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Dewey M, Henriques JPS, Kirov H, Vliegenthart R. ESR Bridges: CT builds bridges in coronary artery disease. Eur Radiol 2024; 34:732-735. [PMID: 38291257 PMCID: PMC10853315 DOI: 10.1007/s00330-023-10485-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 11/19/2023] [Accepted: 11/23/2023] [Indexed: 02/01/2024]
Affiliation(s)
- Marc Dewey
- Charité-Universitätsmedizin Berlin, corporate member of Department of Radiology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Berlin University Alliance, Berlin, Germany.
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany.
| | - José P S Henriques
- Department of Cardiology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Hristo Kirov
- Department of Cardiothoracic Surgery, Jena University Hospital, Friedrich Schiller University of Jena, Jena, Germany
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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2
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Wu MY, Han QJ, Ai Z, Liang YY, Yan HW, Xie Q, Xiang ZM. Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features. Front Oncol 2024; 14:1301649. [PMID: 38357206 PMCID: PMC10864667 DOI: 10.3389/fonc.2024.1301649] [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: 09/27/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
Purpose We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats. Methods A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment. Results Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level. Conclusions MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.
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Affiliation(s)
- Min-Yi Wu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qi-Jia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Yu-Ying Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Hao-Wen Yan
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qi Xie
- Department of Radiology, Guangzhou First People’s Hospital/Department of Medical Imaging, Nansha Hospital, Guangzhou, Guangzhou, China
| | - Zhi-Ming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
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3
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Leiner T. Radiomics for Predicting Risk of Sudden Cardiac Death in Hypertrophic Cardiomyopathy. JACC Cardiovasc Imaging 2024; 17:28-30. [PMID: 37565963 DOI: 10.1016/j.jcmg.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/05/2023] [Indexed: 08/12/2023]
Affiliation(s)
- Tim Leiner
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, USA.
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4
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Maiorino E, Loscalzo J. Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping. Arterioscler Thromb Vasc Biol 2023; 43:1111-1123. [PMID: 37226730 PMCID: PMC10330619 DOI: 10.1161/atvbaha.122.318892] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/10/2023] [Indexed: 05/26/2023]
Abstract
The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.
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Affiliation(s)
- Enrico Maiorino
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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5
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Godefroy T, Frécon G, Asquier-Khati A, Mateus D, Lecomte R, Rizkallah M, Piriou N, Jamet B, Le Tourneau T, Pallardy A, Boutoille D, Eugène T, Carlier T. 18F-FDG-Based Radiomics and Machine Learning: Useful Help for Aortic Prosthetic Valve Infective Endocarditis Diagnosis? JACC Cardiovasc Imaging 2023:S1936-878X(23)00093-1. [PMID: 37052569 DOI: 10.1016/j.jcmg.2023.01.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/25/2023] [Indexed: 04/14/2023]
Abstract
BACKGROUND Fluorine-18 fluorodeoxyglucose (18F-FDG)-positron emission tomography (PET)/computed tomography (CT) results in better sensitivity for prosthetic valve endocarditis (PVE) diagnosis, but visual image analysis results in relatively weak specificity and significant interobserver variability. OBJECTIVES The primary objective of this study was to evaluate the performance of a radiomics and machine learning-based analysis of 18F-FDG PET/CT (PET-ML) as a major criterion for the European Society of Cardiology score using machine learning as a major imaging criterion (ESC-ML) in PVE diagnosis. The secondary objective was to assess performance of PET-ML as a standalone examination. METHODS All 18F-FDG-PET/CT scans performed for suspected aortic PVE at a single center from 2015 to 2021 were retrospectively included. The gold standard was expert consensus after at least 3 months' follow-up. The machine learning (ML) method consisted of manually segmenting each prosthetic valve, extracting 31 radiomics features from the segmented region, and training a ridge logistic regressor to predict PVE. Training and hyperparameter tuning were done with a cross-validation approach, followed by an evaluation on an independent test database. RESULTS A total of 108 patients were included, regardless of myocardial uptake, and were divided into training (n = 68) and test (n = 40) cohorts. Considering the latter, PET-ML findings were positive for 13 of 22 definite PVE cases and 3 of 18 rejected PVE cases (59% sensitivity, 83% specificity), thus leading to an ESC-ML sensitivity of 72% and a specificity of 83%. CONCLUSIONS The use of ML for analyzing 18F-FDG-PET/CT images in PVE diagnosis was feasible and beneficial, particularly when ML was included in the ESC 2015 criteria. Despite some limitations and the need for future developments, this approach seems promising to optimize the role of 18F-FDG PET/CT in PVE diagnosis.
