1
|
Osborne-Grinter M, Ali A, Williams MC. Prevalence and clinical implications of coronary artery calcium scoring on non-gated thoracic computed tomography: a systematic review and meta-analysis. Eur Radiol 2024; 34:4459-4474. [PMID: 38133672 PMCID: PMC11213779 DOI: 10.1007/s00330-023-10439-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/02/2023] [Accepted: 09/07/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVES Coronary artery calcifications (CACs) indicate the presence of coronary artery disease. CAC can be found on thoracic computed tomography (CT) conducted for non-cardiac reasons. This systematic review and meta-analysis of non-gated thoracic CT aims to assess the clinical impact and prevalence of CAC. METHODS Online databases were searched for articles assessing prevalence, demographic characteristics, accuracy and prognosis of incidental CAC on non-gated thoracic CT. Meta-analysis was performed using a random effects model. RESULTS A total of 108 studies (113,406 patients) were included (38% female). Prevalence of CAC ranged from 2.7 to 100% (pooled prevalence 52%, 95% confidence interval [CI] 46-58%). Patients with CAC were older (pooled standardised mean difference 0.88, 95% CI 0.65-1.11, p < 0.001), and more likely to be male (pooled odds ratio [OR] 1.95, 95% CI 1.55-2.45, p < 0.001), with diabetes (pooled OR 2.63, 95% CI 1.95-3.54, p < 0.001), hypercholesterolaemia (pooled OR 2.28, 95% CI 1.33-3.93, p < 0.01) and hypertension (pooled OR 3.89, 95% CI 2.26-6.70, p < 0.001), but not higher body mass index or smoking. Non-gated CT assessment of CAC had excellent agreement with electrocardiogram-gated CT (pooled correlation coefficient 0.96, 95% CI 0.92-0.98, p < 0.001). In 51,582 patients, followed-up for 51.6 ± 27.4 months, patients with CAC had increased all cause mortality (pooled relative risk [RR] 2.13, 95% CI 1.57-2.90, p = 0.004) and major adverse cardiovascular events (pooled RR 2.91, 95% CI 2.26-3.93, p < 0.001). When CAC was present on CT, it was reported in between 18.6% and 93% of reports. CONCLUSION CAC is a common, but underreported, finding on non-gated CT with important prognostic implications. CLINICAL RELEVANCE STATEMENT Coronary artery calcium is an important prognostic indicator of cardiovascular disease. It can be assessed on non-gated thoracic CT and is a commonly underreported finding. This represents a significant population where there is a potential missed opportunity for lifestyle modification recommendations and preventative therapies. This study aims to highlight the importance of reporting incidental coronary artery calcium on non-gated thoracic CT. KEY POINTS • Coronary artery calcification is a common finding on non-gated thoracic CT and can be reliably identified compared to gated-CT. • Coronary artery calcification on thoracic CT is associated with an increased risk of all cause mortality and major adverse cardiovascsular events. • Coronary artery calcification is frequently not reported on non-gated thoracic CT.
Collapse
Affiliation(s)
- Maia Osborne-Grinter
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
- University of Bristol, Bristol, UK.
| | - Adnan Ali
- School of Medicine, University of Dundee, Dundee, UK
| | - Michelle C Williams
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging Facility QMRI, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
2
|
Anokye R, Via JD, Dimmock J, Jackson B, Schultz C, Schæffer M, Dickson JM, Blekkenhorst LC, Stanley M, Hodgson JM, Lewis JR. Impact of Cardiovascular Imaging Results on Medication Use and Adherence: A Systematic Review and Meta-Analysis. Am J Prev Med 2024:S0749-3797(24)00193-4. [PMID: 38876293 DOI: 10.1016/j.amepre.2024.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/16/2024]
Abstract
INTRODUCTION Cardiovascular imaging results offer valuable information that can guide health decisions, but their impact on medication use and adherence is unclear. This systematic review and meta-analysis aimed to determine the downstream impact of cardiovascular imaging results on medication use and adherence. METHODS Searches were conducted across databases, including MEDLINE, PsychINFO, EMBASE, and relevant references up to 2024. Data were extracted from studies comparing outcomes for individuals with diseased versus normal arteries and trials comparing outcomes for individuals who were provided imaging results versus those with no access to imaging results and analysed in 2023 and 2024. Pooled odds ratios (ORs) for outcomes were calculated. RESULTS The analysis included 29 studies with 24 contributing data points. Initiation (OR:2.77;95% CI:1.82-4.20) and continuation (OR:2.06;95% CI:1.28-3.30) of lipid-lowering medications (LLMs), antihypertensives (OR:2.02;95% CI:1.76-2.33), and antiplatelets (OR:2.47;95% CI:1.68-3.64) were significantly higher in individuals with diseased arteries. The proportion of individuals on LLM increased by 2.7-fold in those with diseased arteries and 1.5-fold in those with normal arteries post-screening. The proportion on LLM increased by 4.2 times in the imaging group and 2.2 times in the "no imaging group" post-screening. There was a significant increase in LLM initiation (OR:2.37;95% CI: 1.17-4.79) in the imaging group, but medication continuation did not significantly differ between the imaging and "no imaging group". DISCUSSION Cardiovascular imaging results can prompt initiation of medications, particularly lipid-lowering medications, reflecting a proactive response to identified risk factors. However, evidence regarding medication continuation is mixed, and further research is required.
