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Campello CA, Castanha EB, Vilardo M, Staziaki PV, Francisco MZ, Mohajer B, Watte G, Moraes FY, Hochhegger B, Altmayer S. Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysis. Abdom Radiol (NY) 2023; 48:3114-3126. [PMID: 37365266 DOI: 10.1007/s00261-023-03984-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVES To perform a meta-analysis of the diagnostic performance of learning (ML) algorithms (conventional and deep learning algorithms) for the classification of malignant versus benign focal liver lesions (FLLs) on US and CEUS. METHODS Available databases were searched for relevant published studies through September 2022. Studies met eligibility criteria if they evaluate the diagnostic performance of ML for the classification of malignant and benign focal liver lesions on US and CEUS. The pooled per-lesion sensitivities and specificities for each modality with 95% confidence intervals were calculated. RESULTS A total of 8 studies on US, 11 on CEUS, and 1 study evaluating both methods met the inclusion criteria with a total of 34,245 FLLs evaluated. The pooled sensitivity and specificity of ML for the malignancy classification of FLLs were 81.7% (95% CI, 77.2-85.4%) and 84.8% (95% CI, 76.0-90.8%) for US, compared to 87.1% (95% CI, 81.8-91.0%) and 87.0% (95% CI, 83.1-90.1%) for CEUS. In the subgroup analysis of studies that evaluated deep learning algorithms, the sensitivity and specificity of CEUS (n = 4) increased to 92.4% (95% CI, 88.5-95.0%) and 88.2% (95% CI, 81.1-92.9%). CONCLUSIONS The diagnostic performance of ML algorithms for the malignant classification of FLLs was high for both US and CEUS with overall similar sensitivity and specificity. The similar performance of US may be related to the higher prevalence of DL models in that group.
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Affiliation(s)
- Carlos Alberto Campello
- School of Medicine, Universidade Federal do Mato Grosso, 2367 Quarenta e Nove St, Cuiabá, Brazil
| | - Everton Bruno Castanha
- School of Medicine, Universidade Federal de Pelotas, 538 Prof. Dr. Araújo St. Pelotas, Pelotas, Brazil
| | - Marina Vilardo
- School of Medicine, Universidade Catolica de Brasilia, QS 07, Brasília, Brazil
| | - Pedro V Staziaki
- Department of Radiology, University of Vermont Medical Center, 111 Colchester Ave, Burlington, USA
| | - Martina Zaguini Francisco
- Department of Radiology, Universidade Federal de Ciencias da Saude de Porto Alegre, 245 Sarmento Leite St, Porto Alegre, Brazil
| | - Bahram Mohajer
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, USA
| | - Guilherme Watte
- Department of Radiology, Universidade Federal de Ciencias da Saude de Porto Alegre, 245 Sarmento Leite St, Porto Alegre, Brazil
| | - Fabio Ynoe Moraes
- Department of Oncology, Queen's University, 76 Stuart St, Kingston, Canada
| | - Bruno Hochhegger
- Department of Radiology, University of Florida, 1600 SW Archer Rd, Gainesville, USA
| | - Stephan Altmayer
- Department of Radiology, Stanford University, 300 Pasteur Drive, Suite H1330, Stanford, USA.
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Staziaki PV, Qureshi MM, Maybury A, Gangasani NR, LeBedis CA, Mercier GA, Anderson SW. Hematocrit and lactate trends help predict outcomes in trauma independent of CT and other clinical parameters. Front Radiol 2023; 3:1186277. [PMID: 37789953 PMCID: PMC10544960 DOI: 10.3389/fradi.2023.1186277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/21/2023] [Indexed: 10/05/2023]
Abstract
Background Hematocrit and lactate have an established role in trauma as indicators of bleeding and cell death, respectively. The wide availability of CT imaging and clinical data poses the question of how these can be used in combination to predict outcomes. Purpose To assess the utility of hematocrit or lactate trends in predicting intensive care unit (ICU) admission and hospital length of stay (LOS) in patients with torso trauma combined with clinical parameters and injury findings on CT. Materials and Methods This was a single-center retrospective study of adults with torso trauma in one year. Trends were defined as a unit change per hour. CT findings and clinical parameters were explanatory variables. Outcomes were ICU admission and hospital LOS. Multivariate logistic and negative binomial regression models were used to calculate the odds ratio (OR) and incident rate ratio (IRR). Results Among 840 patients, 561 (72% males, age 39 ± 18) were included, and 168 patients (30%) were admitted to the ICU. Decreasing hematocrit trend [OR 2.54 (1.41-4.58), p = 0.002] and increasing lactate trend [OR 3.85 (1.35-11.01), p = 0.012] were associated with increased odds of ICU admission. LOS median was 2 (IQR: 1-5) days. Decreasing hematocrit trend [IRR 1.37 (1.13-1.66), p = 0.002] and increasing lactate trend [2.02 (1.43-2.85), p < 0.001] were associated with longer hospital LOS. Conclusion Hematocrit and lactate trends may be helpful in predicting ICU admission and LOS in torso trauma independent of organ injuries on CT, age, or admission clinical parameters.
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Affiliation(s)
- Pedro V. Staziaki
- Department of Radiology, The University of Vermont Medical Center, Larner College of Medicine at the University of Vermont, Burlington, VT, United States
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Muhammad M. Qureshi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
- Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Aaron Maybury
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Neha R. Gangasani
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
- Department of Radiology, Emory University, Atlanta, GA, United States
| | - Christina A. LeBedis
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Gustavo A. Mercier
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Stephan W. Anderson
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
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Dreizin D, Staziaki PV, Khatri GD, Beckmann NM, Feng Z, Liang Y, Delproposto ZS, Klug M, Spann JS, Sarkar N, Fu Y. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol 2023; 30:251-265. [PMID: 36917287 PMCID: PMC10640925 DOI: 10.1007/s10140-023-02120-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty. PURPOSE To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness. METHODS Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends. RESULTS A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding. CONCLUSIONS Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Pedro V Staziaki
- Cardiothoracic Imaging, Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Garvit D Khatri
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Nicholas M Beckmann
- Memorial Hermann Orthopedic & Spine Hospital, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Zhaoyong Feng
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Zachary S Delproposto
- Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | - J Stephen Spann
- Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Nathan Sarkar
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yunting Fu
- Health Sciences and Human Services Library, University of Maryland, Baltimore, Baltimore, MD, USA
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Staziaki PV, Yi PH, Li MD, Daye D, Kahn CE, Gichoya JW. The Radiology: Artificial Intelligence Trainee Editorial Board: Initial Experience and Future Directions. Acad Radiol 2022; 29:1899-1902. [PMID: 35606258 DOI: 10.1016/j.acra.2022.04.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/28/2022] [Accepted: 04/13/2022] [Indexed: 01/26/2023]
Abstract
In 2019, the journal Radiology: Artificial Intelligence introduced its Trainee Editorial Board (TEB) to offer formal training in medical journalism to medical students, radiology residents and fellows, and research-career trainees. The TEB aims to build a community of radiologists, radiation oncologists, medical physicists, and researchers in fields related to artificial intelligence (AI) in radiology. The program presented opportunities to learn about the editorial process, improve skills in writing and reviewing, advance the field of AI in radiology, and help translate and disseminate AI research. To meet these goals, TEB members contribute actively to the editorial process from peer review to publication, participate in educational webinars, and create and curate content in a variety of forms. Almost all of the contact has been mediated through the web. In this article, we share initial experiences and identify future directions and opportunities.
