1
|
Patel AS, Miller E, Regis SM, Hunninghake GM, Price LL, Gawlik M, McKee AB, Rieger-Christ KM, Pinto-Plata V, Liesching TN, Wald C, Hashim J, McKee BJ, Gazourian L. Interstitial lung abnormalities in a large clinical lung cancer screening cohort: association with mortality and ILD diagnosis. Respir Res 2023; 24:49. [PMID: 36782326 PMCID: PMC9926562 DOI: 10.1186/s12931-023-02359-9] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 02/06/2023] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND Interstitial lung abnormalities (ILA) are CT findings suggestive of interstitial lung disease in individuals without a prior diagnosis or suspicion of ILD. Previous studies have demonstrated that ILA are associated with clinically significant outcomes including mortality. The aim of this study was to determine the prevalence of ILA in a large CT lung cancer screening program and the association with clinically significant outcomes including mortality, hospitalizations, cancer and ILD diagnosis. METHODS This was a retrospective study of individuals enrolled in a CT lung cancer screening program from 2012 to 2014. Baseline and longitudinal CT scans were scored for ILA per Fleischner Society guidelines. The primary analyses examined the association between baseline ILA and mortality, all-cause hospitalization, and incidence of lung cancer. Kaplan-Meier plots were generated to visualize the associations between ILA and lung cancer and all-cause mortality. Cox regression proportional hazards models were used to test for this association in both univariate and multivariable models. RESULTS 1699 subjects met inclusion criteria. 41 (2.4%) had ILA and 101 (5.9%) had indeterminate ILA on baseline CTs. ILD was diagnosed in 10 (24.4%) of 41 with ILA on baseline CT with a mean time from baseline CT to diagnosis of 4.47 ± 2.72 years. On multivariable modeling, the presence of ILA remained a significant predictor of death, HR 3.87 (2.07, 7.21; p < 0.001) when adjusted for age, sex, BMI, pack years and active smoking, but not of lung cancer and all-cause hospital admission. Approximately 50% with baseline ILA had progression on the longitudinal scan. CONCLUSIONS ILA identified on baseline lung cancer screening exams are associated with all-cause mortality. In addition, a significant proportion of patients with ILA are subsequently diagnosed with ILD and have CT progression on longitudinal scans. TRIAL REGISTRATION NUMBER ClinicalTrials.gov; No.: NCT04503044.
Collapse
Affiliation(s)
- Avignat S. Patel
- grid.415731.50000 0001 0725 1353Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital and Medical Center, Burlington, MA 01805 USA ,grid.67033.310000 0000 8934 4045Tufts University School of Medicine, Boston, MA 02111 USA
| | - Ezra Miller
- grid.415731.50000 0001 0725 1353Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital and Medical Center, Burlington, MA 01805 USA ,grid.67033.310000 0000 8934 4045Tufts University School of Medicine, Boston, MA 02111 USA
| | - Shawn M. Regis
- grid.415731.50000 0001 0725 1353Division of Radiation Oncology, Department of Medicine, Lahey Hospital and Medical Center, Burlington, MA 01805 USA
| | - Gary M. Hunninghake
- grid.62560.370000 0004 0378 8294Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115 USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA 02115 USA
| | - Lori Lyn Price
- grid.67033.310000 0000 8934 4045Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111 USA ,grid.429997.80000 0004 1936 7531Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA 02111 USA
| | - Melissa Gawlik
- grid.415731.50000 0001 0725 1353Quality and Safety, Lahey Hospital and Medical Center, Burlington, MA 01805 USA
| | - Andrea B. McKee
- grid.415731.50000 0001 0725 1353Division of Radiation Oncology, Department of Medicine, Lahey Hospital and Medical Center, Burlington, MA 01805 USA
| | - Kimberly M. Rieger-Christ
- grid.415731.50000 0001 0725 1353Translational Research, Lahey Hospital and Medical Center, Burlington, MA 01805 USA
| | - Victor Pinto-Plata
- grid.415731.50000 0001 0725 1353Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital and Medical Center, Burlington, MA 01805 USA ,grid.67033.310000 0000 8934 4045Tufts University School of Medicine, Boston, MA 02111 USA
| | - Timothy N. Liesching
- grid.415731.50000 0001 0725 1353Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital and Medical Center, Burlington, MA 01805 USA ,grid.67033.310000 0000 8934 4045Tufts University School of Medicine, Boston, MA 02111 USA
| | - Christoph Wald
- grid.415731.50000 0001 0725 1353Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA 01805 USA
| | - Jeffrey Hashim
- grid.415731.50000 0001 0725 1353Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA 01805 USA
| | - Brady J. McKee
- grid.415731.50000 0001 0725 1353Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA 01805 USA
| | - Lee Gazourian
- grid.415731.50000 0001 0725 1353Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital and Medical Center, Burlington, MA 01805 USA ,grid.67033.310000 0000 8934 4045Tufts University School of Medicine, Boston, MA 02111 USA
| |
Collapse
|
2
|
Williams RM, Cordon M, Eyestone E, Smith L, Luta G, McKee BJ, Regis SM, Abrams DB, Niaura RS, Stanton CA, Parikh V, Taylor KL. Improved motivation and readiness to quit shortly after lung cancer screening: Evidence for a teachable moment. Cancer 2022; 128:1976-1986. [PMID: 35143041 PMCID: PMC9038674 DOI: 10.1002/cncr.34133] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/13/2022] [Accepted: 01/18/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND For patients at high risk for lung cancer, screening using low-dose computed tomography (lung cancer screening [LCS]) is recommended. The purpose of this study was to examine whether screening may serve as a teachable moment for smoking-related outcomes. METHODS In a smoking-cessation trial, participants (N = 843) completed 2 phone interviews before randomization: before LCS (T0) and after LCS (T1). By using logistic and linear regression, the authors examined teachable moment variables (perceived risk, lung cancer worry) and outcomes (readiness, motivation, and cigarettes per day [CPD]). RESULTS Participants were a mean ± SD age of 63.7 ± 5.9 years, had 47.8 ± 7.1 pack-years of smoking, 35.2% had a high school diploma or General Educational Development (high school equivalency) degree or less, and 42.3% were undergoing their first scan. Between T0 and T1, 25.7% of participants increased readiness to quit, 9.6% decreased readiness, and 64.7% reported no change (P < .001). Motivation to quit increased (P < .05) and CPD decreased between assessments (P < .001), but only 1.3% self-reported quitting. Compared with individuals who reported no lung cancer worry/little worry, extreme worry was associated with readiness to quit in the next 30 days (odds ratio, 1.8; 95% CI, 1.1-3.0) and with higher motivation (b = 0.83; P < .001) at T1. Individuals undergoing a baseline (vs annual) scan were more ready to quit in the next 30 days (odds ratio, 1.8; 95% CI, 1.3-2.5). CONCLUSIONS During the brief window between registering for LCS and receiving the results, the authors observed that very few participants quit smoking, but a significant proportion improved on readiness and motivation to quit, particularly among individuals who were undergoing their first scan and those who were extremely worried about lung cancer. These results indicate that providing evidence-based tobacco treatment can build upon this teachable moment.
