Screening and Early Detection
Activity: 103rd Annual Meeting
University of Washington
Seattle, WA
United States - Contact Me
Dr. Douglas E. Wood is The Henry N. Harkins Professor and Chair of the Department of Surgery at the University of Washington where he was previously Professor and Endowed Chair in Lung Cancer Research as well as the Chief of the Division of Cardiothoracic Surgery. Dr. Wood received his undergraduate and medical degrees from Harvard University and trained in General Surgery and Cardiothoracic Surgery at the Massachusetts General Hospital in Boston.
Dr. Wood is the Chair of the Lung Screening Guidelines Panel for the National Comprehensive Cancer Network (NCCN), Vice-Chair of the Non-Small Cell Lung Cancer Guidelines Panel for the NCCN, and Vice-Chair of the American Cancer Society National Lung Cancer Roundtable. Notably, Dr. Wood was the chair for the NCCN Lung Cancer Screening Panel which developed and published the first clinical guidelines for lung cancer screening. Dr. Wood helped lead an effort to change lung cancer screening policy at the national level, resulting in lung cancer screening recommendations from the United States Preventive Services Taskforce and from Medicare. Most recently, Dr. Wood has partnered in an international project led by the World Economic Forum and the Lung Cancer Collaborative to advance and prioritize lung cancer care worldwide, including broader utilization of early detection. This initiative is currently being evaluated by the World Health Organization for consideration of a global health priority, or “Best Buy”.
Dr. Wood served on the Board of Directors of the Society of Thoracic Surgeons for over 20 years, completing a 5-year term as Secretary of the Society, and was the President of STS in 2013 - 2014. Dr. Wood served as a Director of the American Board of Thoracic Surgery and as Chair of the ACGME Residency Review Committee Committee for Thoracic Surgery (RRC-Thoracic). Dr. Wood recently served as President of the Thoracic Surgery Foundation and President of the Cardiothoracic Surgery Network (CTSNet).
Duke University
Durham, NC
United States - Contact Me
Betty C. Tong, MD, MHS is an Associate Professor of Surgery at the Duke University Medical Center in Durham, North Carolina, USA. Her clinical interests are in lung cancer screening, surgical management of thoracic malignancies such as lung cancer and soft tissue sarcoma of the chest, and video assisted thoracic surgery. Her research interests are centered in the area of health services research, and include the study of disparities in thoracic surgical patients, and patient preferences and decision making in the management of thoracic disease.
Saturday, May 6, 2023: 8:00 AM - 9:30 AM
Los Angeles Convention Center
Posted Room Name: 408A
Track
Thoracic
103rd Annual Meeting
Presentations
16. Evaluating the Lung Cancer Screening Eligibility of Patients Undergoing Lung Cancer Operations: An Analysis of the Southern Community Cohort Study
Total Time: 15 Minutes
Objective: The goal of lung cancer screening with low-dose computed tomography (LDCT) is to identify lung cancer at an earlier stage when it is amenable to surgical treatment. We sought to evaluate the proportion of lung cancer patients undergoing surgery who would have been eligible for lung cancer screening using data from the Southern Community Cohort Study (SCCS).
Methods: Patients who underwent surgery for lung cancer from 2002-2020 in the SCCS-a prospective cohort study of nearly 85,000 predominately low-income Black and White adults from 12 states in the southeastern United States-were identified for analysis. The proportions of patients who would have been eligible for LDCT screening under the 2013 and 2021 United States
Preventive Services Task Force (USPSTF) guidelines were compared using the McNemar test. Using data from the National Cancer Database, we calculated the proportion of breast and colon cancer patients who underwent surgery and who would have been eligible for breast and colon cancer screening, respectively; these proportions were then compared to the proportion of patients who underwent lung cancer operations in SCCS that would have been eligible for LDCT screening using a chi-square test.