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Affiliation(s)
- Thomas Godefroy
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France
| | - Gauthier Frécon
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France; ECN, LS2N, Nantes, France
| | - Antoine Asquier-Khati
- Nantes Université, CHU Nantes, INSERM, Infectious Diseases Department, Nantes, France
| | | | - Raphaël Lecomte
- Nantes Université, CHU Nantes, INSERM, Infectious Diseases Department, Nantes, France
| | | | - Nicolas Piriou
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, Nantes, France
| | - Bastien Jamet
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France
| | - Thierry Le Tourneau
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du Thorax, Nantes, France
| | - Amandine Pallardy
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France
| | - David Boutoille
- Nantes Université, CHU Nantes, INSERM, Infectious Diseases Department, Nantes, France
| | - Thomas Eugène
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France.
| | - Thomas Carlier
- Nantes Université, CHU Nantes, INSERM, Nuclear Médicine, Nantes, France
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Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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7
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Mestres C, Quintana E, Pereda D. Will artificial intelligence help us in predicting outcomes in cardiac surgery? J Card Surg 2022; 37:3846-3847. [PMID: 36001760 PMCID: PMC9804569 DOI: 10.1111/jocs.16844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 01/05/2023]
Affiliation(s)
- Carlos A. Mestres
- Department of Cardiothoracic Surgery and the Robert WM Frater Cardiovascular Research CentreThe University of the Free StateBloemfonteinSouth Africa
| | - Eduard Quintana
- Department of Cardiothoracic Surgery and the Robert WM Frater Cardiovascular Research CentreThe University of the Free StateBloemfonteinSouth Africa
| | - Daniel Pereda
- Department of Cardiovascular Surgery, Hospital ClinicThe University of BarcelonaBarcelonaSpain
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Zhang X, Sun T, Liu E, Xu W, Wang S, Wang Q. Development and evaluation of a radiomics model of resting 13N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1167. [PMID: 36467349 PMCID: PMC9708489 DOI: 10.21037/atm-22-4692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2023]
Abstract
BACKGROUND Coronary angiography (CAG) is usually performed in patients with coronary heart disease (CHD) to evaluate the coronary artery stenosis. However, patients with iodine allergy and renal dysfunction are not suitable for CAG. We try to develop a radiomics machine learning model based on rest 13N-ammonia (13N-NH3) positron emission tomography (PET) myocardial perfusion imaging (MPI) to predict coronary stenosis. METHODS Eighty-four patients were included with the inclusion criteria: adult patients; suspected CHD; resting MPI and CAG were performed; and complete data. Coronary artery stenosis >75% were considered to be significant stenosis. Patients were randomly divided into a training group and a testing group with a ratio of 1:1. Myocardial blood flow (MBF), perfusion defect extent (EXT), total perfusion deficit (TPD), and summed rest score (SRS) were obtained. Myocardial static images of the left ventricular (LV) coronary segments were segmented, and radiomics features were extracted. In the training set, the conventional parameter (MPI model) and radiomics (Rad model) models were constructed using the machine learning method and were combined to construct a nomogram. The models' performance was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, decision analysis curve (DCA), and calibration curves. Testing and subgroup analysis were performed. RESULTS MPI model was composed of MBF and EXT, and Rad model was composed of 12 radiomics features. In the training set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.795/0.778/0.937/0.511, 0.912/0.825/0.760/0.936 and 0.911/0.865/0.924/0.766 respectively. In the testing set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.798/0.722/0.659/0.841, 0.887/0.810/0.744/0.932 and 0.900/0.849/0.854/0.841 respectively. The AUC of Rad model and nomogram were significantly higher than that of MPI model. The DCA curve also showed that the clinical net benefit of the Rad model and nomogram was similar but greater than that of MPI model. The calibration curve showed good agreement between the observed and predicted values of the Rad model. In the subgroup analysis of Rad model, there was no significant difference in AUC between subgroups. CONCLUSIONS The Rad model is more accurate than the MPI model in predicting coronary stenosis. This noninvasive technique could help improve risk stratification and had good generalization ability.