Collapse
Affiliation(s)
- Randi M Williams
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, District of Columbia
| | - Marisa Cordon
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, District of Columbia
| | - Ellie Eyestone
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, District of Columbia
| | - Laney Smith
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, District of Columbia
| | - George Luta
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, District of Columbia
| | - Brady J McKee
- Division of Radiology, Lahey Hospital and Medical Center, Burlington, Massachusetts
| | - Shawn M Regis
- Division of Radiology, Lahey Hospital and Medical Center, Burlington, Massachusetts
| | - David B Abrams
- New York University School of Global Public Health, New York, New York
| | - Raymond S Niaura
- New York University School of Global Public Health, New York, New York
| | | | - Vicky Parikh
- Department of Population Health, MedStar Shah Medical Group, Hollywood, Maryland
| | - Kathryn L Taylor
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, District of Columbia
| | | |
Collapse
|
3
|
Burks EJ, Zhang J, Sullivan TB, Shi X, Sands JM, Regis SM, McKee BJ, McKee AB, Zhang S, Liu H, Liu G, Spira A, Beane J, Lenburg ME, Rieger-Christ KM. Pathologic and gene expression comparison of CT- screen detected and routinely detected stage I/0 lung adenocarcinoma in NCCN risk-matched cohorts. Cancer Treat Res Commun 2021; 29:100486. [PMID: 34794107 DOI: 10.1016/j.ctarc.2021.100486] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 09/16/2021] [Revised: 11/01/2021] [Accepted: 11/06/2021] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Although three randomized control trials have proven mortality benefit of CT lung cancer screening (CTLS), <5% of eligible US smokers are screened. Some attribute this to fear of harm conveyed at shared decision visits, including the harm of overdiagnosis/overtreatment of indolent BAC-like adenocarcinoma. METHODS Since the frequency of indolent cancers has not been compared between CTLS and routinely detected cohorts, we compare pathology and RNA expression of 86 NCCN high-risk CTLS subjects to 83 high-risk (HR-R) and 51 low-risk (LR-R) routinely detected patients. Indolent adenocarcinoma was defined as previously described for low malignant potential (LMP) adenocarcinoma along with AIS/MIA. Exome RNA sequencing was performed on a subset of high-risk (CTLS and HR-R) FFPE tumor samples. RESULTS Indolent adenocarcinoma (AIS, MIA, and LMP) showed 100% disease-specific survival (DSS) with similar frequency in CTLS (18%) and HR-R (20%) which were comparatively lower than LR-R (33%). Despite this observation, CTLS exhibited intermediate DSS between HR-R and LR-R (5-year DSS: 88% CTLS, 82% HR-R, & 95% LR-R, p = 0.047), possibly reflecting a 0.4 cm smaller median tumor size and lower frequency of tumor necrosis compared to HR-R. WGCNA gene modules derived from TCGA lung adenocarcinoma correlated with aggressive histologic patterns, mitotic activity, and tumor invasive features, but no significant differential expression between CTLS and HR-R was observed. CONCLUSION CTLS subjects are at no greater risk of overdiagnosis from indolent adenocarcinoma (AIS, MIA, and LMP) than risk-matched patients whose cancers are discovered in routine clinical practice. Improved outcomes likely reflect detection and treatment at smaller size.
Collapse
Affiliation(s)
- Eric J Burks
- Department of Pathology, Lahey Hospital & Medical Center, Burlington, MA, United States of America
| | - Jiarui Zhang
- Department of Medicine Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States of America
| | - Travis B Sullivan
- Department of Translational Research, Ian C. Summerhayes Cell and Molecular Biology Laboratory, Lahey Hospital & Medical Center, Burlington, MA, United States of America
| | - Xingyi Shi
- Department of Medicine Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States of America
| | - Jacob M Sands
- Department of Hematology and Oncology, Lahey Hospital & Medical Center, Burlington, MA, United States of America
| | - Shawn M Regis
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, MA, United States of America
| | - Brady J McKee
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, United States of America
| | - Andrea B McKee
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, MA, United States of America
| | - Sherry Zhang
- Department of Medicine Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States of America
| | - Hanqiao Liu
- Department of Medicine Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States of America
| | - Gang Liu
- Department of Medicine Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States of America
| | - Avrum Spira
- Department of Medicine Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States of America; Johnson and Johnson Innovation, Cambridge, MA, United States of America
| | - Jennifer Beane
- Department of Medicine Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States of America
| | - Marc E Lenburg
- Department of Medicine Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States of America; Department of Pathology & Laboratory Medicine, Boston University School of Medicine, Boston Medical Center, Boston, MA, United States of America
| | - Kimberly M Rieger-Christ
- Department of Translational Research, Ian C. Summerhayes Cell and Molecular Biology Laboratory, Lahey Hospital & Medical Center, Burlington, MA, United States of America.
