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Wang Z, Sui X, Song W, Xue F, Han W, Hu Y, Jiang J. Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study. Cancer Med 2024; 13:e7436. [PMID: 38949177 PMCID: PMC11215689 DOI: 10.1002/cam4.7436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/28/2024] [Accepted: 06/18/2024] [Indexed: 07/02/2024] Open
Abstract
BACKGROUND The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. PURPOSE To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models. METHODS Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL-based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack-years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL-based policy models with rule-based follow-up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). RESULTS We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack-years, beyond those considered in guideline protocols) and the selection of follow-up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL-based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule-based protocols, the three best-performing RL-based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. CONCLUSIONS This study highlights the potential of using an RL-based approach that is both clinically interpretable and performance-robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.
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Affiliation(s)
- Zixing Wang
- Peking University People's HospitalPeking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver DiseasesBeijingChina
- Department of Epidemiology and BiostatisticsInstitute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical CollegeBeijingChina
| | - Xin Sui
- Department of RadiologyPeking Union Medical College HospitalBeijingChina
| | - Wei Song
- Department of RadiologyPeking Union Medical College HospitalBeijingChina
| | - Fang Xue
- Department of Epidemiology and BiostatisticsInstitute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical CollegeBeijingChina
| | - Wei Han
- Department of Epidemiology and BiostatisticsInstitute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical CollegeBeijingChina
| | - Yaoda Hu
- Department of Epidemiology and BiostatisticsInstitute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical CollegeBeijingChina
| | - Jingmei Jiang
- Department of Epidemiology and BiostatisticsInstitute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical CollegeBeijingChina
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Wang Z, Xue F, Sui X, Han W, Song W, Jiang J. Personalised follow-up and management schema for patients with screen-detected pulmonary nodules: A dynamic modelling study. Pulmonology 2024:S2531-0437(24)00040-0. [PMID: 38614860 DOI: 10.1016/j.pulmoe.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography. METHODS We developed and validated dynamic models using data of pulmonary nodule patients (aged 55-74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year). RESULTS In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827-0.894) and 0.807 (0.765-0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules. CONCLUSIONS The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.
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Affiliation(s)
- Z Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China; Peking University People's Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases. No. 11 Xizhimen South Street, Beijing, China
| | - F Xue
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China
| | - X Sui
- Department of Radiology, Peking Union Medical College Hospital. No.1 Shuaifuyuan Street, Dongcheng District, Beijing, China
| | - W Han
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China
| | - W Song
- Department of Radiology, Peking Union Medical College Hospital. No.1 Shuaifuyuan Street, Dongcheng District, Beijing, China
| | - J Jiang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China.
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Henschke C, Huber R, Jiang L, Yang D, Cavic M, Schmidt H, Kazerooni E, Zulueta JJ, Sales Dos Santos R, Ventura L. Perspective on Management of Low-Dose Computed Tomography Findings on Low-Dose Computed Tomography Examinations for Lung Cancer Screening. From the International Association for the Study of Lung Cancer Early Detection and Screening Committee. J Thorac Oncol 2024; 19:565-580. [PMID: 37979778 DOI: 10.1016/j.jtho.2023.11.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 10/24/2023] [Accepted: 11/13/2023] [Indexed: 11/20/2023]
Abstract
Lung cancer screening using low-dose computed tomography (LDCT) carefully implemented has been found to reduce deaths from lung cancer. Optimal management starts with selection of eligibility criteria, counseling of screenees, smoking cessation, selection of the regimen of screening which specifies the imaging protocol, and workup of LDCT findings. Coordination of clinical, radiologic, and interventional teams and ultimately treatment of diagnosed lung cancers under screening determine the benefit of LDCT screening. Ethical considerations of who should be eligible for LDCT screening programs are important to provide the benefit to as many people at risk of lung cancer as possible. Unanticipated diseases identified on LDCT may offer important benefits through early detection of leading global causes of death, such as cardiovascular diseases and chronic obstructive pulmonary disease, as the latter may result from conditions such as emphysema and bronchiectasis, which can be identified early on LDCT. This report identifies the key components of the regimen of LDCT screening for lung cancer which include the need for a management system to provide data for continuous updating of the regimen and provides quality assurance assessment of actual screenings. Multidisciplinary clinical management is needed to maximize the benefit of early detection, diagnosis, and treatment of lung cancer. Different regimens have been evolving throughout the world as the resources and needs may be different, for countries with limited resources. Sharing of results, further knowledge, and incorporation of technologic advances will continue to accelerate worldwide improvements in the diagnostic and treatment approaches.