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Affiliation(s)
- Xiaochun Zhang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Taotao Sun
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Entao Liu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Weiping Xu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shuxia Wang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
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9
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Cavallo AU, Di Donna C, Troisi J, Cerimele C, Cesareni M, Chiocchi M, Floris R, Garaci F. Radiomics analysis of short tau inversion recovery images in cardiac magnetic resonance for the prediction of late gadolinium enhancement in patients with acute myocarditis. Magn Reson Imaging 2022; 94:168-173. [PMID: 36116711 DOI: 10.1016/j.mri.2022.09.004] [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: 05/09/2022] [Revised: 08/31/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022]
Abstract
RATIONALE AND OBJECTIVES Cardiac Magnetic Resonance (CMR) imaging is recommended as the reference diagnostic non-invasive modality for myocarditis but is often limited by patients' compliance. The purpose of this study is to evaluate the validity of Radiomics applied to Short Tau Inversion Recovery (STIR) sequences, in predicting the presence of LGE in patients with suspected acute myocarditis. MATERIALS AND METHODS 171 STIR images on short-axis view were segmented with "MaZda" software ver 4.6, by placing a region of interest (ROI) on the left ventricle by two radiologists in consensus. Images were classified according to the presence of LGE in the equivalent short-axis T1-IR slice. A total of 337 ROI features were extracted for each image. Dataset was then split into two parts (train and test set) with 70:30 ratio. RESULTS Eleven classification models were trained. An Ensemble Machine Learning (EML) model was obtained by averaging the predictions of models with accuracy on test set >70%. The EML documented accuracy of 0.75, sensitivity of 0.8 and a specificity of 0.73 with a NPV of 0.81 and a PPV of 0.7, with AUC of 0.79 (95% CI: 0.66-0.92). CONCLUSION Radiomics and machine learning analysis could be a promising approach in reducing scan times without reducing diagnostic accuracy in predicting LGE in patients with acute myocarditis.
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Affiliation(s)
- Armando Ugo Cavallo
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy; Division of Radiology, San Carlo di Nancy Hospital, GVM Care and Research, Rome, Italy
| | - Carlo Di Donna
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy.
| | - Jacopo Troisi
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Italy; Theoreo srl - Spin-off company of the University of Salerno, Italy
| | | | | | | | - Roberto Floris
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy; San Raffaele Cassino, Cassino, FR, Italy
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Influence of Percutaneous Drainage Surgery and the Interval to Perform Laparoscopic Cholecystectomy on Acute Cholecystitis through Genetic Algorithm-Based Contrast-Enhanced Ultrasound Imaging. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3602811. [PMID: 35942459 PMCID: PMC9356791 DOI: 10.1155/2022/3602811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/08/2022] [Accepted: 06/28/2022] [Indexed: 12/07/2022]
Abstract
To discuss the optimal interval time between genetic algorithm-based ultrasound imaging-guided percutaneous drainage surgery (PTGD) and laparoscopic cholecystectomy (LC), 64 cholecystitis patients were selected as the research objects and evenly divided into experimental group (intelligent algorithm was adopted to recognize patients’ ultrasonic images) and control group (professional doctors carried out diagnosis). 92 acute cholecystitis patients undergoing PTGD were divided into three groups. 30 out of the 92 patients received LC within 2 months and were defined as the early group. 32 were performed with LC within 2 to 4 months and were defined as the metaphase group. 28 underwent LC over 4 months and were defined as the late-stage group. The average operation time, the transition from LC to laparotomy, the average postoperative hospital stay, and the incidence of complications of the three groups were compared. The results revealed that the comparison of the diagnostic accuracy and comprehensive effectiveness between experimental group and control group demonstrated that the differences were statistically significant (
). When the optimal interval of implementing LC after PTGD was realized, the corresponding values of the early group were 88.5 minutes, 16.67%, 8.13 days, and 13.75%. Those of the metaphase group were 49.91 minutes, 3.13%, 4.97 days, and 9.52%. Those of the late stage group were 68.78 minutes, 10.71%, 7.09 days, and 11.96%. To sum up, the diagnostic accuracy and comprehensive effectiveness of intelligent algorithm were higher than those of conventional ultrasound, and the optimal interval time of implementing LC after PTGD was 2 to 4 months.
