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Zuzana Diamant
- Department of Microbiology Immunology and Transplantation, KU Leuven, Catholic University of Leuven, Leuven, Belgium
- Department of Respiratory Medicine and Allergology, Institute for Clinical Science, Skane University Hospital, Lund University, Lund, Sweden
- Department Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Keerthi Yarlagadda
- Division of Pulmonary and Critical Care, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
| | - Erick Ponce
- Division of Pulmonary and Critical Care, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
| | | | | | - Akram Khan
- Division of Pulmonary and Critical Care, Oregon Health and Science University, Portland, OR, United States
| | - Michael Lester
- Department of Pulmonary and Critical Care Medicine, Vanderbilt Medical Center, Nashville, CA, United States
| | | | | | - Trishul Siddharthan
- Division of Pulmonary and Critical Care, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
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14
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He S, Chen W, Wang X, Xie X, Liu F, Ma X, Li X, Li A, Feng X. Deep learning radiomics-based preoperative prediction of recurrence in chronic rhinosinusitis. iScience 2023; 26:106527. [PMID: 37123223 PMCID: PMC10139989 DOI: 10.1016/j.isci.2023.106527] [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: 11/01/2022] [Revised: 01/11/2023] [Accepted: 03/27/2023] [Indexed: 05/02/2023] Open
Abstract
Chronic rhinosinusitis (CRS) is characterized by poor prognosis and propensity for recurrence even after surgery. Identification of those CRS patients with high risk of relapse preoperatively will contribute to personalized treatment recommendations. In this paper, we proposed a multi-task deep learning network for sinus segmentation and CRS recurrence prediction simultaneously to develop and validate a deep learning radiomics-based nomogram for preoperatively predicting recurrence in CRS patients who needed surgical treatment. 265 paranasal sinuses computed tomography (CT) images of CRS from two independent medical centers were analyzed to build and test models. The sinus segmentation model achieved good segmentation results. Furthermore, the nomogram combining a deep learning signature and clinical factors also showed excellent recurrence prediction ability for CRS. Our study not only facilitates a technique for sinus segmentation but also provides a noninvasive method for preoperatively predicting recurrence in patients with CRS.
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Affiliation(s)
- Shaojuan He
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Wei Chen
- School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xuehai Wang
- Department of Otorhinolaryngology, Weihai Municipal Hospital, Weihai, China
| | - Xinyu Xie
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
| | - Fangying Liu
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
| | - Xinyi Ma
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
| | - Xuezhong Li
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
| | - Anning Li
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
- Corresponding author
| | - Xin Feng
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
- Corresponding author
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15
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Lau KK, Troupis JM, Parsons D. Transformative radiology: Chest imaging is being re-defined. Respirology 2022; 27:815-817. [PMID: 36070934 DOI: 10.1111/resp.14358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 08/22/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Kenneth K Lau
- Monash Imaging, Monash Health, Clayton, Victoria, Australia.,School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - John M Troupis
- Monash Imaging, Monash Health, Clayton, Victoria, Australia
| | - David Parsons
- Women's and Children's Hospital Adelaide Women's and Babies Division Ringgold Standard Institution-Respiratory and Sleep Medicine, North Adelaide, South Australia, Australia.,The University of Adelaide Ringgold Standard Institution-Robinson Research Institute, Adelaide, South Australia, Australia
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