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Affiliation(s)
- Pedro V Staziaki
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114.
| | - Paul H Yi
- Department of Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Matthew D Li
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114
| | - Dania Daye
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114
| | - Charles E Kahn
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Judy W Gichoya
- Department of Radiology, Emory University, Atlanta, Georgia
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Karády J, Ferencik M, Mayrhofer T, Meyersohn NM, Bittner DO, Staziaki PV, Szilveszter B, Hallett TR, Lu MT, Puchner SB, Simon TG, Foldyna B, Ginsburg GS, McGarrah RW, Voora D, Shah SH, Douglas PS, Hoffmann U, Corey KE. Risk factors for cardiovascular disease among individuals with hepatic steatosis. Hepatol Commun 2022; 6:3406-3420. [PMID: 36281983 PMCID: PMC9701472 DOI: 10.1002/hep4.2090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/25/2022] [Accepted: 08/08/2022] [Indexed: 01/21/2023] Open
Abstract
Cardiovascular disease (CVD) is the leading cause of mortality in adults with hepatic steatosis (HS). However, risk factors for CVD in HS are unknown. We aimed to identify factors associated with coronary artery disease (CAD) and incident major adverse cardiovascular events (MACE) in individuals with HS. We performed a nested cohort study of adults with HS detected on coronary computed tomography in the PROspective Multicenter Imaging Study for Evaluation of chest pain (PROMISE) trial. Obstructive CAD was defined as ≥50% coronary stenosis. MACE included hospitalization for unstable angina, nonfatal myocardial infarction, or all-cause death. Multivariate modeling, adjusted for age, sex, atherosclerotic CVD (ASCVD) risk score and body mass index, identified factors associated with obstructive CAD. Cox regression, adjusted for ASCVD risk score, determined the predictors of MACE. A total of 959 of 3,756 (mean age 59.4 years, 55.0% men) had HS. Obstructive CAD was present in 15.2% (145 of 959). Male sex (adjusted odds ratio [aOR] = 1.83, 95% confidence interval [CI] 1.18-1.2.84; p = 0.007), ASCVD risk score (aOR = 1.05, 95% CI 1.03-1.07; p < 0.001), and n-terminal pro-b-type natriuretic peptide (NT-proBNP; aOR = 1.90, 95% CI 1.38-2.62; p < 0.001) were independently associated with obstructive CAD. In the 25-months median follow-up, MACE occurred in 4.4% (42 of 959). Sedentary lifestyle (adjusted hazard ratio [aHR] = 2.53, 95% CI 1.27-5.03; p = 0.008) and NT-proBNP (aOR = 1.50, 95% CI 1.01-2.25; p = 0.046) independently predicted MACE. Furthermore, the risk of MACE increased by 3% for every 1% increase in ASCVD risk score (aHR = 1.03, 95% CI 1.01-1.05; p = 0.02). Conclusion: In individuals with HS, male sex, NT-pro-BNP, and ASCVD risk score are associated with obstructive CAD. Furthermore, ASCVD, NT-proBNP, and sedentary lifestyle are independent predictors of MACE. These factors, with further validation, may help risk-stratify adults with HS for incident CAD and MACE.
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Affiliation(s)
- Julia Karády
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA,MTA‐SE Cardiovascular Imaging Research GroupHeart and Vascular Center, Semmelweis UniversityBudapestHungary
| | - Maros Ferencik
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA,Knight Cardiovascular InstituteOregon Health and Science UniversityPortlandOregonUSA
| | - Thomas Mayrhofer
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA,School of Business StudiesStralsund University of Applied SciencesStralsundGermany
| | - Nandini M. Meyersohn
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA
| | - Daniel O. Bittner
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA,Department of CardiologyFriedrich‐Alexander University Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Pedro V. Staziaki
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA
| | - Balint Szilveszter
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA,MTA‐SE Cardiovascular Imaging Research GroupHeart and Vascular Center, Semmelweis UniversityBudapestHungary
| | - Travis R. Hallett
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA
| | - Michael T. Lu
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA
| | - Stefan B. Puchner
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA,Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Tracey G. Simon
- Division of GastroenterologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Borek Foldyna
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA
| | | | - Robert W. McGarrah
- Duke Molecular Physiology InstituteDuke UniversityDurhamNorth CarolinaUSA
| | - Deepak Voora
- Duke Center for Applied Genomics & Precision MedicineDuke University School of MedicineDurhamNorth CarolinaUSA
| | - Svati H. Shah
- Duke Molecular Physiology InstituteDuke UniversityDurhamNorth CarolinaUSA,Duke Clinical Research InstituteDuke University School of MedicineDurhamNorth CarolinaUSA
| | - Pamela S. Douglas
- Duke Clinical Research InstituteDuke University School of MedicineDurhamNorth CarolinaUSA
| | - Udo Hoffmann
- Cardiovascular Imaging Research CenterHarvard Medical School, Massachusetts General HospitalBostonMassachusettsUSA
| | - Kathleen E. Corey
- Division of GastroenterologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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Staziaki PV, Hedgire SS. Giant Ascending Aortic Aneurysm Causing Central Venous Occlusion. Radiology 2022; 305:33-34. [DOI: 10.1148/radiol.220414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Pedro V. Staziaki
- From the Division of Cardiovascular Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114
| | - Sandeep S. Hedgire
- From the Division of Cardiovascular Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114
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Staziaki PV, Santinha JAA, Coelho MO, Angulo D, Hussain M, Folio LR. Gamification in Radiology Training Module Developed During the Society for Imaging Informatics in Medicine Annual Meeting Hackathon. J Digit Imaging 2022; 35:714-722. [PMID: 35166970 PMCID: PMC9156580 DOI: 10.1007/s10278-022-00603-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 12/15/2022] Open
Abstract
The purpose of this manuscript is to report our experience in the 2021 SIIM Virtual Hackathon, where we developed a proof-of-concept of a radiology training module with elements of gamification. In the 50 h allotted in the hackathon, we proposed an idea, connected with colleagues from five different countries, and completed an operational proof-of-concept, which was demonstrated live at the hackathon showcase, competing with eight other teams. Our prototype involved participants annotating publicly available chest radiographs of patients with tuberculosis. We showed how we could give experience points to trainees based on annotation precision compared to ground truth radiologists' annotation, ranked in a live leaderboard. We believe that gamification elements could provide an engaging solution for radiology education. Our project was awarded first place out of eight participating hackathon teams.
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Affiliation(s)
- Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, 480 Harrison Ave, FGH Building, 4th floor, MA, 02118, Boston, USA.
- Division of Cardiovascular Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA, 02114, USA.
| | - João A A Santinha
- IST University of Lisbon, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
- Clinical Computational Imaging Group, Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal
| | - Marcelo O Coelho
- Department of Imaging Diagnosis, Federal University of São Paulo (UNIFESP), Rua Napoleão de Barros, 800, Vila Clementino, São Paulo, SP, CEP 04024-002, Brazil
| | - Diego Angulo
- Prodigious, CLL 93B N 13 44 PISO 3, Bogotá, Colombia
| | - Mohannad Hussain
- Techie Maestro Inc., 928 Creekside Drive, Waterloo, ON, N2V2W6, Canada
| | - Les R Folio
- NIH Clinical Center, 10 Center Drive, Bethesda, MD, 20892, USA
- Adjunct Clinical Professor of Radiology, George Washington University Hospital, Washington, DC, USA
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Meyersohn NM, Mayrhofer T, Corey KE, Bittner DO, Staziaki PV, Szilveszter B, Hallett T, Lu MT, Puchner SB, Simon TG, Foldyna B, Voora D, Ginsburg GS, Douglas PS, Hoffmann U, Ferencik M. Association of Hepatic Steatosis With Major Adverse Cardiovascular Events, Independent of Coronary Artery Disease. Clin Gastroenterol Hepatol 2021; 19:1480-1488.e14. [PMID: 32707340 PMCID: PMC7855524 DOI: 10.1016/j.cgh.2020.07.030] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/12/2020] [Accepted: 07/14/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND & AIMS Hepatic steatosis has been associated with increased risk of major adverse cardiovascular events (MACE) but it is not clear whether steatosis is independently associated with risk of MACE. We investigated whether steatosis is associated with risk of MACE independently of the presence and extent of baseline coronary artery disease, assessed by comprehensive contrast-enhanced computed tomography angiography (CTA). METHODS We conducted a nested cohort study of 3756 subjects (mean age, 60.6 years; 48.4% men) who underwent coronary CTA at 193 sites in North America, from July 2010 through September 2013, as part of the PROMISE study, which included noninvasive cardiovascular analyses of symptomatic outpatients without coronary artery disease. Independent core laboratory readers measured hepatic and splenic attenuation, using non-contrast computed tomography images to identify steatosis, and evaluated coronary plaques and stenosis in coronary CTA images. We collected data on participants' cardiovascular risk factors, presence of metabolic syndrome, and body mass index. The primary endpoint was an adjudicated composite of MACE (death, myocardial infarction, or unstable angina) during a median follow-up time of 25 months. RESULTS Among the 959 subjects who had steatosis (25.5% of the cohort), 42 had MACE (4.4%), whereas among the 2797 subjects without steatosis, 73 had MACE (2.6%) (hazard ratio [HR] for MACE in subjects with steatosis, 1.69; 95% CI, 1.16-2.48; P = .006 for MACE in subjects with vs without steatosis). This association remained after adjustment for atherosclerotic cardiovascular disease risk scores, significant stenosis, and metabolic syndrome (adjusted HR, 1.72; 95% CI, 1.16-2.54; P = .007) or obesity (adjusted HR, 1.75; 95% CI, 1.19-2.59; P = .005). Steatosis remained independently associated with MACE after adjustment for all CTA measures of plaques and stenosis. CONCLUSIONS Hepatic steatosis is associated with MACE independently of other cardiovascular risk factors or extent of coronary artery disease. Strategies to reduce steatosis might reduce risk of MACE. ClinicalTrials.gov no: NCT01174550.