| |
Collapse
|
4
|
Deros DE, Hagerman CJ, Kramer JA, Anderson ED, Regis S, McKee AB, McKee BJ, Stanton CA, Niaura R, Abrams DB, Ramsaier M, Fallon S, Harper H, Taylor KL. Change in amount smoked and readiness to quit among patients undergoing lung cancer screening. J Thorac Dis 2021; 13:4947-4955. [PMID: 34527333 PMCID: PMC8411192 DOI: 10.21037/jtd-20-3267] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 12/31/2020] [Accepted: 05/13/2021] [Indexed: 11/06/2022]
Abstract
Background There is mixed evidence regarding whether undergoing computed tomography lung cancer screening (LCS) can serve as a "teachable moment" that impacts smoking behavior and attitudes. The study aim was to assess whether the standard procedures of undergoing LCS and receiving free and low-cost evidence-based cessation resources impacted short-term smoking-related outcomes. Methods Participants were smokers (N=87) who were registered to undergo lung screening and were enrolled in a cessation intervention trial. We conducted two phone interviews, both preceding trial randomization: the first interview was conducted prior to lung screening, and the second interview followed lung screening (median =12.5 days post-screening) and participants' receipt of their screening results. The interviews assessed demographic characteristics, interest in evidence-based cessation intervention methods, and tobacco-related characteristics, including cigarettes per day and readiness to quit. Participants received minimal evidence-based cessation resources following the pre-lung screening interview. Results Participants were 60.3 years old, 56.3% female, and reported a median of 40 pack-years. Participants were interested in using several evidence-based strategies, including counseling from a healthcare provider (76.7%) and receiving nicotine replacement therapy (69.8%). Pre-lung screening, 25.3% smoked ≤10 cigarettes per day, and 29.9% were ready to quit in the next 30 days. We conducted two McNemar binomial distribution tests to assess change from pre- to post-screening. At the post-lung screening assessment, approximately three-quarters reported no change on these variables. However, 23.3% reported smoking fewer cigarettes per day, whereas 4.7% reported smoking more cigarettes per day (McNemar P=0.002), and 17.2% reported increased readiness to quit, whereas 6.9% reported decreased readiness to quit (McNemar P=0.078). Conclusions Following receipt of cessation resources and completion of lung screening, most participants reported no change in smoking outcomes. However, there was a significant reduction in cigarettes per day, and there was a trend for increased readiness to quit. This setting may provide a potential "teachable moment" and an opportunity to assist smokers with quitting. However, more proactive and intensive interventions will be necessary to capitalize on these changes and to support abstinence in the long-term.
Collapse
Affiliation(s)
- Danielle E Deros
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Charlotte J Hagerman
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Jenna A Kramer
- Levine Cancer Institute, Carolinas Healthcare System, Charlotte, NC, USA
| | - Eric D Anderson
- Medstar Georgetown University Medical Center, Washington, DC, USA
| | - Shawn Regis
- Sophia Gordon Cancer Center, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Andrea B McKee
- Sophia Gordon Cancer Center, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Brady J McKee
- Sophia Gordon Cancer Center, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Cassandra A Stanton
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.,Westat, Inc. Rockville, MD, USA
| | - Ray Niaura
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.,Social and Behavioral Sciences, College of Global Public Health, New York University, New York, NY, USA
| | - David B Abrams
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.,Social and Behavioral Sciences, College of Global Public Health, New York University, New York, NY, USA
| | - Michael Ramsaier
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, USA
| | - Shelby Fallon
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Harry Harper
- John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, USA
| | - Kathryn L Taylor
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| |
Collapse
|
5
|
Gazourian L, Regis SM, Pagura EJ, Price LL, Gawlik M, Lamb C, Rieger-Christ KM, Thedinger WB, Sanayei AM, Long WP, Stefanescu CF, Rizzo GS, Patel AS, Come CE, Thomson CC, Pinto-Plata V, Steiling K, McKee AB, Wald C, McKee BJ, Liesching TN. Qualitative coronary artery calcification scores and risk of all cause, COPD and pneumonia hospital admission in a large CT lung cancer screening cohort. Respir Med 2021; 186:106540. [PMID: 34311389 DOI: 10.1016/j.rmed.2021.106540] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 06/24/2021] [Accepted: 07/07/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Patients at high-risk for lung cancer and qualified for CT lung cancer screening (CTLS) are at risk for numerous cardio-pulmonary comorbidities. We sought to examine if qualitatively assessed coronary artery calcifications (CAC) on CTLS exams could identify patients at increased risk for non-cardiovascular events such as all cause, COPD and pneumonia related hospitalization and to verify previously reported associations between CAC and mortality and cardiovascular events. STUDY DESIGN AND METHODS Patients (n = 4673) from Lahey Hospital and Medical Center who underwent CTLS from January 12, 2012 through September 30, 2017 were included with clinical follow-up through September 30, 2019. CTLS exams were qualitatively scored for the presence and severity of CAC at the time of exam interpretation using a four point scale: none, mild, moderate, and marked. Multivariable Cox regression models were used to evaluate the association between CT qualitative CAC and all-cause, COPD-related, and pneumonia-related hospital admissions. RESULTS 3631 (78%) of individuals undergoing CTLS had some degree of CAC on their baseline exam: 1308 (28.0%), 1128 (24.1%), and 1195 (25.6%) had mild, moderate and marked coronary calcification, respectively. Marked CAC was associated with all-cause hospital admission and pneumonia related admissions HR 1.48; 95% CI 1.23-1.78 and HR 2.19; 95% 1.30-3.71, respectively. Mild, moderate and marked CAC were associated with COPD-related admission HR 2.30; 95% CI 1.31-4.03, HR 2.17; 95% CI 1.20-3.91 and HR 2.27; 95% CI 1.24-4.15. CONCLUSION Qualitative CAC on CTLS exams identifies individuals at elevated risk for all cause, pneumonia and COPD-related hospital admissions.
Collapse
Affiliation(s)
- Lee Gazourian
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA.