Collapse
Affiliation(s)
- Reindolf Anokye
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia.
| | - Jack Dalla Via
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Royal Perth Hospital Research Foundation, Perth, Western Australia, Australia
| | - James Dimmock
- Department of Psychology, College of Healthcare Sciences, James Cook University, Townsville, Queensland, Australia
| | - Ben Jackson
- Telethon Kids Institute, Perth, Western Australia, Australia; School of Human Sciences (Exercise and Sport Science), The University of Western Australia, Perth, Western Australia, Australia
| | - Carl Schultz
- Cardiology Department, Royal Perth Hospital, Perth, Western Australia, Australia; Medical School, The University of Western Australia, Perth, Western Australia, Australia
| | - Mie Schæffer
- Department of Cardiology, Odense University Hospital, Denmark
| | - Joanne M Dickson
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Royal Perth Hospital Research Foundation, Perth, Western Australia, Australia; School of Arts and Humanities (Psychology Discipline), Edith Cowan University, Joondalup, Western Australia, Australia; Department of Psychological Science, University of Liverpool, UK
| | - Lauren C Blekkenhorst
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Royal Perth Hospital Research Foundation, Perth, Western Australia, Australia; Medical School, The University of Western Australia, Perth, Western Australia, Australia
| | - Mandy Stanley
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
| | - Jonathan M Hodgson
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Royal Perth Hospital Research Foundation, Perth, Western Australia, Australia; Medical School, The University of Western Australia, Perth, Western Australia, Australia
| | - Joshua R Lewis
- Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Royal Perth Hospital Research Foundation, Perth, Western Australia, Australia; Medical School, The University of Western Australia, Perth, Western Australia, Australia; Centre for Kidney Research, Children's Hospital at Westmead, School of Public Health, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia
| |
Collapse
|
3
|
Malik RF, Sun KJ, Azadi JR, Lau BD, Whelton S, Arbab-Zadeh A, Wilson RF, Johnson PT. Opportunistic Screening for Coronary Artery Disease: An Untapped Population Health Resource. J Am Coll Radiol 2024; 21:880-889. [PMID: 38382860 DOI: 10.1016/j.jacr.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 01/31/2024] [Accepted: 02/13/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Coronary artery disease is the leading cause of death in the United States. At-risk asymptomatic adults are eligible for screening with electrocardiogram-gated coronary artery calcium (CAC) CT, which aids in risk stratification and management decision-making. Incidental CAC (iCAC) is easily quantified on chest CT in patients imaged for noncardiac indications; however, radiologists do not routinely report the finding. OBJECTIVE To determine the clinical significance of CAC identified incidentally on routine chest CT performed for noncardiac indications. DESIGN An informationist developed search strategies in MEDLINE, Embase, and SCOPUS, and two reviewers independently screened results at both the abstract and full text levels. Data extracted from eligible articles included age, rate of iCAC identification, radiologist reporting frequency, impact on downstream medical management, and association of iCAC with patient outcomes. RESULTS From 359 unique citations, 83 research publications met inclusion criteria. The percentage of patients with iCAC ranged from 9% to 100%. Thirty-one investigations measured association(s) between iCAC and cardiovascular morbidity and mortality, and 29 identified significant correlations, including nonfatal myocardial infarction, fatal myocardial infarction, major adverse cardiovascular event, cardiovascular death, and all-cause death. iCAC was present in 20% to 100% of the patients in these cohorts, but when present, iCAC was reported by radiologists in only 31% to 44% of cases. Between 18% and 77% of patients with iCAC were not on preventive medications in studies that reported these data. Seven studies measured the effect of reporting on guideline directed medical therapy, and 5 (71%) reported an increase in medication prescriptions after diagnosis of iCAC, with one confirming reductions in low-density lipoprotein levels. Twelve investigations reported good concordance between CAC grade on noncardiac CT and Agatston score on electrocardiogram-gated cardiac CT, and 10 demonstrated that artificial intelligence tools can reliably calculate an Agatston score on noncardiac CT. CONCLUSION A body of evidence demonstrates that patients with iCAC on routine chest CT are at risk for cardiovascular disease events and death, but they are often undiagnosed. Uniform reporting of iCAC in the chest CT impression represents an opportunity for radiology to contribute to early identification of high-risk individuals and potentially reduce morbidity and mortality. AI tools have been validated to calculate Agatston score on routine chest CT and hold the best potential for facilitating broad adoption.
Collapse
Affiliation(s)
- Rubab F Malik
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kristie J Sun
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Javad R Azadi
- Assistant Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Brandyn D Lau
- Assistant Professor of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Seamus Whelton
- Associate Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Armin Arbab-Zadeh
- Director of Cardiac CT, Professor of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Renee F Wilson
- Evidence Based Practice Center, Johns Hopkins University School of Public Health, Baltimore, Maryland
| | - Pamela T Johnson
- Vice President of Care Transformation, Vice Chair of Quality and Safety in Radiology, and Professor of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| |
Collapse
|
4
|
Foldyna B, Hadzic I, Zeleznik R, Langenbach MC, Raghu VK, Mayrhofer T, Lu MT, Aerts HJWL. Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers. COMMUNICATIONS MEDICINE 2024; 4:44. [PMID: 38480863 PMCID: PMC10937640 DOI: 10.1038/s43856-024-00475-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium. METHODS We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9-12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke). RESULTS Here we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score. CONCLUSIONS Our findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.
Collapse
Affiliation(s)
- Borek Foldyna
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Ibrahim Hadzic
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
| | - Roman Zeleznik
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Marcel C Langenbach
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Vineet K Raghu
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas Mayrhofer
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
| | - Michael T Lu
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
5
|
Lin Y, Lin G, Peng MT, Kuo CT, Wan YL, Cherng WJ. The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels. J Thorac Imaging 2024; 39:111-118. [PMID: 37982516 DOI: 10.1097/rti.0000000000000765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
PURPOSE To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels. PATIENTS AND METHODS Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ). RESULTS The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively). CONCLUSIONS There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.