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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: 1] [Impact Index Per Article: 0.5] [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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Ayx I, Tharmaseelan H, Hertel A, Nörenberg D, Overhoff D, Rotkopf LT, Riffel P, Schoenberg SO, Froelich MF. Myocardial Radiomics Texture Features Associated with Increased Coronary Calcium Score—First Results of a Photon-Counting CT. Diagnostics (Basel) 2022; 12:diagnostics12071663. [PMID: 35885567 PMCID: PMC9320412 DOI: 10.3390/diagnostics12071663] [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: 06/10/2022] [Revised: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
The coronary artery calcium score is an independent risk factor of the development of adverse cardiac events. The severity of coronary artery calcification may influence the myocardial texture. Due to higher spatial resolution and signal-to-noise ratio, new CT technologies such as PCCT may improve the detection of texture alterations depending on the severity of coronary artery calcification. In this retrospective, single-center, IRB-approved study, left ventricular myocardium was segmented and radiomics features were extracted using pyradiomics. The mean and standard deviation with the Pearson correlation coefficient for correlations of features were calculated and visualized as boxplots and heatmaps. Random forest feature selection was performed. Thirty patients (26.7% women, median age 58 years) were enrolled in the study. Patients were divided into two subgroups depending on the severity of coronary artery calcification (Agatston score 0 and Agatston score ≥ 100). Through random forest feature selection, a set of four higher-order features could be defined to discriminate myocardial texture between the two groups. When including the additional Agatston 1–99 groups as a validation, a severity-associated change in feature intensity was detected. A subset of radiomics features texture alterations of the left ventricular myocardium was associated with the severity of coronary artery calcification estimated by the Agatston score.
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Affiliation(s)
- Isabelle Ayx
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.); (M.F.F.)
- Correspondence: ; Tel.: +49-62-1383-2067
| | - Hishan Tharmaseelan
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.); (M.F.F.)
| | - Alexander Hertel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.); (M.F.F.)
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.); (M.F.F.)
| | - Daniel Overhoff
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.); (M.F.F.)
- Department of Diagnostic and Interventional Radiology and Neuroradiology, Bundeswehr Central Hospital Koblenz, Rübenacher Straße 170, 56072 Koblenz, Germany
| | - Lukas T. Rotkopf
- Department of Radiology, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;
| | - Philipp Riffel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.); (M.F.F.)
| | - Stefan O. Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.); (M.F.F.)
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.); (M.F.F.)
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Ayx I, Tharmaseelan H, Hertel A, Nörenberg D, Overhoff D, Rotkopf LT, Riffel P, Schoenberg SO, Froelich MF. Comparison Study of Myocardial Radiomics Feature Properties on Energy-Integrating and Photon-Counting Detector CT. Diagnostics (Basel) 2022; 12:diagnostics12051294. [PMID: 35626448 PMCID: PMC9141463 DOI: 10.3390/diagnostics12051294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 12/30/2022] Open
Abstract
The implementation of radiomics-based, quantitative imaging parameters is hampered by a lack of stability and standardization. Photon-counting computed tomography (PCCT), compared to energy-integrating computed tomography (EICT), does rely on a novel detector technology, promising better spatial resolution and contrast-to-noise ratio. However, its effect on radiomics feature properties is unknown. This work investigates this topic in myocardial imaging. In this retrospective, single-center IRB-approved study, the left ventricular myocardium was segmented on CT, and the radiomics features were extracted using pyradiomics. To compare features between scanners, a t-test for non-paired samples and F-test was performed, with a threshold of 0.05 set as a benchmark for significance. Feature correlations were calculated by the Pearson correlation coefficient, and visualization was performed with heatmaps. A total of 50 patients (56% male, mean age 56) were enrolled in this study, with equal proportions of PCCT and EICT. First-order features were, nearly, comparable between both groups. However, higher-order features showed a partially significant difference between PCCT and EICT. While first-order radiomics features of left ventricular myocardium show comparability between PCCT and EICT, detected differences of higher-order features may indicate a possible impact of improved spatial resolution, better detection of lower-energy photons, and a better signal-to-noise ratio on texture analysis on PCCT.