Results: A total of 314 lung cancer patients underwent surgical treatment. The proportion of patients who would have been eligible for screening increased by 69.5% (from 32.5% to 55.1%, P<0.001) under the 2013 vs. 2021 USPSTF lung cancer screening guidelines. However, 45% of lung cancer patients undergoing surgery would have still been ineligible for LDCT screening under the 2021 USPSTF guidelines-in comparison, only 22% of patients undergoing colon cancer surgery and 15% of patients undergoing breast cancer surgery would have been ineligible for colorectal cancer and breast cancer screening, respectively (P<0.001). Of patients with a smoking history who underwent lung cancer operations and were ineligible for screening, 70.9% had fewer than 20 pack-years, 32.7% had quit smoking more than 15 years prior, 14.6% were too young, and 2.7% were too old (Figure).
Conclusions: Even though lung cancer screening is intended to identify lung cancers at earlier stages when they are amenable to surgical treatment, we found that 45% of patients undergoing lung cancer operations would have been ineligible for lung cancer screening, highlighting the need for further revision to the USPSTF lung cancer screening guidelines.
Methods: Patients who underwent surgery for lung cancer from 2002-2020 in the SCCS-a prospective cohort study of nearly 85,000 predominately low-income Black and White adults from 12 states in the southeastern United States-were identified for analysis. The proportions of patients who would have been eligible for LDCT screening under the 2013 and 2021 United States
Preventive Services Task Force (USPSTF) guidelines were compared using the McNemar test. Using data from the National Cancer Database, we calculated the proportion of breast and colon cancer patients who underwent surgery and who would have been eligible for breast and colon cancer screening, respectively; these proportions were then compared to the proportion of patients who underwent lung cancer operations in SCCS that would have been eligible for LDCT screening using a chi-square test.
Results: A total of 314 lung cancer patients underwent surgical treatment. The proportion of patients who would have been eligible for screening increased by 69.5% (from 32.5% to 55.1%, P<0.001) under the 2013 vs. 2021 USPSTF lung cancer screening guidelines. However, 45% of lung cancer patients undergoing surgery would have still been ineligible for LDCT screening under the 2021 USPSTF guidelines-in comparison, only 22% of patients undergoing colon cancer surgery and 15% of patients undergoing breast cancer surgery would have been ineligible for colorectal cancer and breast cancer screening, respectively (P<0.001). Of patients with a smoking history who underwent lung cancer operations and were ineligible for screening, 70.9% had fewer than 20 pack-years, 32.7% had quit smoking more than 15 years prior, 14.6% were too young, and 2.7% were too old (Figure).
Conclusions: Even though lung cancer screening is intended to identify lung cancers at earlier stages when they are amenable to surgical treatment, we found that 45% of patients undergoing lung cancer operations would have been ineligible for lung cancer screening, highlighting the need for further revision to the USPSTF lung cancer screening guidelines.
View Submission
Invited Discussant
*Sandra Starnes, University of Cincinnati Medical Center - Contact Me Cincinnati, OHUnited States
Abstract Presenter
Alexandra Potter, University of California, Berkeley - Contact Me Wellesley, MAUnited States
17. Computational Risk Model for Predicting 2-Year Malignancy of Pulmonary Nodules using Demographic and Radiographic Characteristics
Total Time: 15 Minutes
Objective: Demographic and radiographic features may be able to characterize whether a pulmonary nodule is malignant. The purpose of this study was to develop a risk model that can predict malignancy of pulmonary nodules. We hypothesized that both conventional and machine learning-based models would be superior to provider opinion in determining the risk of malignancy.
Methods: All patients enrolled in the prospective NAVIGATE trial (NCT02410837) in the United States were included, and patients were excluded for insufficient follow-up or missing study variables. The outcome of malignancy was determined after 2-years of follow-up. Multivariate analysis of demographic data and radiographic characteristics associated with malignancy was performed using stepwise-backwards selection (α<0.1). A random forest model was also constructed using the 'randomForest' package in R version 4.2.2. The accuracy of each model was determined using receiver operating characteristics (ROC) analysis and reported as an area under the curve (AUC); this was compared to the AUC of pre-biopsy provider opinion of malignancy.