Collapse
Affiliation(s)
- Yenpo Lin
- Department of Medical Imaging and Intervention
| | - Gigin Lin
- Department of Medical Imaging and Intervention
| | | | - Chi-Tai Kuo
- Division of Cardiology, Department of Internal Medicine; Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | | | - Wen-Jin Cherng
- Division of Cardiology, Department of Internal Medicine; Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| |
Collapse
|
6
|
Hammer MM, Byrne SC, Blankstein R. Reporting and Outcomes of Coronary Calcification on Lung Cancer Screening CT. Acad Radiol 2023; 30:1614-1619. [PMID: 36460581 DOI: 10.1016/j.acra.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/27/2022] [Accepted: 11/11/2022] [Indexed: 12/05/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the accuracy and downstream testing and statin prescribing of real-world reporting of coronary calcification on lung cancer screening (LCS) CT. MATERIALS AND METHODS We retrospectively reviewed LCS CTs from January 2015 to November 2021 for reporting of coronary calcification; reports that denoted coronary calcification as a significant incidental finding ("S" modifier) were also noted. We evaluated calcium scoring accuracy in patients in whom a cardiac or calcium scoring CT was performed within 1 year of the LCS CT. For the first LCS CT in all patients, we evaluated whether a stress test was performed within 6 months and whether a new statin prescription was written within 90 days of the LCS CT. Patients were stratified by atherosclerotic cardiovascular disease (ASCVD) risk group, used in a multivariable regression analysis for new statin prescriptions. RESULTS Eight thousand nine hundred eighty-seven patients underwent screening. In 117 patients who had a paired cardiac CT, scores were concordant in 65 (56%), and LCS CTs did not mention or underestimated calcifications in 40 (34%). Reporting of coronary artery calcifications led to new statin prescriptions, with OR of 1.8 for calcifications without S modifier and 4.4 for calcifications with S modifier. Reporting of coronary artery calcification with S modifier led to subsequent stress testing in 141/1582 (9%) of patients. CONCLUSION Coronary calcifications are frequently not mentioned or underestimated at LCS CT. Reporting of coronary calcifications leads to new statin prescriptions, and radiologists should consider reporting these to allow for a risk-benefit discussion with the patient's physician.
Collapse
Affiliation(s)
- Mark M Hammer
- Department of Radiology, Brigham and Women's Hospital, Boston, 75 Francis St, Boston, 02115, Massachusetts; Harvard Medical School, Boston, Massachusetts.
| | - Suzanne C Byrne
- Department of Radiology, Brigham and Women's Hospital, Boston, 75 Francis St, Boston, 02115, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Ron Blankstein
- Department of Radiology, Brigham and Women's Hospital, Boston, 75 Francis St, Boston, 02115, Massachusetts; Harvard Medical School, Boston, Massachusetts; Cardiovascular Imaging Program, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| |
Collapse
|
7
|
Patel P, Flores R, Alpert N, Pyenson B, Taioli E. Effect of stage shift and immunotherapy treatment on lung cancer survival outcomes. Eur J Cardiothorac Surg 2023; 64:ezad203. [PMID: 37285318 PMCID: PMC10412408 DOI: 10.1093/ejcts/ezad203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/01/2023] [Accepted: 06/06/2023] [Indexed: 06/09/2023] Open
Abstract
OBJECTIVES Non-small-cell lung cancer mortality has declined at a faster rate than incidence due to multiple factors, including changes in smoking behaviour, early detection which shifts diagnosis, and novel therapies. Limited resources require that we quantify the contribution of early detection versus novel therapies in improving lung cancer survival outcomes. METHODS Non-small-cell lung cancer patients from the Surveillance, Epidemiology, and End Results-Medicare data were queried and divided into: (i) stage IV diagnosed in 2015 (n = 3774) and (ii) stage I-III diagnosed in 2010-2012 (n = 15 817). Multivariable Cox-proportional hazards models were performed to assess the independent association of immunotherapy or diagnosis at stage I/II versus III with survival. RESULTS Patients treated with immunotherapy had significantly better survival than those who did not (HRadj: 0.49, 95% confidence interval: 0.43-0.56), as did those diagnosed at stage I/II versus stage III (HRadj: 0.36, 95% confidence interval: 0.35-0.37). Patients on immunotherapy had a 10.7-month longer survival than those who were not. Stage I/II patients had an average survival benefit of 34 months, compared to stage III. If 25%% of stage IV patients not on immunotherapy received it, there would be a gain of 22 292 person-years survival per 100 000 diagnoses. A switch of only 25% from stage III to stage I/II would correspond to 70 833 person-years survival per 100 000 diagnoses. CONCLUSIONS In this cohort study, earlier stage at diagnosis contributed to life expectancy by almost 3 years, while gains from immunotherapy would contribute ½ year of survival. Given the relative affordability of early detection, risk reduction through increased screening should be optimized.
Collapse
Affiliation(s)
- Parth Patel
- Department of Thoracic Surgery, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA
| | - Raja Flores
- Department of Thoracic Surgery, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA
| | - Naomi Alpert
- Institute for Translational Epidemiology and Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce Pyenson
- NYU School of Global Public Health, New York University, New York, NY, USA
- Milliman Inc., New York, NY, USA
| | - Emanuela Taioli
- Department of Thoracic Surgery, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA
- Institute for Translational Epidemiology and Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
8
|
Zhang Y, Grant BMM, Hope AJ, Hung RJ, Warkentin MT, Lam ACL, Aggawal R, Xu M, Shepherd FA, Tsao MS, Xu W, Pakkal M, Liu G, McInnis MC. Using Recurrent Neural Networks to Extract High-Quality Information From Lung Cancer Screening Computerized Tomography Reports for Inter-Radiologist Audit and Feedback Quality Improvement. JCO Clin Cancer Inform 2023; 7:e2200153. [PMID: 36930839 DOI: 10.1200/cci.22.00153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
PURPOSE Lung cancer screening programs generate a high volume of low-dose computed tomography (LDCT) reports that contain valuable information, typically in a free-text format. High-performance named-entity recognition (NER) models can extract relevant information from these reports automatically for inter-radiologist quality control. METHODS Using LDCT report data from a longitudinal lung cancer screening program (8,305 reports; 3,124 participants; 2006-2019), we trained a rule-based model and two bidirectional long short-term memory (Bi-LSTM) NER neural network models to detect clinically relevant information from LDCT reports. Model performance was tested using F1 scores and compared with a published open-source radiology NER model (Stanza) in an independent evaluation set of 150 reports. The top performing model was applied to a data set of 6,948 reports for an inter-radiologist quality control assessment. RESULTS The best performing model, a Bi-LSTM NER recurrent neural network model, had an overall F1 score of 0.950, which outperformed Stanza (F1 score = 0.872) and a rule-based NER model (F1 score = 0.809). Recall (sensitivity) for the best Bi-LSTM model ranged from 0.916 to 0.991 for different entity types; precision (positive predictive value) ranged from 0.892 to 0.997. Test performance remained stable across time periods. There was an average of a 2.86-fold difference in the number of identified entities between the most and the least detailed radiologists. CONCLUSION We built an open-source Bi-LSTM NER model that outperformed other open-source or rule-based radiology NER models. This model can efficiently extract clinically relevant information from lung cancer screening computerized tomography reports with high accuracy, enabling efficient audit and feedback to improve quality of patient care.