| | - Shawn M Regis
- Department of Medicine, Division of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Elizabeth J Pagura
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA; Tufts University School of Medicine, Boston, MA, 02111, USA
| | - Lori Lyn Price
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, 02111, USA; Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, 02111, USA
| | - Melissa Gawlik
- Quality and Safety, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Carla Lamb
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | | | | | - Ava M Sanayei
- Tufts University School of Medicine, Boston, MA, 02111, USA
| | - William P Long
- Tufts University School of Medicine, Boston, MA, 02111, USA
| | | | - Giulia S Rizzo
- Department of General Surgery, UMass Memorial Medical Center, Worcester, MA, 01655, USA
| | - Avignat S Patel
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Carolyn E Come
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Carey C Thomson
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mount Auburn Hospital, Cambridge, MA, 02138, USA; Harvard Medical School, Boston, MA, 02115, USA
| | - Victor Pinto-Plata
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Baystate Medical Center, Springfield, MA, 01199, USA
| | - Katrina Steiling
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Andrea B McKee
- Department of Medicine, Division of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Christoph Wald
- Department of Hospital Based Specialties, Division of Radiology, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Brady J McKee
- Department of Hospital Based Specialties, Division of Radiology, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Timothy N Liesching
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| |
Collapse
|
6
|
Trajanovski S, Mavroeidis D, Swisher CL, Gebre BG, Veeling BS, Wiemker R, Klinder T, Tahmasebi A, Regis SM, Wald C, McKee BJ, Flacke S, MacMahon H, Pien H. Towards radiologist-level cancer risk assessment in CT lung screening using deep learning. Comput Med Imaging Graph 2021; 90:101883. [PMID: 33895622 DOI: 10.1016/j.compmedimag.2021.101883] [Citation(s) in RCA: 3] [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: 05/01/2020] [Revised: 02/08/2021] [Accepted: 02/13/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Recently, the usefulness of Deep Learning (DL) models for lung cancer risk assessment has been demonstrated. However, in many cases model performances are evaluated on small/medium size test sets, thus not providing strong model generalization and stability guarantees which are necessary for clinical adoption. In this work, our goal is to contribute towards clinical adoption by investigating a deep learning framework on larger and heterogeneous datasets while also comparing to state-of-the-art models. METHODS Three low-dose CT lung cancer screening datasets were used: National Lung Screening Trial (NLST, n = 3410), Lahey Hospital and Medical Center (LHMC, n = 3154) data, Kaggle competition data (from both stages, n = 1397 + 505) and the University of Chicago data (UCM, a subset of NLST, annotated by radiologists, n = 132). At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image context around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk for the entire CT scan. We trained our algorithm on a part of the NLST dataset, and validated it on the other datasets. Special care was taken to ensure there was no patient overlap between the train and validation sets. RESULTS AND CONCLUSIONS The proposed deep learning model is shown to: (a) generalize well across all three data sets, achieving AUC between 86% to 94%, with our external test-set (LHMC) being at least twice as large compared to other works; (b) have better performance than the widely accepted PanCan Risk Model, achieving 6 and 9% better AUC score in our two test sets; (c) have improved performance compared to the state-of-the-art represented by the winners of the Kaggle Data Science Bowl 2017 competition on lung cancer screening; (d) have comparable performance to radiologists in estimating cancer risk at a patient level.
Collapse
Affiliation(s)
| | | | | | | | - Bastiaan S Veeling
- Machine Learning lab, University of Amsterdam, 1090 GH Amsterdam and, Philips Research, Eindhoven, 5656 AE, The Netherlands
| | | | | | - Amir Tahmasebi
- Philips Research North America, Cambridge, MA, 02141, USA
| | - Shawn M Regis
- Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Christoph Wald
- Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Brady J McKee
- Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | | | - Heber MacMahon
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | - Homer Pien
- Philips Research North America, Cambridge, MA, 02141, USA
| |
Collapse
|
7
|
Gazourian L, Thedinger WB, Regis SM, Pagura EJ, Price LL, Gawlik M, Stefanescu CF, Lamb C, Rieger-Christ KM, Singh H, Casasola M, Walker AR, Rupal A, Patel AS, Come CE, Sanayei AM, Long WP, Rizzo GS, McKee AB, Washko GR, San Jose Estepar R, Wald C, McKee BJ, Thomson CC, Liesching TN. Qualitative emphysema and risk of COPD hospitalization in a multicenter CT lung cancer screening cohort study. Respir Med 2020; 176:106245. [PMID: 33253972 DOI: 10.1016/j.rmed.2020.106245] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 11/15/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND In the United States, 9 to 10 million Americans are estimated to be eligible for computed tomographic lung cancer screening (CTLS). Those meeting criteria for CTLS are at high-risk for numerous cardio-pulmonary co-morbidities. The objective of this study was to determine the association between qualitative emphysema identified on screening CTs and risk for hospital admission. STUDY DESIGN AND METHODS We conducted a retrospective multicenter study from two CTLS cohorts: Lahey Hospital and Medical Center (LHMC) CTLS program, Burlington, MA and Mount Auburn Hospital (MAH) CTLS program, Cambridge, MA. CTLS exams were qualitatively scored by radiologists at time of screening for presence of emphysema. Multivariable Cox regression models were used to evaluate the association between CT qualitative emphysema and all-cause, COPD-related, and pneumonia-related hospital admission. RESULTS We included 4673 participants from the LHMC cohort and 915 from the MAH cohort. 57% and 51.9% of the LHMC and MAH cohorts had presence of CT emphysema, respectively. In the LHMC cohort, the presence of emphysema was associated with all-cause hospital admission (HR 1.15, CI 1.07-1.23; p < 0.001) and COPD-related admission (HR 1.64; 95% CI 1.14-2.36; p = 0.007), but not with pneumonia-related admission (HR 1.52; 95% CI 1.27-1.83; p < 0.001). In the MAH cohort, the presence of emphysema was only associated with COPD-related admission (HR 2.05; 95% CI 1.07-3.95; p = 0.031). CONCLUSION Qualitative CT assessment of emphysema is associated with COPD-related hospital admission in a CTLS population. Identification of emphysema on CLTS exams may provide an opportunity for prevention and early intervention to reduce admission risk.
Collapse
Affiliation(s)
- Lee Gazourian
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA.