Results: Among 984 patients in the analysis, 735 (74.7%) were diagnosed with malignancy. Factors associated with malignancy in multivariable regression were: age (OR 1.03, p<0.001), lesion size (OR 1.02, p=0.001), exposure to diesel fumes (OR 0.54, p0.023), PET positivity (OR 2.60, p<0.001), history of pneumonia (OR 0.55, p=0.013), bronchus sign present (OR 1.41, p=0.042), personal history of cancer (OR 1.54, p=0.016), personal history of lung cancer (OR 2.11, p=0.020), and duration of tobacco use (OR 1.01, p<0.001). Random forest analysis was performed on the same group of patients. Variable importance in the model is shown in Figure 1, with the 3 most important variables, as determined by mean decrease in Gini Index, being subject age, largest lesion size, and total pack-years of tobacco use. The AUC for the multivariable model was 0.739, for the random forest model was 0.696, and for provider opinion was 0.828 (p<0.001).
Conclusions: Mathematical models of malignancy risk in pulmonary nodule are inferior to provider opinion. This study questions the ability of "bedside" prognostication tools using conventional radiographic and demographic characteristics to provide additional insight into patient care. Despite the importance of radiographic assessment, expert clinician evaluation of malignancy is paramount.
Methods: All patients enrolled in the prospective NAVIGATE trial (NCT02410837) in the United States were included, and patients were excluded for insufficient follow-up or missing study variables. The outcome of malignancy was determined after 2-years of follow-up. Multivariate analysis of demographic data and radiographic characteristics associated with malignancy was performed using stepwise-backwards selection (α<0.1). A random forest model was also constructed using the 'randomForest' package in R version 4.2.2. The accuracy of each model was determined using receiver operating characteristics (ROC) analysis and reported as an area under the curve (AUC); this was compared to the AUC of pre-biopsy provider opinion of malignancy.
Results: Among 984 patients in the analysis, 735 (74.7%) were diagnosed with malignancy. Factors associated with malignancy in multivariable regression were: age (OR 1.03, p<0.001), lesion size (OR 1.02, p=0.001), exposure to diesel fumes (OR 0.54, p0.023), PET positivity (OR 2.60, p<0.001), history of pneumonia (OR 0.55, p=0.013), bronchus sign present (OR 1.41, p=0.042), personal history of cancer (OR 1.54, p=0.016), personal history of lung cancer (OR 2.11, p=0.020), and duration of tobacco use (OR 1.01, p<0.001). Random forest analysis was performed on the same group of patients. Variable importance in the model is shown in Figure 1, with the 3 most important variables, as determined by mean decrease in Gini Index, being subject age, largest lesion size, and total pack-years of tobacco use. The AUC for the multivariable model was 0.739, for the random forest model was 0.696, and for provider opinion was 0.828 (p<0.001).
Conclusions: Mathematical models of malignancy risk in pulmonary nodule are inferior to provider opinion. This study questions the ability of "bedside" prognostication tools using conventional radiographic and demographic characteristics to provide additional insight into patient care. Despite the importance of radiographic assessment, expert clinician evaluation of malignancy is paramount.
View Submission
Invited Discussant
*Farhood Farjah, MD, MPH, FACS, University of Washington Medicine Center - Contact Me Seattle, WAUnited States
Abstract Presenter
Kunaal Sarnaik, Case Western Reserve University School of Medicine - Contact Me18. Impact of Medicaid Expansion under the Patient Protection and Affordable Care Act on Lung Cancer Care in the US
Total Time: 15 Minutes
Objectives. Healthcare disparities affect access to care and outcomes in lung cancer patients. The Affordable Care Act's (ACA) Medicaid expansion was implemented in 2014 with the aim of improving access to healthcare. We sought to determine the impact of Medicaid expansion on access to care and outcomes for patients with lung cancer.
Methods. This retrospective cohort study was performed using the National Cancer Database. All adults (ages 40 – 64 years) diagnosed with non-small cell lung cancer (NSCLC) between 2009 and 2019 were included. The study population was divided into a pre- (A: 2009 – 2011) and post-expansion era (B: 2015 – 2019). The exposure of interest was residence in a state that expanded Medicaid in 2014 – Medicaid expansion (ME) vs. Non-expansion (NE). Outcomes were insurance coverage, lung cancer clinical stage, treatment facility, and survival. Survival analysis and multivariable Cox regression were used to elucidate associations. A p-value <0.05 was deemed statistically significant.