Collapse
Affiliation(s)
- Yucheng Zhang
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Benjamin M M Grant
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Andrew J Hope
- Radiation Medicine Program, Princess Margaret Cancer Centre, and Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health Systems, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Matthew T Warkentin
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health Systems, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Andrew C L Lam
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Reenika Aggawal
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Maria Xu
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Frances A Shepherd
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Ming-Sound Tsao
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Laboratory Medicine and Pathology, University Health Network, Toronto, ON, Canada
| | - Wei Xu
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Computational Biology and Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Mini Pakkal
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Cardiothoracic Imaging, Joint Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada
| | - Geoffrey Liu
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Micheal C McInnis
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Cardiothoracic Imaging, Joint Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada
| |
Collapse
|
9
|
Voigt W, Prosch H, Silva M. Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges? Cancers (Basel) 2023; 15:cancers15041218. [PMID: 36831559 PMCID: PMC9954060 DOI: 10.3390/cancers15041218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/05/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.
Collapse
Affiliation(s)
- Wieland Voigt
- Medical Innovation and Management, Steinbeis University Berlin, Ernst-Augustin-Strasse 15, 12489 Berlin, Germany
- Correspondence:
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, General Hospital, 1090 Vienna, Austria
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
| |
Collapse
|
10
|
Lopez-Mattei J, Yang EH, Baldassarre LA, Agha A, Blankstein R, Choi AD, Chen MY, Meyersohn N, Daly R, Slim A, Rochitte C, Blaha M, Whelton S, Dzaye O, Dent S, Milgrom S, Ky B, Iliescu C, Mamas MA, Ferencik M. Cardiac computed tomographic imaging in cardio-oncology: An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT). Endorsed by the International Cardio-Oncology Society (ICOS). J Cardiovasc Comput Tomogr 2023; 17:66-83. [PMID: 36216699 DOI: 10.1016/j.jcct.2022.09.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 09/01/2022] [Accepted: 09/12/2022] [Indexed: 11/21/2022]
Abstract
Cardio-Oncology is a rapidly growing sub-specialty of medicine, however, there is very limited guidance on the use of cardiac CT (CCT) in the care of Cardio-Oncology patients. In order to fill in the existing gaps, this Expert Consensus statement comprised of a multidisciplinary collaboration of experts in Cardiology, Radiology, Cardiovascular Multimodality Imaging, Cardio-Oncology, Oncology and Radiation Oncology aims to summarize current evidence for CCT applications in Cardio-Oncology and provide practice recommendations for clinicians.
Collapse
Affiliation(s)
| | - Eric H Yang
- UCLA Cardio-Oncology Program, Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | | | - Ali Agha
- Department of Cardiology, Baylor College of Medicine, Houston, TX, USA
| | - Ron Blankstein
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Andrew D Choi
- Division of Cardiology and Department of Radiology, The George Washington University School of Medicine, Washington, DC, USA
| | - Marcus Y Chen
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nandini Meyersohn
- Division of Cardiovascular Imaging, Department of Radiology, Massachusetts General Hospital, USA
| | - Ryan Daly
- Franciscan Health Indianapolis, Indianapolis, IN, USA
| | | | - Carlos Rochitte
- InCor Heart Institute, University of São Paulo Medical School, São Paulo, Brazil
| | - Michael Blaha
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Seamus Whelton
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Omar Dzaye
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD, USA
| | - Susan Dent
- Duke Cancer Institute, Department of Medicine, Duke University, Durham, NC, USA
| | - Sarah Milgrom
- Department of Radiation Oncology, University of Colorado, Boulder, CO, USA
| | - Bonnie Ky
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Cezar Iliescu
- Heart and Vascular Institute, Lee Health, Fort Myers, FL, USA
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Centre for Prognosis Research, Keele University, UK
| | - Maros Ferencik
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA
| |
Collapse
|
11
|
Mędrek S, Szmit S. Are cardiovascular comorbidities always associated with a worse prognosis in patients with lung cancer? Front Cardiovasc Med 2022; 9:984951. [PMID: 36211566 PMCID: PMC9537604 DOI: 10.3389/fcvm.2022.984951] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 09/01/2022] [Indexed: 12/01/2022] Open
Abstract
Many factors contribute to mortality in lung cancer, including the presence of concomitant cardiovascular disease. In the treatment of early stage of lung cancer, the presence of comorbidities and occurence of cardiotoxicity may be prognostic. The effect of cardiotoxicity of radiotherapy and chemoradiotherapy on overall survival has been documented. Acute arterial and venous thromboembolic events seem to correlate with the degree of the histological malignancy, its clinical advancement, and even with optimal cardiac treatment, they may influence the survival time. In the case of high-grade and advanced lung cancer stage especially in an unresectable stadium, the prognosis depends primarily on the factors related to the histopathological and molecular diagnosis. Electrocardiographic and echocardiographic abnormalities may be prognostic factors, as they seem to correlate with the patient's performance status as well as tumor localization and size.