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Affiliation(s)
- Nandini M. Meyersohn
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Thomas Mayrhofer
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA,School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
| | - Kathleen E. Corey
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
| | - Daniel O. Bittner
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA,Friedrich-Alexander University Erlangen-Nürnberg, Department of Cardiology, University Hospital Erlangen, Germany
| | - Pedro V. Staziaki
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Balint Szilveszter
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Travis Hallett
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Michael T. Lu
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Stefan B. Puchner
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA,Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Tracey G. Simon
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
| | - Borek Foldyna
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Deepak Voora
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC
| | - Geoffrey S. Ginsburg
- Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC
| | - Pamela S. Douglas
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | - Udo Hoffmann
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA
| | - Maros Ferencik
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA,Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR
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Staziaki PV, Santo IDDO, Skobodzinski AA, Park LK, Bedi HS. How to Use YouTube for Radiology Education. Curr Probl Diagn Radiol 2020; 50:461-468. [PMID: 33261926 DOI: 10.1067/j.cpradiol.2020.11.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/29/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022]
Abstract
YouTube, the most commonly used free video-sharing platform globally, is increasingly being used as an educational tool in Radiology. Trainees worldwide now have the opportunity to learn about medical imaging at their own pace in the comfort of their homes, without geographical and financial constraints. Unfortunately, because YouTube is an easily accessible platform, it also incurs the risk of disseminating erroneous medical information or low-quality educational content. This article outlines the primary considerations when creating educational content on YouTube, including technical aspects, best practices, and measures to maximize effectiveness and success. Additionally, we discuss the current usage of the platform for Radiology education and its advantages and disadvantages and list some of the most popular Radiology YouTube channels.
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Affiliation(s)
- Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA.
| | | | - Alexus A Skobodzinski
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA
| | - Lisa K Park
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA
| | - Harprit S Bedi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA
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Santo IDDO, Staziaki PV, Prilutskiy A, Sachs TE, Murakami AM. Solitary intramuscular myofibroma in an adult: Case report and MR imaging findings. Clin Imaging 2020; 67:95-100. [PMID: 32531695 DOI: 10.1016/j.clinimag.2020.05.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/08/2020] [Accepted: 05/28/2020] [Indexed: 11/16/2022]
Abstract
Myofibroma is a benign, soft tissue neoplasm that predominantly affects infants and young children. Most occur in the skin or subcutaneous tissues, with a predilection for the head and neck regions. We describe the magnetic resonance (MR) imaging and histophathologic findings of a rare case of intramuscular myofibroma of the right deltoid in a healthy 30-year-old male. MR imaging revealed a well-circumscribed intramuscular mass, with isointense signal on T1-weighted images, hyperintense signal on T2-weighed images, and a "target-sign" with peripheral rim enhancement after gadolinium administration. The lesion was surgically excised with no complications, and the histopathologic analysis revealed the typical morphologic and histochemical markers of a myofibroma. We conclude that, although rare, myofibroma can be considered in the differential diagnosis of adults with lesions the above signal characteristics.
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Affiliation(s)
- Irene Dixe de Oliveira Santo
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, United States of America
| | - Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, United States of America.
| | - Andrey Prilutskiy
- Department of Pathology, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, United States of America
| | - Teviah E Sachs
- Department of Surgical Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, United States of America
| | - Akira M Murakami
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, United States of America
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11
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Staziaki PV, Vadvala HV, Furtado VF, Daye D, Arellano RS, Uppot RN. Early trends and predictors of renal function following computed tomography-guided percutaneous cryoablation of a renal mass in patients with and without prior renal impairment. Radiol Bras 2020; 53:141-147. [PMID: 32587420 PMCID: PMC7302900 DOI: 10.1590/0100-3984.2019.0098] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 09/12/2019] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVE To assess trends and predictors of the glomerular filtration rate (GFR) after renal mass cryoablation in patients with and without history of renal impairment. MATERIALS AND METHODS This was a retrospective study of 39 patients who underwent computed tomography-guided percutaneous cryoablation of a renal mass, divided into two groups: those with prior renal impairment (PRI+); and those without prior renal impairment (PRI-). The GFR trend and the chronic kidney disease stage were evaluated at baseline, as well as at 1, 6, and 12 months after cryoablation. Predictors of GFR at 1 and 6 months were modeled with linear regression. RESULTS In both groups, the mean GFR at 1 month and 6 months was significantly lower than at baseline (p < 0.001 and p = 0.01, respectively). Although the GFR was lower across all time points in the PRI+ group (-26.1; p < 0.001), the overall trend was not statistically different from that observed in the PRI- group (p = 0.89). Univariate analysis showed that the decline in GFR at 1 and 6 months correlated with the baseline GFR (0.77 and 0.63; p < 0.001 and p = 0.03, respectively) and with the size of the ablation zone (-7.6 and -12.84, respectively; p = 0.03 for both). However, in the multivariate model, baseline GFR was predictive only of GFR at 1 month (p < 0.001). CONCLUSION The trend in GFR decline after cryoablation is similar for patients with and without a history of renal impairment. Baseline GFR predicts the mean GFR in the early post-cryoablation period.
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Affiliation(s)
- Pedro V. Staziaki
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Harshna V. Vadvala
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Johns Hopkins Hospital, Johns Hopkins University, Baltimore, MD, USA
| | - Vanessa Fiorini Furtado
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Dania Daye
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Raul N. Uppot
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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12
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Takumi K, Staziaki PV, Hito R, Nadgir RN, Berk JL, Andreu-Arasa VC, Chavez W, Sakai O. Amyloidosis in the head and neck: CT findings with clinicopathological correlation. Eur J Radiol 2020; 128:109034. [PMID: 32438260 DOI: 10.1016/j.ejrad.2020.109034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 03/11/2020] [Accepted: 04/20/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE To characterize the CT imaging features of head and neck amyloidosis and correlate with extent of disease and clinical outcomes. MATERIALS AND METHODS This retrospective study included 80 patients with head and neck amyloidosis who underwent soft tissue neck CT imaging between November 2003 and April 2018. The CT imaging features including lesion distribution, morphology (focal, diffuse/circumferential, or combined), presence and pattern of calcification, (punctate or diffuse), and thickness of airway lesion were evaluated and compared with the extent of amyloidosis (localized or systemic), and clinical course (stable, no recurrence, or progression requiring repeated surgical treatment). RESULTS Localized disease (83.8%, 67/80) was most common with AL type (97.6%, 41/42) representing nearly all cases of head and neck amyloidosis. The larynx was the most frequently affected organ (60.0%, 48/80), specifically the glottis (43.8%, 35/80). Calcification was seen in 65.0% of cases (52/80). Non-airway or tongue lesions were significantly associated with systemic (92.3%, 12/13) as opposed to localized amyloidosis (4.5%, 3/67; P < 0.001). Repeated surgical treatment was significantly associated with laryngeal amyloidosis (35.3%, 12/34; P = 0.002) and multi-centric disease (33.3%, 10/30; P = 0.048). Airway wall thickness in patients who required repeated surgical treatment was significantly greater than in patients with stable or no recurrent disease (P = 0.016). CONCLUSION Knowledge of the imaging features of head and neck amyloidosis can aid the diagnosis, disease monitoring, and prediction of patients requiring repeated surgical intervention.