| | | | - Shawn M Regis
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Elizabeth J Pagura
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Lori Lyn Price
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, 02111, USA; Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, 02111, USA
| | - Melissa Gawlik
- Quality and Safety, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | | | - Carla Lamb
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | | | - Harpreet Singh
- Department of Medicine, Mount Auburn Hospital, Cambridge, MA, 02138, USA
| | - Marcel Casasola
- Department of Medicine, Mount Auburn Hospital, Cambridge, MA, 02138, USA
| | - Alexander R Walker
- Department of Medicine, Mount Auburn Hospital, Cambridge, MA, 02138, USA
| | - Arashdeep Rupal
- Department of Medicine, Mount Auburn Hospital, Cambridge, MA, 02138, USA
| | - Avignat S Patel
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Carolyn E Come
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Ava M Sanayei
- Tufts University School of Medicine, Boston, MA, 02111, USA
| | - William P Long
- Tufts University School of Medicine, Boston, MA, 02111, USA
| | - Giulia S Rizzo
- Department of General Surgery, UMass Memorial Medical Center, Worcester, MA, 01655, USA
| | - Andrea B McKee
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - George R Washko
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA; Applied Chest Imaging Laboratories, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Raul San Jose Estepar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA; Department of Radiology, Brigham and Women's Hospital Boston, MA, 02115, USA
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Brady J McKee
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Carey C Thomson
- Department of Medicine, Mount Auburn Hospital, Cambridge, MA, 02138, USA; Harvard Medical School, Boston, MA, 02115, USA; Division of Pulmonary and Critical Care Medicine, Mount Auburn Hospital, Cambridge, MA, 02138, USA
| | - Timothy N Liesching
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| |
Collapse
|
8
|
Gazourian L, Durgana CS, Huntley D, Rizzo GS, Thedinger WB, Regis SM, Price LL, Pagura EJ, Lamb C, Rieger-Christ K, Thomson CC, Stefanescu CF, Sanayei A, Long WP, McKee AB, Washko GR, Estépar RSJ, Wald C, Liesching TN, McKee BJ. Quantitative Pectoralis Muscle Area is Associated with the Development of Lung Cancer in a Large Lung Cancer Screening Cohort. Lung 2020; 198:847-853. [PMID: 32889594 DOI: 10.1007/s00408-020-00388-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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: 05/23/2020] [Accepted: 08/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND Studies have demonstrated an inverse relationship between body mass index (BMI) and the risk of developing lung cancer. We conducted a retrospective cohort study evaluating baseline quantitative computed tomography (CT) measurements of body composition, specifically muscle and fat area in a large CT lung screening cohort (CTLS). We hypothesized that quantitative measurements of baseline body composition may aid in risk stratification for lung cancer. METHODS Patients who underwent baseline CTLS between January 1st, 2012 and September 30th, 2014 and who had an in-network primary care physician were included. All patients met NCCN Guidelines eligibility criteria for CTLS. Quantitative measurements of pectoralis muscle area (PMA) and subcutaneous fat area (SFA) were performed on a single axial slice of the CT above the aortic arch with the Chest Imaging Platform Workstation software. Cox multivariable proportional hazards model for cancer was adjusted for variables with a univariate p < 0.2. Data were dichotomized by sex and then combined to account for baseline differences between sexes. RESULTS One thousand six hundred and ninety six patients were included in this study. A total of 79 (4.7%) patients developed lung cancer. There was an association between the 25th percentile of PMA and the development of lung cancer [HR 1.71 (1.07, 2.75), p < 0.025] after adjusting for age, BMI, qualitative emphysema, qualitative coronary artery calcification, and baseline Lung-RADS® score. CONCLUSIONS Quantitative assessment of PMA on baseline CTLS was associated with the development of lung cancer. Quantitative PMA has the potential to be incorporated as a variable in future lung cancer risk models.
Collapse
Affiliation(s)
- Lee Gazourian
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA.
| | | | | | | | | | - Shawn M Regis
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, USA
| | - Lori Lyn Price
- Tufts Clinical and Translational Science Institute, Tufts University, Boston, USA.,Institute of Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, USA
| | - Elizabeth J Pagura
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Carla Lamb
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Kimberly Rieger-Christ
- Cancer Research, Sophia Gordon Cancer Center, Lahey Hospital & Medical Center, Burlington, USA
| | - Carey C Thomson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mount Auburn Hospital, Cambridge, USA.,Harvard Medical School, Boston, USA
| | | | - Ava Sanayei
- Tufts University School of Medicine, Boston, USA
| | | | - Andrea B McKee
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, USA
| | - George R Washko
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, USA
| | - Raul San José Estépar
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, USA.,Department of Radiology, Brigham and Women's Hospital, Boston, USA
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, USA
| | - Timothy N Liesching
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Lahey Hospital & Medical Center, Burlington, MA, 01805, USA
| | - Brady J McKee
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, USA
| |
Collapse
|
9
|
McKee BJ, Regis S, Borondy-Kitts AK, Hashim JA, French RJ, Wald C, McKee AB. NCCN Guidelines as a Model of Extended Criteria for Lung Cancer Screening. J Natl Compr Canc Netw 2019; 16:444-449. [PMID: 29632062 DOI: 10.6004/jnccn.2018.7021] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 02/28/2018] [Indexed: 11/17/2022]
Abstract
Background: This review assessed the performance of patients in NCCN high-risk group 2 in a clinical CT lung screening (CTLS) program. Methods: We retrospectively reviewed screening results for all patients from our institution undergoing clinical CTLS from January 2012 through December 2016, with follow-up through June 2017. To qualify for screening, patients had to meet the NCCN Guidelines high-risk criteria for CTLS, have a physician order for screening, be asymptomatic, be lung cancer-free for 5 years, and have no known metastatic disease. We compared demographics and screening performance of NCCN high-risk groups 1 and 2 across >4 rounds of screening. Screening metrics assessed included rates of positive and suspicious examinations, significant incidental and infectious/inflammatory findings, false negatives, and cancer detection. We also compared cancer stage and histology detected in each NCCN high-risk group. Results: A total of 2,927 individuals underwent baseline screening, of which 698 (24%) were in NCCN group 2. On average, group 2 patients were younger (60.6 vs 63.1 years), smoked less (38.8 vs 50.8 pack-years), had quit longer (18.1 vs 6.3 years), and were more often former smokers (61.4% vs 44.2%). Positive and suspicious examination rates, false negatives, and rates of infectious/inflammatory findings were equivalent in groups 1 and 2 across all rounds of screening. An increased rate of cancer detection was observed in group 2 during the second annual (T2) screening round (2.7% vs 0.5%; P=.005), with no difference in the other screening rounds: baseline (T0; 2% vs 2.3%; P=.61), first annual (T1; 1.2% vs 1.7%; P=.41), and third annual and beyond (≥T3; 1.2% vs 1.1%; P=1.00). Conclusions: CTLS appears to be equally effective in both NCCN high-risk groups.