Collapse
Affiliation(s)
- Sabina Mędrek
- Department of Cardiology, Subcarpathian Oncological Center, Brzozów, Poland
- *Correspondence: Sabina Mędrek
| | - Sebastian Szmit
- Department of Pulmonary Circulation, Thromboembolic Diseases and Cardiology, Centre of Postgraduate Medical Education, European Health Centre, Otwock, Poland
| |
Collapse
|
12
|
Majeed A, Ruane B, Shusted CS, Austin M, Mirzozoda K, Pimpinelli M, Vojnika J, Ward L, Sundaram B, Lakhani P, Kane G, Lev Y, Barta JA. Frequency of Statin Prescription Among Individuals with Coronary Artery Calcifications Detected Through Lung Cancer Screening. Am J Med Qual 2022; 37:388-395. [PMID: 35302536 DOI: 10.1097/jmq.0000000000000053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Individuals eligible for lung cancer screening (LCS) are at risk for atherosclerotic cardiovascular disease (ASCVD) due to smoking history. Coronary artery calcifications (CAC), a common incidental finding on low-dose CT (LDCT) for LCS, is a predictor of cardiovascular events. Despite findings of high ASCVD risk and CAC, a substantial proportion of LCS patients are not prescribed primary preventive statin therapy for ASCVD. We assessed the frequency of statin prescription in LCS patients with moderate levels of CAC. Among 259 individuals with moderate CAC, 95% had ASCVD risk ≥ 7.5%. Despite this, 27% of patients were statin-free prior to LDCT and 21.2% remained statin-free after LDCT showing moderate CAC. Illustratively, while a substantial proportion of LCS patients are statin-eligible, many lack a statin prescription, even after findings of CAC burden. CAC reporting should be standardized, and interdisciplinary communication should be optimized to ensure that LCS patients are placed on appropriate preventive therapy.
Collapse
Affiliation(s)
- Amry Majeed
- Sidney Kimmel Medical College at Thomas Jefferson University
| | - Brooke Ruane
- Division of Pulmonary and Critical Care Medicine, The Jane and Leonard Korman Respiratory Institute, Thomas Jefferson University
| | - Christine S Shusted
- Division of Pulmonary and Critical Care Medicine, The Jane and Leonard Korman Respiratory Institute, Thomas Jefferson University
| | - Melissa Austin
- Sidney Kimmel Medical College at Thomas Jefferson University
| | - Khulkar Mirzozoda
- Department of Medicine, Division of Internal Medicine, Thomas Jefferson University
| | | | - Jetmir Vojnika
- Department of Medicine, Division of Internal Medicine, Thomas Jefferson University
| | - Lawrence Ward
- Department of Medicine, Division of Internal Medicine, Thomas Jefferson University
| | | | - Paras Lakhani
- Department of Radiology, Thomas Jefferson University
| | - Gregory Kane
- Division of Pulmonary and Critical Care Medicine, The Jane and Leonard Korman Respiratory Institute, Thomas Jefferson University
| | - Yair Lev
- Division of Cardiology, Thomas Jefferson University
| | - Julie A Barta
- Division of Pulmonary and Critical Care Medicine, The Jane and Leonard Korman Respiratory Institute, Thomas Jefferson University
| |
Collapse
|
13
|
Abstract
Background: Lung-RADS category 3 and 4 nodules account for most cancers among screening-detected lung cancers and are considered actionable nodules with management implications. The cancer frequency among such nodules is estimated in the Lung-RADS recommendations and has been investigated primarily using retrospectively assigned Lung-RADS classifications. Objective: To assess the frequency of cancer among lung nodules assigned Lung-RADS category 3 or 4 at lung cancer screening (LCS) in clinical practice and factors that impact the cancer frequency within each category. Methods: This retrospective study was based on review of clinical radiology reports of 9148 consecutive low-dose CT LCS examinations performed in 4798 patients between June 2014 and January 2021 as part of an established LCS program. Unique nodules assigned Lung-RADS category 3 or 4 (4A, 4B, or 4X) that were clinically categorized as benign or malignant by a multidisciplinary conference, considering histologic analysis and follow-up imaging, were selected for further analysis; benign diagnoses based on stability required at least 12 months of follow-up imaging. Indeterminate nodules were excluded. Cancer frequencies were evaluated. Results: Of the 9148 LCS examinations, 857 (9.4%) were assigned Lung-RADS category 3, and 721 (7.9%) were assigned category 4. The final analysis included 1297 unique nodules in 1139 patients (598 men, 541 women; mean age, 66.0±6.3 years). A total of 1108/1297 (85.4%) nodules were deemed benign, and 189/1297 (14.6%) were deemed malignant. Frequencies of malignancy for category 3, 4A, 4B, and 4X nodules were 3.9%, 15.5%, 36.3%, and 76.8%, respectively. A total of 45/46 (97.8%) endobronchial nodules (all category 4A) were deemed benign based on resolution. Cancer frequency was 13.1%, 24.4%, and 13.5% for solid, part-solid, and ground-glass nodules, respectively. Conclusions: When applying Lung-RADS for LCS clinical practice, the frequency of Lung-RADS category 3 and 4 nodules, as well as cancer frequency in these categories, were higher than prevalence and cancer risk estimated for category 3 and 4 nodules in the Lung-RADS recommendations and reported in earlier studies using retrospective category assignments. Nearly all endobronchial category 4A nodules were benign. Clinical Impact: Future Lung-RADS iterations should consider these findings from real-world practice to improve the system's clinical utility.
Collapse
|
14
|
Mazzone PJ, Silvestri GA, Souter LH, Caverly TJ, Kanne JP, Katki HA, Wiener RS, Detterbeck FC. Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. Chest 2021; 160:e427-e494. [PMID: 34270968 PMCID: PMC8727886 DOI: 10.1016/j.chest.2021.06.063] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/11/2021] [Accepted: 06/16/2021] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND Low-dose chest CT screening for lung cancer has become a standard of care in the United States, in large part because of the results of the National Lung Screening Trial (NLST). Additional evidence supporting the net benefit of low-dose chest CT screening for lung cancer, and increased experience in minimizing the potential harms, has accumulated since the prior iteration of these guidelines. Here, we update the evidence base for the benefit, harms, and implementation of low-dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not. METHODS Approved panelists reviewed previously developed key questions using the Population, Intervention, Comparator, Outcome format to address the benefit and harms of low-dose CT screening, and key areas of program implementation. A systematic literature review was conducted using MEDLINE via PubMed, Embase, and the Cochrane Library on a quarterly basis since the time of the previous guideline publication. Reference lists from relevant retrievals were searched, and additional papers were added. Retrieved references were reviewed for relevance by two panel members. The quality of the evidence was assessed for each critical or important outcome of interest using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Meta-analyses were performed when enough evidence was available. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and ungraded statements were drafted, voted on, and revised until consensus was reached. RESULTS The systematic literature review identified 75 additional studies that informed the response to the 12 key questions that were developed. Additional clinical questions were addressed resulting in seven graded recommendations and nine ungraded consensus statements. CONCLUSIONS Evidence suggests that low-dose CT screening for lung cancer can result in a favorable balance of benefit and harms. The selection of screen-eligible individuals, the quality of imaging and image interpretation, the management of screen-detected findings, and the effectiveness of smoking cessation interventions can impact this balance.