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Affiliation(s)
- Koji Takumi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States; Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Rania Hito
- Department of Radiology, Veteran Affairs Boston Healthcare System, Boston University School of Medicine, Boston, MA, United States
| | - Rohini N Nadgir
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States; The Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology, The Johns Hopkins Medical Institutions, Baltimore, MD, United States
| | - John L Berk
- Amyloidosis Center, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Wilson Chavez
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States; Department of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States; Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States.
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13
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Bittner DO, Mayrhofer T, Budoff M, Szilveszter B, Foldyna B, Hallett TR, Ivanov A, Janjua S, Meyersohn NM, Staziaki PV, Achenbach S, Ferencik M, Douglas PS, Hoffmann U, Lu MT. Prognostic Value of Coronary CTA in Stable Chest Pain: CAD-RADS, CAC, and Cardiovascular Events in PROMISE. JACC Cardiovasc Imaging 2019; 13:1534-1545. [PMID: 31734213 DOI: 10.1016/j.jcmg.2019.09.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 08/13/2019] [Accepted: 09/13/2019] [Indexed: 01/29/2023]
Abstract
OBJECTIVES The purpose of this study was to compare Coronary Artery Disease Reporting and Data System (CAD-RADS) to traditional stenosis categories and the coronary artery calcium score (CACS) for predicting cardiovascular events in patients with stable chest pain and suspected coronary artery disease (CAD). BACKGROUND The 2016 CAD-RADS has been established to standardize the reporting of CAD on coronary CT angiography (CTA). METHODS PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial participants' CTAs were assessed by a central CT core laboratory for CACS, traditional stenosis-based categories, and modified CAD-RADS grade including high-risk coronary plaque (HRP) features. Traditional stenosis categories and CAD-RADS grade were compared for the prediction of the composite endpoint of death, myocardial infarction, or hospitalization for unstable angina over a median follow-up of 25 months. Incremental prognostic value over traditional risk factors and CACS was assessed. RESULTS In 3,840 eligible patients (mean age: 60.4 ± 8.2 years; 49% men), 3.0% (115) experienced events. CAD-RADS (concordance statistic [C-statistic] 0.747) had significantly higher discriminatory value than traditional stenosis-based assessments (C-statistic 0.698 to 0.717; all p for comparison ≤0.001). With no plaque (CAD-RADS 0) as the baseline, the hazard ratio (HR) for an event increased from 2.43 (95% confidence interval [CI]: 1.16 to 5.08) for CAD-RADS 1 to 21.84 (95% CI: 8.63 to 55.26) for CAD-RADS 4b and 5. In stepwise nested models, CAD-RADS added incremental prognostic value beyond ASCVD risk score and CACS (C-statistic 0.776 vs. 0.682; p < 0.001), and added incremental value persisted in all CACS strata. CONCLUSIONS These data from a large representative contemporary cohort of patients undergoing coronary CTA for stable chest pain support the prognostic value of CAD-RADS as a standard reporting system for coronary CTA.
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Affiliation(s)
- Daniel O Bittner
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Friedrich-Alexander University Erlangen-Nürnberg (FAU), Department of Cardiology, Erlangen, Germany.
| | - Thomas Mayrhofer
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
| | - Matt Budoff
- Los Angeles Biomedical Research Institute, Torrance, California
| | - Balint Szilveszter
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; MTA-SE Lendület Cardiovascular Imaging Research Group, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary
| | - Borek Foldyna
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Travis R Hallett
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Alexander Ivanov
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sumbal Janjua
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Nandini M Meyersohn
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Pedro V Staziaki
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Stephan Achenbach
- Friedrich-Alexander University Erlangen-Nürnberg (FAU), Department of Cardiology, Erlangen, Germany
| | - Maros Ferencik
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Oregon
| | - Pamela S Douglas
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
| | - Udo Hoffmann
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Michael T Lu
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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14
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Oda M, Staziaki PV, Qureshi MM, Andreu-Arasa VC, Li B, Takumi K, Chapman MN, Wang A, Salama AR, Sakai O. Using CT texture analysis to differentiate cystic and cystic-appearing odontogenic lesions. Eur J Radiol 2019; 120:108654. [PMID: 31539792 DOI: 10.1016/j.ejrad.2019.108654] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/16/2019] [Accepted: 08/26/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE Cystic and cystic-appearing odontogenic lesions of the jaw may appear similar on CT imaging. Accurate diagnosis is often difficult although the relationship of the lesion to the tooth root or crown may offer a clue to the etiology. The purpose of this study was to evaluate CT texture analysis as an aid in differentiating cystic and cystic-appearing odontogenic lesions of the jaw. METHODS This was an IRB-approved retrospective study including 42 pathology-proven dentigerous cysts, 37 odontogenic keratocysts, and 19 ameloblastomas. Each lesion was manually segmented on axial CT images, and textural features were analyzed using an in-house-developed Matlab-based texture analysis program that extracted 47 texture features from each segmented volume. Statistical analysis was performed comparing all pairs of the three types of lesions. RESULTS Pairwise analysis revealed that nine histogram features, one GLCM feature, three GLRL features, two Laws features, four GLGM features and two Chi-square features showed significant differences between dentigerous cysts and odontogenic keratocysts. Four histogram features and one Chi-square feature showed significant differences between odontogenic keratocysts and ameloblastomas. Two histogram features showed significant differences between dentigerous cysts and ameloblastomas. CONCLUSIONS CT texture analysis may be useful as a noninvasive method to obtain additional quantitative information to differentiate cystic and cystic-appearing odontogenic lesions of the jaw.
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Affiliation(s)
- Masafumi Oda
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Division of Oral and Maxillofacial Radiology, Kyushu Dental University, Kitakyushu, Fukuoka, Japan
| | - Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Muhammad M Qureshi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, United States
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Baojun Li
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Koji Takumi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Margaret N Chapman
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Albert Wang
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Andrew R Salama
- Deparment of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, United States; Department of Oral & Maxillofacial Surgery, Boston Medical Center, Boston University Henry M. Goldman School of Dental Medicine, United States
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, United States.
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15
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Daye D, Staziaki PV, Furtado VF, Tabari A, Fintelmann FJ, Frenk NE, Shyn P, Tuncali K, Silverman S, Arellano R, Gee MS, Uppot RN. CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept. Cardiovasc Intervent Radiol 2019; 42:1771-1776. [PMID: 31489473 DOI: 10.1007/s00270-019-02336-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 08/30/2019] [Indexed: 01/17/2023]
Abstract
INTRODUCTION To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool. MATERIALS AND METHODS This is a pilot retrospective study of patients with adrenal metastases undergoing ablation. Clinical variables were collected. Thirty-two texture features were extracted from manually segmented adrenal tumors. A univariate cox proportional hazard model was used for prediction of local progression and survival. A linear support vector machine (SVM) learning technique was applied to the texture features and clinical variables, with leave-one-out cross-validation. Receiver operating characteristic analysis and the area under the curve (AUC) were used to assess performance between using clinical variables only versus clinical variables and texture features. RESULTS Twenty-one patients (61% male, age 64.1 ± 10.3 years) were included. Mean time to local progression was 29.8 months. Five texture features exhibited association with progression (p < 0.05). The SVM model based on clinical variables alone resulted in an AUC of 0.52, whereas the SVM model that included texture features resulted in an AUC 0.93 (p = 0.01). Mean overall survival was 35 months. Fourteen texture features were associated with survival in the univariate model (p < 0.05). While the trained SVM model based on clinical variables resulted in an AUC of 0.68, the SVM model that included texture features resulted in an AUC of 0.93 (p = 0.024). DISCUSSION Pre-ablation texture analysis and machine learning improve local tumor progression and survival prediction in patients with adrenal metastases who undergo ablation.