Collapse
|
10
|
Malcolm KB, Dinwoodey DL, Cundiff MC, Regis SM, Kitts AKB, Wald C, Lynch ML, Al-Husami W, McKee AB, McKee BJ. Qualitative coronary artery calcium assessment on CT lung screening exam helps predict first cardiac events. J Thorac Dis 2018; 10:2740-2751. [PMID: 29997936 DOI: 10.21037/jtd.2018.04.76] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [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: 11/06/2022]
Abstract
Results A total of 1,513 individuals underwent CTLS. Downstream data, pre-test cardiac risk factors and CAC scores were available for 88.3% (1,336/1,513). The average length of follow-up was 2.64 (SD ±0.72) years. There were a total of 43 events, occurring in 1.55% (6/386) of patients with mild CAC, 3.24% (11/339) of patients with moderate CAC, and 8.90% (26/292) of patients with marked CAC. There were no events among patients with no reported CAC (0/319). Using multivariable logistic modeling, the increased odds of an initial cardiac event was 2.56 (95% CI, 1.76-3.92, P<0.001) for mild CAC, 6.57 (95% CI, 3.10-15.4, P<0.001) for moderate CAC, and 16.8 (95% CI, 5.46-60.3, P<0.001) for marked CAC, as compared to individuals with no CAC. Time to event analysis showed distinct differences among the four CAC categories (P<0.001). Conclusions Qualitative coronary artery calcification scoring of CTLS exams may provide a novel method to help select individuals at elevated risk for an initial cardiac event.
Collapse
Affiliation(s)
- Katherine B Malcolm
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA.,Department of Internal Medicine, University of California, San Francisco, CA, USA
| | - Danya L Dinwoodey
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Michael C Cundiff
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Shawn M Regis
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Andrea K Borondy Kitts
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Christoph Wald
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Miranda L Lynch
- Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Wael Al-Husami
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Andrea B McKee
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| | - Brady J McKee
- Department of Radiation Oncology, Radiology, Cardiology, Lahey Hospital & Medical Center, Burlington, MA, USA
| |
Collapse
|
11
|
Alshora S, McKee BJ, Regis SM, Borondy Kitts AK, Bolus CC, McKee AB, French RJ, Flacke S, Wald C. Adherence to Radiology Recommendations in a Clinical CT Lung Screening Program. J Am Coll Radiol 2017; 15:282-286. [PMID: 29289507 DOI: 10.1016/j.jacr.2017.10.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [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: 09/09/2017] [Revised: 10/03/2017] [Accepted: 10/06/2017] [Indexed: 01/15/2023]
Abstract
BACKGROUND Assess patient adherence to radiologist recommendations in a clinical CT lung cancer screening program. METHODS Patients undergoing CT lung cancer screening between January 12, 2012, and June 12, 2013, were included in this institutional review board-approved retrospective review. Patients referred from outside our institution were excluded. All patients met National Comprehensive Cancer Network Guidelines Lung Cancer Screening high-risk criteria. Full-time program navigators used a CT lung screening program management system to schedule patient appointments, generate patient result notification letters detailing the radiologist follow-up recommendation, and track patient and referring physician notification of missed appointments at 30, 60, and 90 days. To be considered adherent, patients could be no more than 90 days past due for their next recommended examination as of September 12, 2014. Patients who died, were diagnosed with cancer, or otherwise became ineligible for screening were considered adherent. Adherence rates were assessed across multiple variables. RESULTS During the study interval, 1,162 high-risk patients were screened, and 261 of 1,162 (22.5%) outside referrals were excluded. Of the remaining 901 patients, 503 (55.8%) were male, 414 (45.9%) were active smokers, 377 (41.8%) were aged 65 to 73, and >95% were white. Of the 901 patients, 772 (85.7%) were adherent. Most common reasons for nonadherence were patient refusal of follow-up exam (66.7%), inability to successfully contact the patient (20.9%), and inability to obtain the follow-up order from the referring provider (7.8%); 23 of 901 (2.6%) were discharged for other reasons. CONCLUSIONS High rates of adherence to radiologist recommendations are achievable for in-network patients enrolled in a clinical CT lung screening program.
Collapse
Affiliation(s)
- Sama Alshora
- Lahey Hospital and Medical Center, Burlington, Massachusetts; King Saud University Medical City, Riyadh, Kingdom of Saudi Arabia
| | - Brady J McKee
- Lahey Hospital and Medical Center, Burlington, Massachusetts
| | - Shawn M Regis
- Lahey Hospital and Medical Center, Burlington, Massachusetts
| | | | | | - Andrea B McKee
- Lahey Hospital and Medical Center, Burlington, Massachusetts
| | - Robert J French
- Lahey Hospital and Medical Center, Burlington, Massachusetts
| | | | - Christoph Wald
- Lahey Hospital and Medical Center, Burlington, Massachusetts
| |
Collapse
|
12
|
Beyer SE, McKee BJ, Regis SM, McKee AB, Flacke S, El Saadawi G, Wald C. Automatic Lung-RADS™ classification with a natural language processing system. J Thorac Dis 2017; 9:3114-3122. [PMID: 29221286 DOI: 10.21037/jtd.2017.08.13] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background Our aim was to train a natural language processing (NLP) algorithm to capture imaging characteristics of lung nodules reported in a structured CT report and suggest the applicable Lung-RADS™ (LR) category. Methods Our study included structured, clinical reports of consecutive CT lung screening (CTLS) exams performed from 08/2014 to 08/2015 at an ACR accredited Lung Cancer Screening Center. All patients screened were at high-risk for lung cancer according to the NCCN Guidelines®. All exams were interpreted by one of three radiologists credentialed to read CTLS exams using LR using a standard reporting template. Training and test sets consisted of consecutive exams. Lung screening exams were divided into two groups: three training sets (500, 120, and 383 reports each) and one final evaluation set (498 reports). NLP algorithm results were compared with the gold standard of LR category assigned by the radiologist. Results The sensitivity/specificity of the NLP algorithm to correctly assign LR categories for suspicious nodules (LR 4) and positive nodules (LR 3/4) were 74.1%/98.6% and 75.0%/98.8% respectively. The majority of mismatches occurred in cases where pulmonary findings were present not currently addressed by LR. Misclassifications also resulted from the failure to identify exams as follow-up and the failure to completely characterize part-solid nodules. In a sub-group analysis among structured reports with standardized language, the sensitivity and specificity to detect LR 4 nodules were 87.0% and 99.5%, respectively. Conclusions An NLP system can accurately suggest the appropriate LR category from CTLS exam findings when standardized reporting is used.