Collapse
Affiliation(s)
| | | | | | - Tanner J Caverly
- Ann Arbor VA Center for Clinical Management Research, Ann Arbor, MI; University of Michigan Medical School, Ann Arbor, MI
| | - Jeffrey P Kanne
- University of Wisconsin School of Medicine and Public Health, Madison, WI
| | | | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA; Boston University School of Medicine, Boston, MA
| | | |
Collapse
|
15
|
Rankin NM, McWilliams A, Marshall HM. Lung cancer screening implementation: Complexities and priorities. Respirology 2021; 25 Suppl 2:5-23. [PMID: 33200529 DOI: 10.1111/resp.13963] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 10/06/2020] [Indexed: 12/17/2022]
Abstract
Lung cancer is the number one cause of cancer death worldwide. The benefits of lung cancer screening to reduce mortality and detect early-stage disease are no longer in any doubt based on the results of two landmark trials using LDCT. Lung cancer screening has been implemented in the US and South Korea and is under consideration by other communities. Successful translation of demonstrated research outcomes into the routine clinical setting requires careful implementation and co-ordinated input from multiple stakeholders. Implementation aspects may be specific to different healthcare settings. Important knowledge gaps remain, which must be addressed in order to optimize screening benefits and minimize screening harms. Lung cancer screening differs from all other cancer screening programmes as lung cancer risk is driven by smoking, a highly stigmatized behaviour. Stigma, along with other factors, can impact smokers' engagement with screening, meaning that smokers are generally 'hard to reach'. This review considers critical points along the patient journey. The first steps include selecting a risk threshold at which to screen, successfully engaging the target population and maximizing screening uptake. We review barriers to smoker engagement in lung and other cancer screening programmes. Recruitment strategies used in trials and real-world (clinical) programmes and associated screening uptake are reviewed. To aid cross-study comparisons, we propose a standardized nomenclature for recording and calculating recruitment outcomes. Once participants have engaged with the screening programme, we discuss programme components that are critical to maximize net benefit. A whole-of-programme approach is required including a standardized and multidisciplinary approach to pulmonary nodule management, incorporating probabilistic nodule risk assessment and longitudinal volumetric analysis, to reduce unnecessary downstream investigations and surgery; the integration of smoking cessation; and identification and intervention for other tobacco related diseases, such as coronary artery calcification and chronic obstructive pulmonary disease. National support, integrated with tobacco control programmes, and with appropriate funding, accreditation, data collection, quality assurance and reporting mechanisms will enhance lung cancer screening programme success and reduce the risks associated with opportunistic, ad hoc screening. Finally, implementation research must play a greater role in informing policy change about targeted LDCT screening programmes.
Collapse
Affiliation(s)
- Nicole M Rankin
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Annette McWilliams
- Department of Respiratory Medicine, Fiona Stanley Hospital, Perth, WA, Australia.,Faculty of Health and Medical Sciences, University of Western Australia, Perth, WA, Australia.,Thoracic Tumour Collaborative of Western Australia, Western Australia Cancer and Palliative Care Network, Perth, WA, Australia
| | - Henry M Marshall
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, QLD, Australia.,The University of Queensland Thoracic Research Centre, Brisbane, QLD, Australia
| |
Collapse
|
16
|
Chao H, Shan H, Homayounieh F, Singh R, Khera RD, Guo H, Su T, Wang G, Kalra MK, Yan P. Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nat Commun 2021; 12:2963. [PMID: 34017001 PMCID: PMC8137697 DOI: 10.1038/s41467-021-23235-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 04/20/2021] [Indexed: 12/14/2022] Open
Abstract
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieves an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identifies patients with high CVD mortality risks (AUC of 0.768). We validate our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects. Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.
Collapse
Affiliation(s)
- Hanqing Chao
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Hongming Shan
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ramandeep Singh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ruhani Doda Khera
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hengtao Guo
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Timothy Su
- Niskayuna High School, Niskayuna, NY, USA
| | - Ge Wang
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA.
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Pingkun Yan
- Department of Biomedical Engineering, Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA.
| |
Collapse
|
17
|
Chamberlin J, Kocher MR, Waltz J, Snoddy M, Stringer NFC, Stephenson J, Sahbaee P, Sharma P, Rapaka S, Schoepf UJ, Abadia AF, Sperl J, Hoelzer P, Mercer M, Somayaji N, Aquino G, Burt JR. Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value. BMC Med 2021; 19:55. [PMID: 33658025 PMCID: PMC7931546 DOI: 10.1186/s12916-021-01928-3] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/26/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. METHODS A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. RESULTS Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). CONCLUSION We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.
Collapse
Affiliation(s)
- Jordan Chamberlin
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Madison R Kocher
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Jeffrey Waltz
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Madalyn Snoddy
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Natalie F C Stringer
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Joseph Stephenson
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | | | | | | | - U Joseph Schoepf
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Andres F Abadia
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | | | | | - Megan Mercer
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Nayana Somayaji
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Gilberto Aquino
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA
| | - Jeremy R Burt
- Department of Radiology, Medical University of South Carolina, Charleston, SC, 29403, USA.