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Affiliation(s)
- Dania Daye
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA.
| | - Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | | | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA
| | - Florian J Fintelmann
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA
| | - Nathan Elie Frenk
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA
| | - Paul Shyn
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kemal Tuncali
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stuart Silverman
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ronald Arellano
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA
| | - Michael S Gee
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA
| | - Raul Nirmal Uppot
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA
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16
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Furtado VF, Batalini F, Staziaki PV, Prilutskiy A, Sloan JM. Acute promyelocytic leukaemia presenting as necrotising fasciitis of the perineum (Fournier gangrene). BMJ Case Rep 2018; 11:11/1/e226837. [PMID: 30567203 DOI: 10.1136/bcr-2018-226837] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
We present a case of an unusual presentation of acute promyelocytic leukaemia (APML), which presented with Fournier gangrene (FG). A 38-year-old man presented with malaise, groin swelling, anal bleeding, fever and was found to have FG. Initial workup revealed pancytopaenia, borderline low fibrinogen, prolonged international normalized ratio (INR), which raised the suspicion for leukaemia. The peripheral blood differential revealed leucopaenia with absolute neutropaenia and a 5% abnormal promyelocytes but no blasts, suspicious for APML. Bone marrow biopsy was performed and fluorescence in situ hydridization (FISH), karyotype and PCR confirmed a t(15;17) translocation, establishing a diagnosis of APML. After 1 month of therapy for intermediate risk APML with All-trans retinoic acid (ATRA) and arsenic trioxide (ATO), repeat chromosomal analysis and repeat bone marrow biopsy revealed no evidence of residual APML. After the consolidation phase was started with ATRA and ATO regimen, the wound healed after 2 months and the patient achieved complete remission.
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Affiliation(s)
- Vanessa Fiorini Furtado
- Department of Internal Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Felipe Batalini
- Department of Internal Medicine, Boston Medical Center, Boston, Massachusetts, USA
| | - Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University, Boston, Massachusetts, USA
| | - Andrey Prilutskiy
- Department of Pathology, Boston Medical Center, Boston, Massachusetts, USA
| | - John Mark Sloan
- Department of Hematology and Oncology, Boston Medical Center, Boston, Massachusetts, USA
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17
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Janjua SA, Triant VA, Addison D, Szilveszter B, Regan S, Staziaki PV, Grinspoon SA, Hoffmann U, Zanni MV, Neilan TG. HIV Infection and Heart Failure Outcomes in Women. J Am Coll Cardiol 2018; 69:107-108. [PMID: 28057235 DOI: 10.1016/j.jacc.2016.11.013] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 10/26/2016] [Accepted: 11/01/2016] [Indexed: 01/13/2023]
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18
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Meyersohn NM, Mayrhofer T, Ivanov A, Bittner DO, Staziaki PV, Szilveszter B, Hallett T, Lu ML, Puchner SB, Simon TG, Corey KE, Ginsburg GS, Douglas PS, Hoffmann U, Ferencik M. P6209Association of hepatic steatosis with adverse cardiovascular events: insights from the PROspective Multicenter Imaging Study for Evaluation of chest pain (PROMISE) trial. Eur Heart J 2018. [DOI: 10.1093/eurheartj/ehy566.p6209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- N M Meyersohn
- Massachusetts General Hospital, Boston, United States of America
| | - T Mayrhofer
- Massachusetts General Hospital, Boston, United States of America
| | - A Ivanov
- Massachusetts General Hospital, Boston, United States of America
| | - D O Bittner
- Massachusetts General Hospital, Boston, United States of America
| | - P V Staziaki
- Massachusetts General Hospital, Boston, United States of America
| | - B Szilveszter
- Massachusetts General Hospital, Boston, United States of America
| | - T Hallett
- Massachusetts General Hospital, Boston, United States of America
| | - M L Lu
- Massachusetts General Hospital, Boston, United States of America
| | - S B Puchner
- Massachusetts General Hospital, Boston, United States of America
| | - T G Simon
- Massachusetts General Hospital, Boston, United States of America
| | - K E Corey
- Massachusetts General Hospital, Boston, United States of America
| | - G S Ginsburg
- Duke University Medical Center, Durham, United States of America
| | - P S Douglas
- Duke University Medical Center, Durham, United States of America
| | - U Hoffmann
- Massachusetts General Hospital, Boston, United States of America
| | - M Ferencik
- Oregon Health & Science University, Portland, United States of America
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Kohli P, Staziaki PV, Janjua SA, Addison DA, Hallett TR, Hennessy O, Takx RAP, Lu MT, Fintelmann FJ, Semigran M, Harris RS, Celli BR, Hoffmann U, Neilan TG. The effect of emphysema on readmission and survival among smokers with heart failure. PLoS One 2018; 13:e0201376. [PMID: 30059544 PMCID: PMC6066229 DOI: 10.1371/journal.pone.0201376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 07/13/2018] [Indexed: 12/22/2022] Open
Abstract
Heart Failure (HF) and chronic obstructive pulmonary disease (COPD) are morbid diseases that often coexist. In patients with coexisting disease, COPD is an independent risk factor for readmission and mortality. However, spirometry is often inaccurate in those with active heart failure. Therefore, we investigated the association between the presence of emphysema on computed tomography (CT) and readmission rates in smokers admitted with heart failure (HF). The cohort included a consecutive group of smokers discharged with HF from a tertiary center between January 1, 2014 and April 1, 2014 who also had a CT of the chest for dyspnea. The primary endpoint was any readmission for HF before April 1, 2016; secondary endpoints were 30-day readmission for HF, length of stay and all-cause mortality. Over the study period, there were 225 inpatient smokers with HF who had a concurrent chest CT (155 [69%] males, age 69±11 years, ejection fraction [EF] 46±18%, 107 [48%] LVEF of < 50%). Emphysema on CT was present in 103 (46%) and these were older, had a lower BMI, more pack-years, less diabetes and an increased afterload. During a follow-up of 2.1 years, there were 110 (49%) HF readmissions and 55 (24%) deaths. When separated by emphysema on CT, any readmission, 30-day readmission, length of stay and mortality were higher among HF patients with emphysema. In multivariable regression, emphysema by CT was associated with a two-fold higher (adjusted HR 2.11, 95% CI 1.41–3.15, p < 0.001) risk of readmission and a trend toward increased mortality (adjusted HR 1.70 95% CI 0.86–3.34, p = 0.12). In conclusion, emphysema by CT is a frequent finding in smokers hospitalized with HF and is associated with adverse outcomes in HF. This under recognized group of patients with both emphysema and heart failure may benefit from improved recognition and characterization of their co-morbid disease processes and optimization of therapies for their lung disease.