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Affiliation(s)
- Isabelle Ayx
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (I.A.); (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.)
| | - Hishan Tharmaseelan
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (I.A.); (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.)
| | - Alexander Hertel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (I.A.); (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.)
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (I.A.); (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.)
| | - Daniel Overhoff
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (I.A.); (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.)
- Department of Diagnostic and Interventional Radiology and Neuroradiology, Bundeswehr Central Hospital Koblenz, Rübenacher Straße 170, 56072 Koblenz, Germany
| | - Lukas T. Rotkopf
- Department of Radiology, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;
| | - Philipp Riffel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (I.A.); (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.)
| | - Stefan O. Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (I.A.); (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.)
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany; (I.A.); (H.T.); (A.H.); (D.N.); (D.O.); (P.R.); (S.O.S.)
- Correspondence: ; Tel.: +49-621-383-2067
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14
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Alabed S, Uthoff J, Zhou S, Garg P, Dwivedi K, Alandejani F, Gosling R, Schobs L, Brook M, Shahin Y, Capener D, Johns CS, Wild JM, Rothman AMK, van der Geest RJ, Condliffe R, Kiely DG, Lu H, Swift AJ. Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:265-275. [PMID: 36713008 PMCID: PMC9708011 DOI: 10.1093/ehjdh/ztac022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/19/2022] [Indexed: 02/01/2023]
Abstract
Aims Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning. Methods and results Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and four-chamber MPCA-based predictions was statistically significant (hazard ratios: 2.1, 95% CI: 1.3, 3.4, c-index = 0.70, P = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (P ≤ 0.001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality. Conclusion The MPCA-based machine learning is an explainable time-resolved approach that allows visualization of prognostic cardiac features throughout the cardiac cycle at the population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of 1-year mortality risk in PAH.
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Affiliation(s)
| | - Johanna Uthoff
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Shuo Zhou
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Pankaj Garg
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Faisal Alandejani
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Rebecca Gosling
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Lawrence Schobs
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Martin Brook
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Yousef Shahin
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Dave Capener
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Christopher S Johns
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Jim M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,INSIGNEO, Institute for in silico medicine, University of Sheffield, UK
| | - Alexander M K Rothman
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | | | - Robin Condliffe
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - David G Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,INSIGNEO, Institute for in silico medicine, University of Sheffield, UK,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
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15
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Cavallo AU, Troisi J, Muscogiuri E, Cavallo P, Rajagopalan S, Citro R, Bossone E, McVeigh N, Forte V, Di Donna C, Giannini F, Floris R, Garaci F, Sperandio M. Cardiac Computed Tomography Radiomics-Based Approach for the Detection of Left Ventricular Remodeling in Patients with Arterial Hypertension. Diagnostics (Basel) 2022; 12:diagnostics12020322. [PMID: 35204413 PMCID: PMC8871253 DOI: 10.3390/diagnostics12020322] [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: 12/19/2021] [Revised: 01/17/2022] [Accepted: 01/24/2022] [Indexed: 11/16/2022] Open
Abstract
The aim of the study is to verify the feasibility of a radiomics based approach for the detection of LV remodeling in patients with arterial hypertension. Cardiac Computed Tomography (CCT) and clinical data of patients with and without history of arterial hypertension were collected. In one image per patient, on a 4-chamber view, left ventricle (LV) was segmented using a polygonal region of interest by two radiologists in consensus. A total of 377 radiomics features per region of interest were extracted. After dataset splitting (70:30 ratio), eleven classification models were tested for the discrimination of patients with and without arterial hypertension based on radiomics data. An Ensemble Machine Learning (EML) score was calculated from models with an accuracy >60%. Boruta algorithm was used to extract radiomic features discriminating between patients with and without history of hypertension. Pearson correlation coefficient was used to assess correlation between EML score and septum width in patients included in the test set. EML showed an accuracy, sensitivity and specificity of 0.7. Correlation between EML score and LV septum width was 0.53 (p-value < 0.0001). We considered LV septum width as a surrogate of myocardial remodeling in our population, and this is the reason why we can consider the EML score as a possible tool to evaluate myocardial remodeling. A CCT-based radiomic approach for the identification of LV remodeling is possible in patients with past medical history of arterial hypertension.