- MUSC-ART, Cardiothoracic Imaging, 25 Courtenay Drive, MSC 226, 2nd Floor, Rm 2256, Charleston, SC, 29425, USA.
| |
Collapse
|
18
|
Shipe ME, Maiga AW, Deppen SA, Edwards GC, Marmor HN, Pinkerman R, Smith GT, Lio E, Wright JL, Shah C, Nesbitt JC, Grogan EL. Preoperative coronary artery calcifications in veterans predict higher all-cause mortality in early-stage lung cancer: a cohort study. J Thorac Dis 2021; 13:1427-1433. [PMID: 33841935 PMCID: PMC8024847 DOI: 10.21037/jtd-20-2102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background Lung cancer patients often have comorbidities that may impact survival. This observational cohort study examines whether coronary artery calcifications (CAC) impact all-cause mortality in patients with resected stage I non-small cell lung cancer (NSCLC). Methods Veterans with stage I NSCLC who underwent resection at a single institution between 2005 and 2018 were selected from a prospectively collected database. Radiologists blinded to patient outcomes graded CAC severity (mild, moderate, or severe) in preoperative CT scans using a visual estimation scoring system. Inter-rater reliability was calculated using the kappa statistic. All-cause mortality was the primary outcome. Kaplan-Meier survival analysis and Cox proportional hazards regression were used to compare time-to-death by varying CAC. Results The Veteran patients (n=195) were predominantly older (median age of 67) male (98%) smokers (96%). The majority (68%) were pathologic stage IA. Overall, 12% of patients had no CAC, 27% mild, 26% moderate, and 36% severe CAC. Median unadjusted survival was 8.8 years for patients with absent or mild CAC versus 6.3 years for moderate and 5.9 years for severe CAC (P=0.01). The adjusted hazard ratio for moderate CAC was 1.44 (95% CI, 0.85–2.46) and for severe CAC was 1.73 (95% CI, 1.03–2.88; P for trend <0.05). Conclusions The presence of severe CAC on preoperative imaging significantly impacted the all-cause survival of patients undergoing resection for stage I NSCLC. This impact on mortality should be taken into consideration by multidisciplinary teams when making treatment plans for patients with early-stage disease.
Collapse
Affiliation(s)
- Maren E Shipe
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amelia W Maiga
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Surgery, Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Stephen A Deppen
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Surgery, Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Gretchen C Edwards
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Surgery, Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Hannah N Marmor
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rhonda Pinkerman
- Department of Surgery, Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Gary T Smith
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Radiology, Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Elizabeth Lio
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Johnny L Wright
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Chirayu Shah
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Radiology, Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Jonathan C Nesbitt
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Surgery, Tennessee Valley Healthcare System, Nashville, TN, USA
| | - Eric L Grogan
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Surgery, Tennessee Valley Healthcare System, Nashville, TN, USA
| |
Collapse
|
19
|
Tailor TD, Chiles C, Yeboah J, Rivera MP, Tong BC, Schwartz FR, Benefield T, Lane LM, Stashko I, Thomas SM, Henderson LM. Cardiovascular Risk in the Lung Cancer Screening Population: A Multicenter Study Evaluating the Association Between Coronary Artery Calcification and Preventive Statin Prescription. J Am Coll Radiol 2021; 18:1258-1266. [PMID: 33640340 DOI: 10.1016/j.jacr.2021.01.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/20/2021] [Accepted: 01/22/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Coronary artery calcification (CAC) is a marker of atherosclerotic cardiovascular disease (ASCVD), the leading cause of death in individuals receiving lung cancer screening (LCS) with low-dose CT. Our purpose was to determine the proportion of the LCS population eligible for primary ASCVD preventive statin therapy by American College of Cardiology/American Heart Association guidelines, assess statin prescription rates among statin-eligible individuals, and determine associations of CAC on downstream statin prescribing within 90 days of LCS. METHODS Individuals receiving LCS between January 1, 2016, and December 31, 2018, across three centers were retrospectively enrolled. Statin eligibility in individuals without pre-existing ASCVD was determined by 2013 American College of Cardiology/American Heart Association guidelines: (1) low-density lipoprotein ≥190 mg/dL, (2) diabetes, or (3) ASCVD risk score ≥7.5%. CAC presence and severity (mild, moderate, heavy) were extracted from LCS reports. Variation in statin prescription rates and associations between CAC and statin prescription were determined using mixed-effects logistic regression. RESULTS Of 5,495 individuals receiving LCS, 31.4% (1,724 of 5,495) had pre-existing ASCVD. Of the remaining 3,771 individuals, 73.6% were statin eligible (2,777 of 3,771). However, most lacked statin prescription (60.5%, 1,681 of 2,777). CAC was associated with downstream statin prescribing (adjusted odds ratio = 2.60, 95% confidence interval: 1.12-6.02), with a higher likelihood of statin prescribing with increasing CAC severity (adjusted odds ratio = 2.21, 95% confidence interval: 1.35-3.60). CONCLUSION Although most of the LCS population is eligible for guideline-directed statin therapy, statins are underprescribed in this group. Radiologist reporting of CAC at LCS reflects a potential opportunity to raise awareness of ASCVD risk and improve preventive statin prescribing.
Collapse
Affiliation(s)
- Tina D Tailor
- Fellowship Director of Cardiothoracic Radiology, Research Director, Duke Lung Cancer Screening Program, Department of Radiology, Duke University Medical Center, Durham, North Carolina.