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Affiliation(s)
- Puja Kohli
- Pulmonary and Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- * E-mail:
| | - Pedro V. Staziaki
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Sumbal A. Janjua
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Daniel A. Addison
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Travis R. Hallett
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Orla Hennessy
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Richard A. P. Takx
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Michael T. Lu
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Florian J. Fintelmann
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Marc Semigran
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Robert S. Harris
- Pulmonary and Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Bartolome R. Celli
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham & Women’s Hospital, Boston, Massachusetts, United States of America
| | - Udo Hoffmann
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Tomas G. Neilan
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
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20
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Hedgire S, Baliyan V, Zucker EJ, Bittner DO, Staziaki PV, Takx RAP, Scholtz JE, Meyersohn N, Hoffmann U, Ghoshhajra B. Perivascular Epicardial Fat Stranding at Coronary CT Angiography: A Marker of Acute Plaque Rupture and Spontaneous Coronary Artery Dissection. Radiology 2018; 287:808-815. [PMID: 29401041 DOI: 10.1148/radiol.2017171568] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Purpose To evaluate the frequency and implications of perivascular fat stranding on coronary computed tomography (CT) angiograms obtained for suspected acute coronary syndrome (ACS). Materials and Methods This retrospective registry study was approved by the institutional review board. The authors reviewed the medical records and images of 1403 consecutive patients (796 men, 607 women; mean age, 52.8 years) who underwent coronary CT angiography at the emergency department from February 2012 to March 2016. Fat attenuation, length and number of circumferential quadrants of the affected segment, and attenuation values in the unaffected epicardial and subcutaneous fat were measured. "Cases" were defined as patients with perivascular fat stranding. Patients with significant stenosis but without fat stranding were considered control subjects. Baseline imaging characteristics, ACS frequency, and results of subsequent downstream testing were compared between cases and control subjects by using two-sample t, Mann-Whitney U, and Fisher tests. Results Perivascular fat stranding was seen in 11 subjects, nine with atherosclerotic lesions and two with spontaneous coronary artery dissections, with a mean fat stranding length of 19.2 mm and circumferential extent averaging 2.9 quadrants. The mean attenuation of perivascular fat stranding, normal epicardial fat, and normal subcutaneous fat was 17, -93.2, and -109.3 HU, respectively (P < .001). Significant differences (P < .05) between cases and control subjects included lower Agatston score, presence of wall motion abnormality, and initial elevation of serum troponin level. ACS frequency was 45.4% in cases and 3.8% in control subjects (P = .001). Conclusion Recognition of perivascular fat stranding may be a helpful additional predictor of culprit lesion and marker of risk for ACS in patients with significant stenosis or spontaneous coronary artery dissection. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Sandeep Hedgire
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Vinit Baliyan
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Evan J Zucker
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Daniel O Bittner
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Pedro V Staziaki
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Richard A P Takx
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Jan-Erik Scholtz
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Nandini Meyersohn
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Udo Hoffmann
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
| | - Brian Ghoshhajra
- From the Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114 (S.H., V.B., N.M., U.H., B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (E.J.Z.); Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.O.B., P.S., J.E.S.); Department of Cardiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany (D.O.B.); and Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (R.A.P.T.)
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21
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Addison D, Lawler PR, Emami H, Janjua SA, Staziaki PV, Hallett TR, Hennessy O, Lee H, Szilveszter B, Lu M, Mousavi N, Nayor MG, Delling FN, Romero JM, Wirth LJ, Chan AW, Hoffmann U, Neilan TG. Incidental Statin Use and the Risk of Stroke or Transient Ischemic Attack after Radiotherapy for Head and Neck Cancer. J Stroke 2018; 20:71-79. [PMID: 29402065 PMCID: PMC5836583 DOI: 10.5853/jos.2017.01802] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 12/01/2017] [Accepted: 12/05/2017] [Indexed: 12/29/2022] Open
Abstract
Background and Purpose Interventions to reduce the risk for cerebrovascular events (CVE; stroke and transient ischemic attack [TIA]) after radiotherapy (RT) for head and neck cancer (HNCA) are needed. Among broad populations, statins reduce CVEs; however, whether statins reduce CVEs after RT for HNCA is unclear. Therefore, we aimed to test whether incidental statin use at the time of RT is associated with a lower rate of CVEs after RT for HNCA. Methods From an institutional database we identified all consecutive subjects treated with neck RT from 2002 to 2012 for HNCA. Data collection and event adjudication was performed by blinded teams. The primary outcome was a composite of ischemic stroke and TIA. The secondary outcome was ischemic stroke. The association between statin use and events was determined using Cox proportional hazard models after adjustment for traditional and RT-specific risk factors. Results The final cohort consisted of 1,011 patients (59±13 years, 30% female, 44% hypertension) with 288 (28%) on statins. Over a median follow-up of 3.4 years (interquartile range, 0.1 to 14) there were 102 CVEs (89 ischemic strokes and 13 TIAs) with 17 in statin users versus 85 in nonstatins users. In a multivariable model containing known predictors of CVE, statins were associated with a reduction in the combination of stroke and TIA (hazard ratio [HR], 0.4; 95% confidence interval [CI], 0.2 to 0.8; P=0.01) and ischemic stroke alone (HR, 0.4; 95% CI, 0.2 to 0.8; P=0.01). Conclusions Incidental statin use at the time of RT for HNCA is associated with a lower risk of stroke or TIA.
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Affiliation(s)
- Daniel Addison
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Cardio-Oncology Program, Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Division of Cardiology, Department of Medicine, The Ohio State University, Columbus, OH, USA
| | - Patrick R Lawler
- Peter Munk Cardiac Centre, Toronto General Hospital, and the Heart and Stroke Richard Lewar Centre of Excellence, University of Toronto, Toronto, ON, Canada.,Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hamed Emami
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sumbal A Janjua
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Pedro V Staziaki
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Travis R Hallett
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Orla Hennessy
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hang Lee
- Biostatistics Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bálint Szilveszter
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Lu
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Negar Mousavi
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Matthew G Nayor
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Francesca N Delling
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Javier M Romero
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lori J Wirth
- Division of Oncology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Annie W Chan
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Udo Hoffmann
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tomas G Neilan
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Cardio-Oncology Program, Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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22
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Bittner DO, Takx RAP, Staziaki PV, Janjua S, Neilan TG, Meyersohn NM, Lu MT, Prabhakar AM, Nagurney JT, Hoffmann U, Ghoshhajra BB. Identification of coronary artery calcification can optimize risk stratification in patients with acute chest pain. Int J Cardiol 2017; 249:473-478. [PMID: 29121752 PMCID: PMC5939567 DOI: 10.1016/j.ijcard.2017.06.119] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2017] [Revised: 06/16/2017] [Accepted: 06/29/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND The number of patients presenting to the emergency department (ED) with suspected acute coronary syndrome (ACS) is substantial. We tested whether identification of coronary artery calcium (CAC) can improve the negative predictive value (NPV) of clinical risk assessment for ACS in patients with acute chest pain. METHODS AND RESULTS We included 826 consecutive patients (mean age: 53±11years; 42% female) without known coronary artery disease (CAD) or initially elevated serum biomarkers, whom underwent non-contrast CT, to assess the CAC score, and CT angiography (CTA), to detect coronary stenosis. We analyzed the diagnostic performance of CAC and the Thrombolysis In Myocardial Infarction (TIMI) risk score for our primary outcomes (ACS and obstructive CAD). No CAC was found in 54% (n=444) of all patients, 63% (n=524) had a TIMI score of 0 and 40% (n=328) had both. The prevalence of obstructive CAD was 16% for ≥50% stenosis and 8.7% for ≥70% stenosis. The incidence of ACS was 7.9%, (MI=11, UAP=54). The NPV of CAC=0 was 99.5% for ACS. The NPV of a combination of TIMI score=0 and no CAC was 89% for any CAD (any plaque or stenosis) and 99.7% for ≥50% stenosis. A 100% NPV was found for ≥70% stenosis and ACS, correctly identifying 328 (40%) patients. CONCLUSIONS The exclusion of CAC, in combination with clinical risk assessment, has high clinical value in patients with acute chest pain, as it identifies patients at low risk for ACS and obstructive CAD more accurately as compared to clinical risk assessment alone.