Results. A total of 161,713 patients were included (era A – 36%, and era B – 64%). The mean age was 57 years, and the majority of patients were Caucasian (80%), had no comorbidities (62%) and adenocarcinoma as underlying histology (58%). There was no significant age difference between patients in the ME and NE groups in eras A and B (p>0.05 for both). From era A to B, insurance coverage increased from 90.1% to 96.7% (+6.6%), clinical stage I disease increased from 20.6% to 27.3% (+6.7%), and treatment at an academic facility increased from 39.3% to 43.9% (+4.6) in the ME group. For the NE group, the trends were 84.6% to 88.3% (+3.7%), 18.9% to 23.4% (+4.5%), and 27.8% to 28.6% (+0.8%), respectively. On univariate analysis, ME was associated with a decreased risk of mortality when compared to NE in eras A and B (p <0.05 for both). Following risk adjustment, ME remained an independent predictor for survival only in era B (HR for mortality: 0.96, CI: 0.94 – 0.98; p=0.0009).
Conclusions. The ACA ME is associated with improved insurance coverage and more frequent lung cancer treatment at academic facilities. A higher proportion of early-stage NSCLC and better survival are observed in states that implemented ME. Ongoing monitoring is necessary to confirm the program's long-term impact on access to care and survival for NSCLC.
Methods. This retrospective cohort study was performed using the National Cancer Database. All adults (ages 40 – 64 years) diagnosed with non-small cell lung cancer (NSCLC) between 2009 and 2019 were included. The study population was divided into a pre- (A: 2009 – 2011) and post-expansion era (B: 2015 – 2019). The exposure of interest was residence in a state that expanded Medicaid in 2014 – Medicaid expansion (ME) vs. Non-expansion (NE). Outcomes were insurance coverage, lung cancer clinical stage, treatment facility, and survival. Survival analysis and multivariable Cox regression were used to elucidate associations. A p-value <0.05 was deemed statistically significant.
Results. A total of 161,713 patients were included (era A – 36%, and era B – 64%). The mean age was 57 years, and the majority of patients were Caucasian (80%), had no comorbidities (62%) and adenocarcinoma as underlying histology (58%). There was no significant age difference between patients in the ME and NE groups in eras A and B (p>0.05 for both). From era A to B, insurance coverage increased from 90.1% to 96.7% (+6.6%), clinical stage I disease increased from 20.6% to 27.3% (+6.7%), and treatment at an academic facility increased from 39.3% to 43.9% (+4.6) in the ME group. For the NE group, the trends were 84.6% to 88.3% (+3.7%), 18.9% to 23.4% (+4.5%), and 27.8% to 28.6% (+0.8%), respectively. On univariate analysis, ME was associated with a decreased risk of mortality when compared to NE in eras A and B (p <0.05 for both). Following risk adjustment, ME remained an independent predictor for survival only in era B (HR for mortality: 0.96, CI: 0.94 – 0.98; p=0.0009).
Conclusions. The ACA ME is associated with improved insurance coverage and more frequent lung cancer treatment at academic facilities. A higher proportion of early-stage NSCLC and better survival are observed in states that implemented ME. Ongoing monitoring is necessary to confirm the program's long-term impact on access to care and survival for NSCLC.
View Submission
Invited Discussant
*Loretta Erhunmwunsee, City of Hope - Contact Me Duarte, CAUnited States
Abstract Presenter
Aitua Salami, University of Minnesota Medical Center - Contact Me St Louis Park, MNUnited States
Over a Decade of Lung Cancer Screening and We Still Don't Get It!
Total Time: 15 Minutes
19. Association of Air Quality with Incidence of Lung Cancer in a Large Urban/Suburban County
Total Time: 15 Minutes
Objective: Lung cancer is the leading cause of cancer-related death in the United States. The incidence of lung cancer varies geographically, but the association of environmental inequities with these disparities requires further study. Our goal was to determine the association of air quality with lung cancer over a 40-year period in a large urban/suburban county, Wayne, in Michigan.
Methods: Lung cancer data was queried from the Michigan Cancer Registry from 1985 to 2018. Air pollutant data were obtained from the United States Environmental Protection Agency from 1980 to 2018. Patient demographics and cancer incidence were recorded. SatScan (V9.7) was used to detect spatial and space-time clusters of lung cancer cases. Sensitivity analysis on maximum percentage of population at risk was conducted by using the Gini index. Phillips-Perron Unit Root Test was used to test stationary levels of both lung and air data. Akaike information criterion was used to find max lag length and bounds testing was used to test significance of cointegration between lung incidence and air quality. Autoregressive Distributed Lag Model (ARDL) was used to identify relationships between air pollutants and incidence of lung cancer.