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Affiliation(s)
- Armando Ugo Cavallo
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.D.); (R.F.); (F.G.)
- Division of Radiology, San Carlo di Nancy Hospital, GVM Care and Research, 00165 Rome, Italy; (V.F.); (M.S.)
- Correspondence: ; Tel.: +39-333-903-3702
| | - Jacopo Troisi
- Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno, 84100 Salerno, Italy; or
- Theoreo srl—Spin-Off Company of the University of Salerno, 84100 Salerno, Italy
| | - Emanuele Muscogiuri
- Radiology Department, Ospedale S. Andrea, Sapienza—Università di Roma, 00189 Rome, Italy;
| | - Pierpaolo Cavallo
- Department of Physics “E.R. Caianello”, University of Salerno, 84100 Salerno, Italy;
- Istituto Sistemi Complessi—Consiglio Nazionale delle Ricerche (CNR), 00185 Rome, Italy
| | - Sanjay Rajagopalan
- Division of Cardiovascular Medicine, Harrington Heart and Vascular Institute, Cleveland, OH 44106, USA;
| | - Rodolfo Citro
- Division of Cardiology, University Hosptal “San Giovanni di Dio e Ruggi D’Aragona”, 84100 Salerno, Italy;
| | - Eduardo Bossone
- Cardiology Division, “A. Cardarelli” Hospital, 80131 Naples, Italy;
| | - Niall McVeigh
- Department of Radiology, St Vincent’s University Hospital, Merrion Road, D04 T6F4 Dublin, Ireland;
- School of Medicine, University College Dublin, D04 T6F4 Dublin, Ireland
| | - Valerio Forte
- Division of Radiology, San Carlo di Nancy Hospital, GVM Care and Research, 00165 Rome, Italy; (V.F.); (M.S.)
| | - Carlo Di Donna
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.D.); (R.F.); (F.G.)
| | - Francesco Giannini
- Division of Cardiology, Maria Cecilia Hospital, GVM Care and Research, 48033 Cotignola, Italy;
| | - Roberto Floris
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.D.); (R.F.); (F.G.)
| | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (C.D.D.); (R.F.); (F.G.)
- San Raffaele Cassino, 03043 Cassino, Italy
| | - Massimiliano Sperandio
- Division of Radiology, San Carlo di Nancy Hospital, GVM Care and Research, 00165 Rome, Italy; (V.F.); (M.S.)
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16
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Peng F, Zheng T, Tang X, Liu Q, Sun Z, Feng Z, Zhao H, Gong L. Magnetic Resonance Texture Analysis in Myocardial Infarction. Front Cardiovasc Med 2021; 8:724271. [PMID: 34778395 PMCID: PMC8581163 DOI: 10.3389/fcvm.2021.724271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
Texture analysis (TA) is a newly arisen field that can detect the invisible MRI signal changes among image pixels. Myocardial infarction (MI) is cardiomyocyte necrosis caused by myocardial ischemia and hypoxia, becoming the primary cause of death and disability worldwide. In recent years, various TA studies have been performed in patients with MI and show a good clinical application prospect. This review briefly presents the main pathogenesis and pathophysiology of MI, introduces the overview and workflow of TA, and summarizes multiple magnetic resonance TA (MRTA) clinical applications in MI. We also discuss the facing challenges currently for clinical utilization and propose the prospect.