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Affiliation(s)
- Daniel O Bittner
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Friedrich-Alexander University Erlangen-Nürnberg (FAU), Department of Cardiology, University Hospital Erlangen, Germany.
| | - Richard A P Takx
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pedro V Staziaki
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sumbal Janjua
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tomas G Neilan
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nandini M Meyersohn
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael T Lu
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anand M Prabhakar
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - John T Nagurney
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Udo Hoffmann
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brian B Ghoshhajra
- Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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23
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Janjua SA, Staziaki PV, Szilveszter B, Takx RAP, Mayrhofer T, Hennessy O, Emami HA, Park J, Ivanov A, Hallett TR, Lu MT, Romero JM, Grinspoon SK, Hoffmann U, Zanni MV, Neilan TG. Presence, Characteristics, and Prognostic Associations of Carotid Plaque Among People Living With HIV. Circ Cardiovasc Imaging 2017; 10:CIRCIMAGING.116.005777. [PMID: 29021257 DOI: 10.1161/circimaging.116.005777] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 08/21/2017] [Indexed: 01/22/2023]
Abstract
BACKGROUND Data from broad populations have established associations between incidental carotid plaque and vascular events. Among people living with HIV (PLWHIV), the risk of vascular events is increased; however, whether incidental carotid plaque is increased and there is an association between incidental carotid plaque, plaque characteristics, and vascular events among PLWHIV is unclear. METHODS AND RESULTS Data from the multi-institutional Research Patient Data Registry were used. Presence and characteristics (high-risk plaque, including spotty calcification and low attenuation) of carotid plaque by computerized tomography among PLWHIV without known vascular disease were described. Data were compared with uninfected controls similar in age, sex, and cardiovascular risk factors, including diabetes mellitus, hyperlipidemia, and cigarette smoking to cases. Primary outcome was an atherosclerotic cardiovascular disease event, and secondary outcome was ischemic stroke. Cohort consisted of 209 PLWHIV (45±10 years, 72% male) and 168 controls. Using computerized tomography, PLWHIV without vascular disease had higher rates of any carotid plaque (34% versus 25%; P=0.04), noncalcified (18% versus 5%; P<0.001) and high-risk plaque (25% versus 16%; P=0.03). Over a follow-up of 3 years, 19 atherosclerotic cardiovascular disease events (9 strokes) occurred. Carotid plaque was independently associated with a 3-fold increase in atherosclerotic cardiovascular disease events among PLWHIV (hazard ratio, 2.91; confidence interval, 1.10-7.7, P=0.03) and a 4-fold increased risk of stroke (hazard ratio, 4.43; confidence interval, 1.17-16.70; P=0.02); high-risk plaque was associated with a 3-fold increased risk of atherosclerotic cardiovascular disease events and a 4-fold increased risk of stroke. CONCLUSIONS There is an increase in incidental carotid plaque, noncalcified plaque, and high-risk plaque among PLWHIV, and the presence and characteristics of carotid plaque are associated with subsequent vascular events.
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Affiliation(s)
- Sumbal A Janjua
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Pedro V Staziaki
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Balint Szilveszter
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Richard A P Takx
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Thomas Mayrhofer
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Orla Hennessy
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Hamed A Emami
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Jakob Park
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Alexander Ivanov
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Travis R Hallett
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Michael T Lu
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Javier M Romero
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Steven K Grinspoon
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Udo Hoffmann
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Markella V Zanni
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston
| | - Tomas G Neilan
- From the Cardiac MR PET CT Program, Department of Radiology (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Neuroradiology Division, Department of Radiology (J.M.R.), Program in Nutritional Metabolism (S.K.G., M.V.Z.), and Division of Cardiology, Department of Medicine (S.A.J., P.V.S., B.S., R.A.P.T., T.M., O.H., H.A.E., J.P., A.I., T.R.H., M.T.L., U.H., T.G.N.), Massachusetts General Hospital and Harvard Medical School, Boston.
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Addison D, Seidelmann SB, Janjua SA, Emami H, Staziaki PV, Hallett TR, Szilveszter B, Lu MT, Cambria RP, Hoffmann U, Chan AW, Wirth LJ, Neilan TG. Human Papillomavirus Status and the Risk of Cerebrovascular Events Following Radiation Therapy for Head and Neck Cancer. J Am Heart Assoc 2017; 6:JAHA.117.006453. [PMID: 28855164 PMCID: PMC5634292 DOI: 10.1161/jaha.117.006453] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background Radiation therapy (RT) is a standard treatment for head and neck cancer; however, it is associated with inflammation, accelerated atherosclerosis, and cerebrovascular events (CVEs; stroke or transient ischemic attack). Human papillomavirus (HPV) is found in nearly half of head and neck cancers and is associated with inflammation and atherosclerosis. Whether HPV confers an increased risk of CVEs after RT is unknown. Methods and Results Using an institutional database, we identified all consecutive patients treated with RT from 2002 to 2012 for head and neck cancer who were tested for HPV. The outcome of interest was the composite of ischemic stroke and transient ischemic attack, and the association between HPV and CVEs was assessed using Cox proportional hazard models, competing risk analysis, and inverse probability weighting. Overall, 326 participants who underwent RT for head and neck cancer were tested for HPV (age 59±12 years, 75% were male, 9% had diabetes mellitus, 45% had hypertension, and 61% were smokers), of which 191 (59%) were tumor HPV positive. Traditional risk factors for CVEs were similar between HPV‐positive and ‐negative patients. Over a median follow‐up of 3.4 years, there were 18 ischemic strokes and 5 transient ischemic attacks (event rate of 1.8% per year). The annual event rate was higher in the HPV‐positive patients compared with the HPV‐negative patients (2.6% versus 0.9%, P=0.002). In a multivariable model, HPV‐positive status was associated with a >4 times increased risk of CVEs (hazard ratio: 4.4; 95% confidence interval, 1.5–13.2; P=0.008). Conclusions In this study, HPV‐positive status is associated with an increased risk of stroke or transient ischemic attack following RT for head and neck cancer.
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Affiliation(s)
- Daniel Addison
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.,Cardio-Oncology Program, Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Sara B Seidelmann
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Sumbal A Janjua
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Hamed Emami
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Pedro V Staziaki
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Travis R Hallett
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Bálint Szilveszter
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Michael T Lu
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Richard P Cambria
- Division of Vascular and Endovascular Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Udo Hoffmann
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Annie W Chan
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Lori J Wirth
- Division of Oncology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Tomas G Neilan
- Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA .,Cardio-Oncology Program, Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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Ghoshhajra BB, Takx RAP, Staziaki PV, Vadvala H, Kim P, Neilan TG, Meyersohn NM, Bittner D, Janjua SA, Mayrhofer T, Greenwald JL, Truong QA, Abbara S, Brown DFM, Januzzi JL, Francis S, Nagurney JT, Hoffmann U. Clinical implementation of an emergency department coronary computed tomographic angiography protocol for triage of patients with suspected acute coronary syndrome. Eur Radiol 2017; 27:2784-2793. [PMID: 27885414 PMCID: PMC5976244 DOI: 10.1007/s00330-016-4562-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 08/03/2016] [Accepted: 08/11/2016] [Indexed: 12/27/2022]
Abstract
OBJECTIVES To evaluate the efficiency and safety of emergency department (ED) coronary computed tomography angiography (CTA) during a 3-year clinical experience. METHODS Single-center registry of coronary CTA in consecutive ED patients with suspicion of acute coronary syndrome (ACS). The primary outcome was efficiency of coronary CTA defined as the length of hospitalization. Secondary endpoints of safety were defined as the rate of downstream testing, normalcy rates of invasive coronary angiography (ICA), absence of missed ACS, and major adverse cardiac events (MACE) during follow-up, and index radiation exposure. RESULTS One thousand twenty two consecutive patients were referred for clinical coronary CTA with suspicion of ACS. Overall, median time to discharge home was 10.5 (5.7-24.1) hours. Patient disposition was 42.7 % direct discharge from the ED, 43.2 % discharge from emergency unit, and 14.1 % hospital admission. ACS rate during index hospitalization was 9.1 %. One hundred ninety two patients underwent additional diagnostic imaging and 77 underwent ICA. The positive predictive value of CTA compared to ICA was 78.9 % (95 %-CI 68.1-87.5 %). Median CT radiation exposure was 4.0 (2.5-5.8) mSv. No ACS was missed; MACE at follow-up after negative CTA was 0.2 %. CONCLUSIONS Coronary CTA in an experienced tertiary care setting allows for efficient and safe management of patients with suspicion for ACS. KEY POINTS • ED Coronary CTA using advanced systems is associated with low radiation exposure. • Negative coronary CTA is associated with low rates of MACE. • CTA in ED patients enables short median time to discharge home. • CTA strategy is characterized by few downstream tests including unnecessary ICA.