| | - Caroline Chiles
- Department of Radiology, Wake Forest Baptist Medical Center, Wake Forest, North Carolina
| | - Joseph Yeboah
- Department of Cardiology, Wake Forest Baptist Medical Center, Wake Forest, North Carolina
| | - M Patricia Rivera
- Medical Director, Pulmonary Function Test and Bronchoscopy Services; Service Chief, Pulmonary Inpatient Service, Department of Medicine, Division of Pulmonary Disease and Critical Care Medicine; Director, Lung Cancer Screening Program, University of North Carolina School of Medicine., Chapel Hill, North Carolina
| | - Betty C Tong
- Department of Surgery, Duke University Medical Center, Durham, North Carolina; Clinical Director, Lung Cancer Screening Program, Duke University Health System, Duke Cancer Institute, Duke University, Durham, North Carolina
| | - Fides R Schwartz
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
| | - Thad Benefield
- Department of Radiology, University of North Carolina School of Medicine., Chapel Hill, North Carolina
| | - Lindsay M Lane
- Department of Radiology, University of North Carolina School of Medicine., Chapel Hill, North Carolina
| | - Ilona Stashko
- Duke Cancer Institute, Duke University, Durham, North Carolina
| | - Samantha M Thomas
- Manager, Duke Cancer Institute (DCI) Biostatistics Shared Resource and Internship Director, Biostatistics Core Training and Internship Program, Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina
| | - Louise M Henderson
- Director Epidemiology Research Team, Director Carolina Mammography Registry; Co-Lead, Cancer Epidemiology Program at Lineberger Comprehensive Cancer Center; Department of Radiology, University of North Carolina School of Medicine., Chapel Hill, North Carolina
| |
Collapse
|
20
|
Gupta YS, Finkelstein M, Manna S, Toussie D, Bernheim A, Little BP, Concepcion J, Maron SZ, Jacobi A, Chung M, Kukar N, Voutsinas N, Cedillo MA, Fernandes A, Eber C, Fayad ZA, Hota P. Coronary artery calcification in COVID-19 patients: an imaging biomarker for adverse clinical outcomes. Clin Imaging 2021; 77:1-8. [PMID: 33601125 PMCID: PMC7875715 DOI: 10.1016/j.clinimag.2021.02.016] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 02/03/2021] [Accepted: 02/08/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Recent studies have demonstrated a complex interplay between comorbid cardiovascular disease, COVID-19 pathophysiology, and poor clinical outcomes. Coronary artery calcification (CAC) may therefore aid in risk stratification of COVID-19 patients. METHODS Non-contrast chest CT studies on 180 COVID-19 patients ≥ age 21 admitted from March 1, 2020 to April 27, 2020 were retrospectively reviewed by two radiologists to determine CAC scores. Following feature selection, multivariable logistic regression was utilized to evaluate the relationship between CAC scores and patient outcomes. RESULTS The presence of any identified CAC was associated with intubation (AOR: 3.6, CI: 1.4-9.6) and mortality (AOR: 3.2, CI: 1.4-7.9). Severe CAC was independently associated with intubation (AOR: 4.0, CI: 1.3-13) and mortality (AOR: 5.1, CI: 1.9-15). A greater CAC score (UOR: 1.2, CI: 1.02-1.3) and number of vessels with calcium (UOR: 1.3, CI: 1.02-1.6) was associated with mortality. Visualized coronary stent or coronary artery bypass graft surgery (CABG) had no statistically significant association with intubation (AOR: 1.9, CI: 0.4-7.7) or death (AOR: 3.4, CI: 1.0-12). CONCLUSION COVID-19 patients with any CAC were more likely to require intubation and die than those without CAC. Increasing CAC and number of affected arteries was associated with mortality. Severe CAC was associated with higher intubation risk. Prior CABG or stenting had no association with elevated intubation or death.
Collapse
Affiliation(s)
- Yogesh Sean Gupta
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA.
| | - Mark Finkelstein
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Sayan Manna
- Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Danielle Toussie
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Adam Bernheim
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Brent P Little
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02144, USA
| | - Jose Concepcion
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Samuel Z Maron
- Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Adam Jacobi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Michael Chung
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Nina Kukar
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA; Department of Cardiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Nicholas Voutsinas
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Mario A Cedillo
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Ajit Fernandes
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Corey Eber
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA
| | - Zahi A Fayad
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY 10029, USA; BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Partha Hota
- Division of Cardiothoracic Imaging, Atlantic Medical Imaging, Galloway, NJ 08205, USA
| |
Collapse
|
21
|
Management and Outcomes of Suspected Infectious and Inflammatory Lung Abnormalities Identified on Lung Cancer Screening CT. AJR Am J Roentgenol 2020; 217:1083-1092. [PMID: 33377416 DOI: 10.2214/ajr.20.25124] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Background: Incidental findings are frequently encountered during lung cancer screening (LCS). Limited data describe the prevalence of suspected acute infectious and inflammatory lung processes on LCS and how they should be managed. Objective: To determine the prevalence, radiologic reporting and management, and outcome of suspected infectious and inflammatory lung processes identified incidentally during LCS, and to propose a management algorithm. Methods: This retrospective study included 6314 low dose CT (LDCT) examinations performed between June 2014 and April 2019 in 3800 patients as part of an established LCS program. Radiology reports were reviewed, and patients with potentially infectious or inflammatory lung abnormalities were identified and analyzed for descriptors of imaging findings, Lung-RADS designation, recommendations, and clinical outcomes. Based on the descriptors, outcomes and a >2% threshold risk of malignancy, a follow-up algorithm was developed to decrease additional imaging without affecting cancer detection. Results: A total of 331/3800 (8.7%) patients (178 men, 153 women; mean age: 66 ± 7 years) undergoing LCS had lung findings that were attributed to infection or inflammation. These abnormalities were reported as potentially significant findings using the "S" modifier in 149/331 (45.0%) and as the "dominant nodule" determining the Lung-RADS category in 96/331 (29.0%). Abnormalities were multiple or multifocal in 260/331 (78.5%). Common descriptors were ground-glass (155/331; 46.8%), tree-in-bud (56/331; 16.9%), consolidation (41/331; 12.4%), and clustered (67/331; 20.2%) opacities. A follow-up chest CT outside of screening was performed within 12 months or less in 264/331 (79.8%) and within 6 months or less in 286/331 (56.2%). A total of 260/331 (78.5%) opacities resolved on follow-up imaging. Two malignancies (2/331; 0.60%) were associated with these abnormalities, and both had consolidations. Theoretical adoption of a proposed management algorithm for suspected infectious and inflammatory findings reduced unnecessary follow-up imaging by 82.6% without missing a single malignancy. Conclusions: Presumed acute infectious or inflammatory lung abnormalities are frequently encountered in the setting of LCS. These opacities are commonly multifocal and resolve on follow-up. Less than 1% are associated with malignancy. Clinical impact: Adoption of a conservative management algorithm can standardize recommendations and reduce unnecessary imaging without increasing the risk of missing a malignancy.
Collapse
|
22
|
Chintanapakdee W, Mendoza DP, Zhang EW, Botwin A, Gilman MD, Gainor JF, Shepard JAO, Digumarthy SR. Detection of Extrapulmonary Malignancy During Lung Cancer Screening: 5-Year Analysis at a Tertiary Hospital. J Am Coll Radiol 2020; 17:1609-1620. [DOI: 10.1016/j.jacr.2020.09.032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/02/2020] [Accepted: 09/09/2020] [Indexed: 12/18/2022]
|