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Affiliation(s)
- Fei Peng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tian Zheng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoping Tang
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qiao Liu
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zijing Sun
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhaofeng Feng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Heng Zhao
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Lianggeng Gong
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
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17
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Stadelmann SA, Blüthgen C, Milanese G, Nguyen-Kim TDL, Maul JT, Dummer R, Frauenfelder T, Eberhard M. Lung Nodules in Melanoma Patients: Morphologic Criteria to Differentiate Non-Metastatic and Metastatic Lesions. Diagnostics (Basel) 2021; 11:diagnostics11050837. [PMID: 34066913 PMCID: PMC8148527 DOI: 10.3390/diagnostics11050837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 04/28/2021] [Accepted: 05/05/2021] [Indexed: 12/01/2022] Open
Abstract
Lung nodules are frequent findings in chest computed tomography (CT) in patients with metastatic melanoma. In this study, we assessed the frequency and compared morphologic differences of metastases and benign nodules. We retrospectively evaluated 85 patients with melanoma (AJCC stage III or IV). Inclusion criteria were ≤20 lung nodules and follow-up using CT ≥183 days after baseline. Lung nodules were evaluated for size and morphology. Nodules with significant growth, nodule regression in line with RECIST assessment or histologic confirmation were judged to be metastases. A total of 438 lung nodules were evaluated, of which 68% were metastases. At least one metastasis was found in 78% of patients. A 10 mm diameter cut-off (used for RECIST) showed a specificity of 95% and a sensitivity of 20% for diagnosing metastases. Central location (n = 122) was more common in metastatic nodules (p = 0.009). Subsolid morphology (n = 53) was more frequent (p < 0.001), and calcifications (n = 13) were solely found in non-metastatic lung nodules (p < 0.001). Our data show that lung nodules are prevalent in about two-thirds of melanoma patients (AJCC stage III/IV) and the majority are metastases. Even though we found a few morphologic indicators for metastatic or non-metastatic lung nodules, morphology has limited value to predict the presence of lung metastases.
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Affiliation(s)
- Simone Alexandra Stadelmann
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (S.A.S.); (C.B.); (T.D.L.N.-K.); (T.F.)
| | - Christian Blüthgen
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (S.A.S.); (C.B.); (T.D.L.N.-K.); (T.F.)
| | - Gianluca Milanese
- Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy;
| | - Thi Dan Linh Nguyen-Kim
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (S.A.S.); (C.B.); (T.D.L.N.-K.); (T.F.)
| | - Julia-Tatjana Maul
- Department of Dermatology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (J.-T.M.); (R.D.)
| | - Reinhard Dummer
- Department of Dermatology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (J.-T.M.); (R.D.)
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (S.A.S.); (C.B.); (T.D.L.N.-K.); (T.F.)
| | - Matthias Eberhard
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; (S.A.S.); (C.B.); (T.D.L.N.-K.); (T.F.)
- Correspondence: ; Tel.: +41-(0)44-255-9139; Fax: +41-(0)44-255-4443
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18
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Slart RHJA, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans AWJM, Gimelli A, Georgoulias P, Gheysens O, Gaemperli O, Habib G, Hustinx R, Cosyns B, Verberne HJ, Hyafil F, Erba PA, Lubberink M, Slomka P, Išgum I, Visvikis D, Kolossváry M, Saraste A. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol Imaging 2021; 48:1399-1413. [PMID: 33864509 PMCID: PMC8113178 DOI: 10.1007/s00259-021-05341-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/25/2021] [Indexed: 12/18/2022]
Abstract
In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
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Affiliation(s)
- Riemer H J A Slart
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
- Faculty of Science and Technology Biomedical, Photonic Imaging, University of Twente, Enschede, The Netherlands.
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Luis Eduardo Juarez-Orozco
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging facility QMRI, Edinburgh, UK
| | - Andor W J M Glaudemans
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
| | | | - Panagiotis Georgoulias
- Department of Nuclear Medicine, Faculty of Medicine, University of Thessaly, University Hospital of Larissa, Larissa, Greece
| | - Olivier Gheysens
- Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc and Institute of Clinical and Experimental Research (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium
| | | | - Gilbert Habib
- APHM, Cardiology Department, La Timone Hospital, Marseille, France
- IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, ULiège, Liège, Belgium
| | - Bernard Cosyns
- Department of Cardiology, Centrum voor Hart en Vaatziekten, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, 1090, Brussels, Belgium
| | - Hein J Verberne
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Fabien Hyafil
- Department of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F-75015, Paris, France
- University of Paris, PARCC, INSERM, F-75006, Paris, France
| | - Paola A Erba
- Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands
- Department of Nuclear Medicine (P.A.E.), University of Pisa, Pisa, Italy
- Department of Translational Research and New Technology in Medicine (P.A.E.), University of Pisa, Pisa, Italy
| | - Mark Lubberink
- Department of Surgical Sciences/Radiology, Uppsala University, Uppsala, Sweden
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden
| | - Piotr Slomka
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ivana Išgum
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, University of Amsterdam, 1105, Amsterdam, AZ, Netherlands
| | | | - Márton Kolossváry
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
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