Collapse
Affiliation(s)
- Sebastian E Beyer
- Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Brady J McKee
- Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Shawn M Regis
- Department of Radiation Oncology, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Andrea B McKee
- Department of Radiation Oncology, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Sebastian Flacke
- Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA, USA
| | | | - Christoph Wald
- Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA, USA
| |
Collapse
|
13
|
Abstract
BACKGROUND Lung cancer screening may provide a "teachable moment" for promoting smoking cessation. This study assessed smoking cessation and relapse rates among individuals undergoing follow-up low-dose chest computed tomography (CT) in a clinical CT lung screening program and assessed the influence of initial screening results on smoking behavior. METHODS Self-reported smoking status for individuals enrolled in a clinical CT lung screening program undergoing a follow-up CT lung screening exam between 1st February, 2014 and 31st March, 2015 was retrospectively reviewed and compared to self-reported smoking status using a standardized questionnaire at program entry. Point prevalence smoking cessation and relapse rates were calculated across the entire population and compared with exam results. All individuals undergoing screening fulfilled the National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology: Lung Cancer Screening v1.2012(®) high-risk criteria and had an order for CT lung screening. RESULTS A total of 1,483 individuals underwent a follow-up CT lung screening exam during the study interval. Smoking status at time of follow-up exam was available for 1,461/1,483 (98.5%). A total of 46% (678/1,461) were active smokers at program entry. The overall point prevalence smoking cessation and relapse rates were 20.8% and 9.3%, respectively. Prior positive screening exam results were not predictive of smoking cessation (OR 1.092; 95% CI, 0.715-1.693) but were predictive of reduced relapse among former smokers who had stopped smoking for 2 years or less (OR 0.330; 95% CI, 0.143-0.710). Duration of program enrollment was predictive of smoking cessation (OR 0.647; 95% CI, 0.477-0.877). CONCLUSIONS Smoking cessation and relapse rates in a clinical CT lung screening program rates are more favorable than those observed in the general population. Duration of participation in the screening program correlated with increased smoking cessation rates. A positive exam result correlated with reduced relapse rates among smokers recently quit smoking.
Collapse
Affiliation(s)
- Andrea K Borondy Kitts
- Patient Advocate, Lung Cancer Screening Program Consultant, Lahey Hospital & Medical Center, Burlington, USA
| | - Andrea B McKee
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, USA
| | - Shawn M Regis
- Patient Navigator, Lahey Hospital & Medical Center, Burlington, USA
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, USA
| | - Sebastian Flacke
- Interventional Radiology, Lahey Hospital & Medical Center, Burlington, USA
| | - Brady J McKee
- Thoracic Imaging, Lahey Hospital & Medical Center, Burlington, USA
| |
Collapse
|
14
|
McKee BJ, Hashim JA, French RJ, McKee AB, Hesketh PJ, Lamb CR, Williamson C, Flacke S, Wald C. Experience With a CT Screening Program for Individuals at High Risk for Developing Lung Cancer. J Am Coll Radiol 2016; 13:R8-R13. [PMID: 26846536 DOI: 10.1016/j.jacr.2015.12.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [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: 12/17/2022]
Abstract
PURPOSE The aim of this study was to compare results of National Comprehensive Cancer Network (NCCN) high-risk group 2 with those of NCCN high-risk group 1 in a clinical CT lung screening program. METHODS The results of consecutive clinical CT lung screening examinations performed from January 2012 through December 2013 were retrospectively reviewed. All examinations were interpreted by radiologists credentialed in structured CT lung screening reporting, following the NCCN Clinical Practice Guidelines in Oncology: Lung Cancer Screening (version 1.2012). Positive results required a solid nodule ≥4 mm, a ground-glass nodule ≥5 mm, or a mediastinal or hilar lymph node >1 cm, not stable for >2 years. Significant incidental findings and findings suspicious for pulmonary infection were also recorded. RESULTS A total of 1,760 examinations were performed (464 in group 2, 1,296 in group 1); no clinical follow-up was available in 432 patients (28%). Positive results, clinically significant incidental findings, and suspected pulmonary infection were present in 25%, 6%, and 6% in group 2 and 28.2%, 6.2%, and 6.6% in group 1, respectively. Twenty-three cases of lung cancer were diagnosed (6 in group 2, 17 in group 1), for annualized rates of malignancy of 1.8% in group 2 and 1.6% in group 1. CONCLUSION NCCN group 2 results were substantively similar to those for group 1 and closely resemble those reported in the National Lung Screening Trial. Similar rates of positivity and lung cancer diagnosis in both groups suggest that thousands of additional lives may be saved each year if screening eligibility is expanded to include this particular high-risk group.
Collapse
Affiliation(s)
- Brady J McKee
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts.
| | - Jeffrey A Hashim
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Robert J French
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Andrea B McKee
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Paul J Hesketh
- Department of Hematology and Oncology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Carla R Lamb
- Department of Pulmonary and Critical Care, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Christina Williamson
- Department of Cardiovascular and Thoracic Surgery, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Sebastian Flacke
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| |
Collapse
|
15
|
Abstract
PURPOSE The aim of this study was to assess the effect of applying ACR Lung-RADS in a clinical CT lung screening program on the frequency of positive and false-negative findings. METHODS Consecutive, clinical CT lung screening examinations performed from January 2012 through May 2014 were retroactively reclassified using the new ACR Lung-RADS structured reporting system. All examinations had initially been interpreted by radiologists credentialed in structured CT lung screening reporting following the National Comprehensive Cancer Network's Clinical Practice Guidelines in Oncology: Lung Cancer Screening (version 1.2012), which incorporated positive thresholds modeled after those in the National Lung Screening Trial. The positive rate, number of false-negative findings, and positive predictive value were recalculated using the ACR Lung-RADS-specific positive solid/part-solid nodule diameter threshold of 6 mm and nonsolid (ground-glass) threshold of 2 cm. False negatives were defined as cases reclassified as benign under ACR Lung-RADS that were diagnosed with malignancies within 12 months of the baseline examination. RESULTS A total of 2,180 high-risk patients underwent baseline CT lung screening during the study interval; no clinical follow-up was available in 577 patients (26%). ACR Lung-RADS reduced the overall positive rate from 27.6% to 10.6%. No false negatives were present in the 152 patients with >12-month follow-up reclassified as benign. Applying ACR Lung-RADS increased the positive predictive value for diagnosed malignancy in 1,603 patients with follow-up from 6.9% to 17.3%. CONCLUSIONS The application of ACR Lung-RADS increased the positive predictive value in our CT lung screening cohort by a factor of 2.5, to 17.3%, without increasing the number of examinations with false-negative results.