Results: A total of 58,882 new cases of lung cancer were identified in Wayne County. Mean age was 67.8 years. Thirty-seven percent of patients identified as a racial minority. The Gini index demonstrated 5% was the optimal scanning window for spatial clusters detection and a total of 7 significant clusters were detected in the county (Figure 1). Most clusters were in downtown Detroit and in the heavily industrialized downriver area. Both lung cancer incidence and carbon monoxide (CO) levels showed similarly decreasing temporal trends from 1985 to 2018. Phillips-Perron Unit Root Tests showed both lung cancer (p=0.1463) and CO (0.99) were non-stationary. The ARDL bound testing showed lung cancer and CO levels correlated with at least 1 cointegration with max lag length of 15 (F-statistic = 25.58).
Conclusions: Poor air quality correlated with lung cancer incidence over a four-decade period. A 6 year lag between changes in air quality and lung cancer incidence was observed. Specific regions in Wayne County demonstrated an elevated relative risk of lung cancer when correlated with air pollution. Resident health in areas with poor air quality may benefit from targeted interventions such as campaigns for lung cancer screening and reduction of pollutants.
Methods: Lung cancer data was queried from the Michigan Cancer Registry from 1985 to 2018. Air pollutant data were obtained from the United States Environmental Protection Agency from 1980 to 2018. Patient demographics and cancer incidence were recorded. SatScan (V9.7) was used to detect spatial and space-time clusters of lung cancer cases. Sensitivity analysis on maximum percentage of population at risk was conducted by using the Gini index. Phillips-Perron Unit Root Test was used to test stationary levels of both lung and air data. Akaike information criterion was used to find max lag length and bounds testing was used to test significance of cointegration between lung incidence and air quality. Autoregressive Distributed Lag Model (ARDL) was used to identify relationships between air pollutants and incidence of lung cancer.
Results: A total of 58,882 new cases of lung cancer were identified in Wayne County. Mean age was 67.8 years. Thirty-seven percent of patients identified as a racial minority. The Gini index demonstrated 5% was the optimal scanning window for spatial clusters detection and a total of 7 significant clusters were detected in the county (Figure 1). Most clusters were in downtown Detroit and in the heavily industrialized downriver area. Both lung cancer incidence and carbon monoxide (CO) levels showed similarly decreasing temporal trends from 1985 to 2018. Phillips-Perron Unit Root Tests showed both lung cancer (p=0.1463) and CO (0.99) were non-stationary. The ARDL bound testing showed lung cancer and CO levels correlated with at least 1 cointegration with max lag length of 15 (F-statistic = 25.58).
Conclusions: Poor air quality correlated with lung cancer incidence over a four-decade period. A 6 year lag between changes in air quality and lung cancer incidence was observed. Specific regions in Wayne County demonstrated an elevated relative risk of lung cancer when correlated with air pollution. Resident health in areas with poor air quality may benefit from targeted interventions such as campaigns for lung cancer screening and reduction of pollutants.
View Submission
Invited Discussant
*Harvey Pass, Tisch Hospital and Kimmel Pavilion - Contact Me New York, NYUnited States
Abstract Presenter
Hollis Hutchings, Henry Ford Hospital - Contact Me Berkley, MIUnited States
20. Value of Robotic Navigational Bronchoscopy to Enhance Diagnostic Yield and Guide Oncological Strategy in Treatment of Pulmonary Nodules
Total Time: 15 Minutes
Objectives:
Robotic navigational bronchoscopy is increasingly used to improve diagnostic yield for pulmonary nodules compared to the 50-60% obtained by standard bronchoscopy, however safety and efficacy data is limited to small series. The aim of this study was to evaluate diagnostic yield and clinical outcomes in a large multi-surgeon single-center cohort.