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Affiliation(s)
- Brian B Ghoshhajra
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA.
| | - Richard A P Takx
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pedro V Staziaki
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
| | - Harshna Vadvala
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
| | - Phillip Kim
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
| | - Tomas G Neilan
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
- Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nandini M Meyersohn
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
| | - Daniel Bittner
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
- Friedrich-Alexander University Erlangen-Nürnberg (FAU), Department of Medicine 2 - Cardiology, University Hospital Erlangen, Erlangen, Germany
| | - Sumbal A Janjua
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
| | - Thomas Mayrhofer
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
- School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
| | - Jeffrey L Greenwald
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Quyhn A Truong
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
- Department of Radiology, Weill Cornell College of Medicine, New York, NY, USA
| | - Suhny Abbara
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
- Department Cardiothoracic Imaging, UT Southwestern Medical Center, Dallas, TX, USA
| | - David F M Brown
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - James L Januzzi
- Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sanjeev Francis
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
- Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - John T Nagurney
- Department of Emergency Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Udo Hoffmann
- Cardiac MR PET CT Program, Department of Radiology (Cardiovascular Imaging) and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, MA, 02114-2750, USA
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Farhad H, Staziaki PV, Addison D, Coelho-Filho OR, Shah RV, Mitchell RN, Szilveszter B, Abbasi SA, Kwong RY, Scherrer-Crosbie M, Hoffmann U, Jerosch-Herold M, Neilan TG. Characterization of the Changes in Cardiac Structure and Function in Mice Treated With Anthracyclines Using Serial Cardiac Magnetic Resonance Imaging. Circ Cardiovasc Imaging 2017; 9:CIRCIMAGING.115.003584. [PMID: 27923796 DOI: 10.1161/circimaging.115.003584] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 09/29/2016] [Indexed: 12/17/2022]
Abstract
BACKGROUND Anthracyclines are cardiotoxic; however, there are limited data characterizing the serial changes in cardiac structure and function after anthracyclines. The aim of this study was to use cardiac magnetic resonance to characterize anthracycline-induced cardiotoxicity in mice. METHODS AND RESULTS This was a longitudinal cardiac magnetic resonance and histological study of 45 wild-type male mice randomized to doxorubicin (n=30, 5 mg/kg of doxorubicin/week for 5 weeks) or placebo (n=15). A cardiac magnetic resonance was performed at baseline and at 5, 10, and 20 weeks after randomization. Measures of primary interest included left ventricular ejection fraction, myocardial edema (multiecho short-axis spin-echo acquisition), and myocardial fibrosis (Look-Locker gradient echo). In doxorubicin-treated mice versus placebo, there was an increase in myocardial edema at 5 weeks (T2 values of 32±4 versus 21±3 ms; P<0.05), followed by a reduction in left ventricular ejection fraction (54±6 versus 63±5%; P<0.05) and an increase in myocardial fibrosis (extracellular volume of 0.34±0.03 versus 0.27±0.03; P<0.05) at 10 weeks. There was a strong association between the early (5 weeks) increase in edema and the subacute (10 weeks) increase in fibrosis (r=0.90; P<0.001). Both the increase in edema and fibrosis predicted the late doxorubicin-induced mortality in mice (P<0.001). CONCLUSIONS Our data suggest that, in mice, anthracycline-induced cardiotoxicity is associated with an early increase in cardiac edema and a subsequent increase in myocardial fibrosis. The early increase in edema and subacute increase in fibrosis are strongly linked and are both predictive of late mortality.
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Affiliation(s)
- Hoshang Farhad
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Pedro V Staziaki
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Daniel Addison
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Otavio R Coelho-Filho
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Ravi V Shah
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Richard N Mitchell
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Balint Szilveszter
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Siddique A Abbasi
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Raymond Y Kwong
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Marielle Scherrer-Crosbie
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Udo Hoffmann
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Michael Jerosch-Herold
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Tomas G Neilan
- From the Non-Invasive Cardiovascular Imaging Program and the Cardiovascular Division, Department of Medicine (H.F., S.A.A., R.V.S., R.Y.K.), Department of Pathology (R.N.M.), and Department of Radiology (M.J.-H.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Faculty of Medical Science, State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil (O.R.C.-F.); and Cardiac MR PET CT Program, Division of Radiology (P.V.S., D.A., B.S., U.H., T.G.N.) and Division of Cardiology, Department of Medicine (M.S.-C., T.G.N.), Massachusetts General Hospital, Harvard Medical School, Boston, MA.
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Meyersohn NM, Szilveszter B, Staziaki PV, Scholtz JE, Takx RAP, Hoffmann U, Ghoshhajra BB. Coronary CT angiography in the emergency department utilizing second and third generation dual source CT. J Cardiovasc Comput Tomogr 2017; 11:249-257. [PMID: 28506470 DOI: 10.1016/j.jcct.2017.03.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Revised: 03/17/2017] [Accepted: 03/19/2017] [Indexed: 01/28/2023]
Abstract
BACKGROUND Coronary computed tomography angiography (coronary CTA) allows efficient triage of low to intermediate risk patients with suspected acute coronary syndrome (ACS) in the emergency department (ED). Techniques for coronary CTA acquisition in the ED continue to evolve with the establishment of standardized scan protocols and the introduction of newer generations of CT hardware. OBJECTIVES To evaluate qualitative and quantitative image quality and radiation dose exposure of coronary CTA acquired on 2nd versus 3rd generation dual source CT (DSCT) scanners using a standardized institutional scan protocol designed for the ED. METHODS A retrospective observational case-control study was performed of 246 ED patients referred to coronary CTA with suspicion of ACS (56.5% male; mean age 53.3 ± 11.6 years) between October 2013 and August 2015.123 consecutive patients were scanned on 3rd generation DSCT, and a cohort of 123 patients matched by age, BMI and heart rate were identified who had undergone 2nd generation DSCT imaging utilizing the same standard clinical protocol. Qualitative and quantitative image quality parameters and radiation exposures were evaluated. RESULTS Qualitative image quality was significantly higher using 3rd generation DSCT as compared to 2nd generation (p < 0.001). Mean attenuation in the proximal coronary arteries was also significantly higher on 3rd generation DSCT than for 2nd generation (586 HU vs. 426 HU in the left main coronary artery (LM), p < 0.001). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values, however, were lower in 3rd generation DSCT than 2nd generation (SNR 11.2 [9.9-13.4] vs 13.5 [11.0-15.5] and CNR 12.4 [10.9-14.8] vs 15.2 [12.8-17.9] in the LM, p < 0.001). Median effective dose was also lower for 3rd generation DSCT than for 2nd generation (2.9 [2.3-5.0] mSv and 3.7 mSv [2.5-5.7], respectively) although this trend did not reach statistical significance (p = 0.065). CONCLUSION Qualitative image quality and mean CT attenuation values of the assessed coronary segments were significantly higher using 3rd generation DSCT. SNR and CNR were lower on 3rd generation DSCT, however this was accompanied by a trend toward lower radiation dose exposure when using the same standard institutional protocol.
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Affiliation(s)
- Nandini M Meyersohn
- Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
| | - Balint Szilveszter
- Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Pedro V Staziaki
- Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jan-Erik Scholtz
- Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Richard A P Takx
- Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Udo Hoffmann
- Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Brian B Ghoshhajra
- Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
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V. Staziaki P, M. Marques C, M. Delattre A, de Paula Cioni B, Rufino M, Vila dos Santos F, Licks F, P. Marroni N, C. Ferraz A. Fish Oil has Beneficial Effects on Behavior Impairment and Oxidative Stress in Rats Subjected to a Hepatic Encephalopathy Model. CNSNDDT 2013; 12:84-93. [DOI: 10.2174/1871527311312010014] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2012] [Revised: 09/17/2012] [Accepted: 09/20/2012] [Indexed: 11/22/2022]
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