Collapse
Affiliation(s)
- Brady J McKee
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts.
| | - Shawn M Regis
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Andrea B McKee
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Sebastian Flacke
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| |
Collapse
|
16
|
Walker BL, Williamson C, Regis SM, McKee AB, D'Agostino RS, Hesketh PJ, Lamb CR, Flacke S, Wald C, McKee BJ. Surgical Outcomes in a Large, Clinical, Low-Dose Computed Tomographic Lung Cancer Screening Program. Ann Thorac Surg 2015. [PMID: 26209493 DOI: 10.1016/j.athoracsur.2015.04.112] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Lung cancer screening with low-dose computed tomography is proven to reduce lung cancer mortality among high-risk patients. However, critics raise concern over the potential for unnecessary surgical procedures performed for benign disease as a result of screening. We reviewed our outcomes in a large clinical lung cancer screening program to assess the number of surgical procedures done for benign disease, as we believe this is an important quality metric. METHODS We retrospectively reviewed our surgical outcomes of consecutive patients who underwent low-dose computed tomography lung cancer screening from January 2012 through June 2014 using a prospectively collected database. All patients met the National Comprehensive Cancer Network lung cancer screening guidelines high-risk criteria. RESULTS There were 1,654 screened patients during the study interval with clinical follow-up at Lahey Hospital & Medical Center. Twenty-five of the 1,654 (1.5%) had surgery. Five of 25 had non-lung cancer diagnoses: 2 hamartomas, 2 necrotizing granulomas, and 1 breast cancer metastasis. The incidence of surgery for non-lung cancer diagnosis was 0.30% (5 of 1,654), and the incidence of surgery for benign disease was 0.24% (4 of 1,654). Twenty of 25 had lung cancer, 18 early stage and 2 late stage. There were no surgery-related deaths, and there was 1 major surgical complication (4%) at 30 days. CONCLUSIONS The incidence of surgical intervention for non-lung cancer diagnosis was low (0.30%) and is comparable to the rate reported in the National Lung Screening Trial (0.62%). Surgical intervention for benign disease was rare (0.24%) in our experience.
Collapse
Affiliation(s)
- Bryan L Walker
- Tufts University School of Medicine, Boston, Massachusetts
| | - Christina Williamson
- Department of Cardiovascular and Thoracic Surgery, Lahey Hospital & Medical Center, Burlington, Massachusetts.
| | - Shawn M Regis
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Andrea B McKee
- Department of Radiation Oncology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Richard S D'Agostino
- Department of Cardiovascular and Thoracic Surgery, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Paul J Hesketh
- Department of Hematology and Oncology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Carla R Lamb
- Department of Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Sebastian Flacke
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| | - Brady J McKee
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts
| |
Collapse
|
17
|
Lamb CR, McKee AB, McKee BJ, Campagna A, Hesketh PJ. Lung cancer screening. Chest 2014; 144:1737. [PMID: 24189878 DOI: 10.1378/chest.13-1539] [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: 11/01/2022] Open
Affiliation(s)
- Carla R Lamb
- Department of Pulmonary and Critical Care, Lahey Clinic, Burlington, MA.
| | - Andrea B McKee
- Department of Radiation Oncology, Lahey Clinic, Burlington, MA
| | - Brady J McKee
- Department of Radiology, Lahey Clinic, Burlington, MA
| | - Anthony Campagna
- Department of Pulmonary and Critical Care, Lahey Clinic, Burlington, MA
| | - Paul J Hesketh
- Department of Medical Oncology, Lahey Clinic, Burlington, MA
| |
Collapse
|
18
|
McKee BJ, McKee AB, Flacke S, Lamb CR, Hesketh PJ, Wald C. Initial Experience With a Free, High-Volume, Low-Dose CT Lung Cancer Screening Program. J Am Coll Radiol 2013; 10:586-92. [DOI: 10.1016/j.jacr.2013.02.015] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2013] [Accepted: 02/15/2013] [Indexed: 11/28/2022]
|
19
|
Abstract
PURPOSE Concern is frequently raised regarding the tolerance of irradiation over a joint space. The purpose of this study was to determine the outcome in terms of relapse and potential complications in patients with knee and elbow soft tissue sarcoma treated with limb-sparing surgery with or without adjuvant radiotherapy (RT). METHODS AND MATERIALS A review of our prospective database between June 1982 and December 1999 identified 86 adult patients with primary soft tissue sarcoma arising from the knee (n = 65; 76%) or elbow (n = 21; 24%) treated with limb-sparing surgery. Tumors had high-grade histologic features in 72% and were >5 cm in 48% of patients. Adjuvant RT was given to 46 (54%) of 86 patients. The type of RT was postoperative external beam RT in 63% and brachytherapy in 37%. Of the 46 patients who received RT, 85% (n = 39) had deep, 78% (n = 36) high-grade, and 54% (n = 25) >5-cm tumors. Complications were assessed in terms of wound complications requiring repeated surgery, bone fracture, nerve damage, and joint stiffness. RESULTS With a median follow-up of 48 months (range 4-175), the 5-year actuarial rate of local control, distant control, and overall survival was 75% (95% confidence interval [CI] 64-85%), 82% (95% CI 73-91%), and 81% (95% CI 71-91%), respectively. The 5-year local control rate for patients who received RT was 80% vs. 71% for those who did not (p = 0.3). The type of RT did not significantly influence the local control rate. Patients treated with external beam RT had a 5-year local control rate of 84% compared with 73% for those treated with brachytherapy (p = 0.4). On multivariate analysis, tumor size >5 cm retained its significance as an independent predictor of poor local control (p = 0.04; relative risk 3; 95% CI 1-6). In addition, high-grade histologic features emerged as an independent predictor of local recurrence (p = 0.02; relative risk 4; 95% CI 1-20). No statistically significant difference was found between the RT and no-RT group in terms of the 5-year actuarial rate of wound reoperation (10% vs. 3%, p = 0.1), bone fracture (3% vs. 5%, p = 0.5), or nerve damage (6% vs. 3%, p = 0.5). Joint stiffness was significantly higher in the RT group (24% vs. 0%, p = 0.001), but this stiffness was severe to moderate in only 2 patients. CONCLUSION On the basis of the findings of this retrospective review, adjuvant RT seems to be relatively well tolerated despite the inclusion of part of the joint space in the irradiation portal. Joint stiffness was seen more frequently with adjuvant RT, but it was moderate to severe in only a small number of patients.
Collapse
Affiliation(s)
- Kaled M Alektiar
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA.
| | | | | | | | | | | |
Collapse
|