Methods:
All patients who underwent robotic navigational bronchoscopy and biopsy from September 2020 to October 2022 were identified from a prospective institutional registry. The primary outcome was diagnostic yield, defined as the proportion of samples with diagnostic pathology. Secondary outcomes were defined according to the Society of Thoracic Surgeons General Thoracic Surgery Database quality benchmarks.
Results:
Robotic navigational bronchoscopy was used to biopsy 503 lesions in 415 patients (mean 1,6, range 1-4): median nodule size was 2.1cm, and lesions were upper lobe in 214 (42.5%) patients, peripheral in 258 (51%) and a bronchus sign was present in 121 (24%) patients. Mediastinal staging was performed using endobronchial ultrasound in 158 (38.6%) patients. Mean procedural time for robotic navigational bronchoscopy was 67+/-30 minutes. Overall diagnostic yield was 89.3% (83%, 87% and 93% for nodules ≤1cm, 1.1-2cm and >2cm, respectively). Diagnostic yield increased with greater nodule size (OR 1.03, CI 1.01-1.07, p=0.026) per 0.1cm increment. Molecular analysis sent in 101 patients was sufficient in 90% of cases. Complications occurred in 22 (5%) patients, including 13 (3.1%) pneumothoraces (7 patients requiring a chest drain), and 5 (1.2%) patients had bleeding requiring a bronchial intervention. Subsequent minimally invasive anatomic pulmonary resection was performed in 140 patients (34%). Additionally, 41 patients were consented for possible concomitant surgical resection during the same anesthesia event, 4 were stopped due to an alternative diagnosis.
Conclusions:
This study suggests robotic navigational bronchoscopy has a high diagnostic yield and obtains adequate tissue for the molecular analysis critical for selection of targeted therapies. With careful patient selection robotic navigational bronchoscopy can be combined with surgery to treat lung cancer as a single procedure with low complication rates.
Robotic navigational bronchoscopy is increasingly used to improve diagnostic yield for pulmonary nodules compared to the 50-60% obtained by standard bronchoscopy, however safety and efficacy data is limited to small series. The aim of this study was to evaluate diagnostic yield and clinical outcomes in a large multi-surgeon single-center cohort.
Methods:
All patients who underwent robotic navigational bronchoscopy and biopsy from September 2020 to October 2022 were identified from a prospective institutional registry. The primary outcome was diagnostic yield, defined as the proportion of samples with diagnostic pathology. Secondary outcomes were defined according to the Society of Thoracic Surgeons General Thoracic Surgery Database quality benchmarks.
Results:
Robotic navigational bronchoscopy was used to biopsy 503 lesions in 415 patients (mean 1,6, range 1-4): median nodule size was 2.1cm, and lesions were upper lobe in 214 (42.5%) patients, peripheral in 258 (51%) and a bronchus sign was present in 121 (24%) patients. Mediastinal staging was performed using endobronchial ultrasound in 158 (38.6%) patients. Mean procedural time for robotic navigational bronchoscopy was 67+/-30 minutes. Overall diagnostic yield was 89.3% (83%, 87% and 93% for nodules ≤1cm, 1.1-2cm and >2cm, respectively). Diagnostic yield increased with greater nodule size (OR 1.03, CI 1.01-1.07, p=0.026) per 0.1cm increment. Molecular analysis sent in 101 patients was sufficient in 90% of cases. Complications occurred in 22 (5%) patients, including 13 (3.1%) pneumothoraces (7 patients requiring a chest drain), and 5 (1.2%) patients had bleeding requiring a bronchial intervention. Subsequent minimally invasive anatomic pulmonary resection was performed in 140 patients (34%). Additionally, 41 patients were consented for possible concomitant surgical resection during the same anesthesia event, 4 were stopped due to an alternative diagnosis.
Conclusions:
This study suggests robotic navigational bronchoscopy has a high diagnostic yield and obtains adequate tissue for the molecular analysis critical for selection of targeted therapies. With careful patient selection robotic navigational bronchoscopy can be combined with surgery to treat lung cancer as a single procedure with low complication rates.
View Submission
Invited Discussant
*Philip Linden, University Hospitals Cleveland Medical Center - Contact Me Cleveland, OHUnited States
Abstract Presenter
Andrew Brownlee, Cedars-Sinai Medical Center - Contact Me Los Angeles, CAUnited States