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Affiliation(s)
- Claudia Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Rudolf Huber
- Division of Respiratory Medicine and Thoracic Oncology, Department of Medicine, University of Munich - Campus Innenstadt, Ziemssenstrabe, Munich, Germany
| | - Long Jiang
- Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Dawei Yang
- Department of Pulmonary Medicine and Critical Care, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Milena Cavic
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Belgrade, Serbia
| | - Heidi Schmidt
- Department of Medical Imaging, Toronto General Hospital, Toronto, Canada
| | - Ella Kazerooni
- Division of Cardiothoracic Radiology and Internal Medicine, University of Michigan Medical School, Frankel Cardiovascular Center, Ann Arbor, Michigan
| | - Javier J Zulueta
- Department of Medicine, Mount Sinai Morningside, New York, New York
| | - Ricardo Sales Dos Santos
- Department of Minimally Invasive Thoracic and Robotic Surgery, Albert Einstein Israeli Hospital, Sao Paulo, Brazil
| | - Luigi Ventura
- Department of Medicine and Surgery, University Hospital of Parma, Parma, Italy
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Wolf A, Laskey D, Yip R, Beasley MB, Yankelevitz DF, Henschke CI. Measuring the margin distance in pulmonary wedge resection. J Surg Oncol 2022; 126:1350-1358. [PMID: 35975701 DOI: 10.1002/jso.27053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/25/2022] [Accepted: 07/30/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Margin distance contributes to survival and recurrence during wedge resections for early-stage non-small cell lung cancer. The Initiative for Early Lung Cancer Research on Treatment sought to standardize a surgeon-measured margin intraoperatively. METHODS Lung cancer patients who underwent wedge resection were reviewed. Margins were measured by the surgeon twice as per a standardized protocol. Intraobserver variability as well as surgeon-pathologist variability were compared. RESULTS Forty-five patients underwent wedge resection. Same-surgeon measurement analysis indicated good reliability with a small mean difference and narrow limit of agreement for the two measures. The median surgeon-measured margin was 18.0 mm, median pathologist-measured margin was 16.0 mm and the median difference between the surgeon-pathologist margin was -1.0 mm, ranging from -18.0 to 12.0 mm. Bland-Altman analysis for margin measurements demonstrated a mean difference of 0.65 mm. The limit of agreement for the two approaches were wide, with the difference lying between -16.25 and 14.96 mm. CONCLUSIONS A novel protocol of surgeon-measured margin was evaluated and compared with pathologist-measured margin. High intraobserver agreement for repeat surgeon measurements yet low-to-moderate correlation or directionality between surgeon and pathologic measurements were found. DISCUSSION A standardized protocol may reduce variability in pathologic assessment. These findings have critical implications considering the impact of margin distance on outcomes.
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Affiliation(s)
- Andrea Wolf
- Department of Thoracic Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Daniel Laskey
- Department of Thoracic Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Rowena Yip
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mary B Beasley
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - David F Yankelevitz
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Tisch Center Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Center for Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Claudia I Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Tisch Center Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Center for Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Maurice NM, Tanner NT. Lung cancer screening at the VA: Past, present and future. Semin Oncol 2022; 49:S0093-7754(22)00041-0. [PMID: 35831214 DOI: 10.1053/j.seminoncol.2022.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 06/04/2022] [Indexed: 11/11/2022]
Abstract
Lung cancer is responsible for more deaths annually in the United States than breast, prostate and colon cancers combined. Lung cancer screening with annual low-dose computed tomography reduces lung cancer mortality in high-risk patients through early detection. The incidence of lung cancer is higher in the veteran population compared to the general population due, in part, to the prevalence of tobacco use. Early detection of lung cancer is therefore an important goal of the Veterans Health Administration (VHA), the largest integrated health care system in the United States. The following will review previous and current initiatives undertaken by the VHA to implement and expand access to lung cancer screening and will highlight target areas of interest to improve uptake and quality of lung cancer screening. Through these initiatives and programs, the VHA aims to provide high quality and equitable access to lung cancer screening for all Veterans that incorporates research that will improve outcomes and potentially inform and optimize the practice of Lung cancer screening across the United States.
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Affiliation(s)
- Nicholas M Maurice
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Atlanta, GA, U.S.A.; Atlanta Veterans Affairs Health Care System, Decatur, GA.
| | - Nichole T Tanner
- Ralph H. Johnson Veterans Affairs Hospital, Health Equity and Rural Outreach Innovation Center (HEROIC), Charleston, SC, U.S.A.; Medical University of South Carolina, Thoracic Oncology Research Group, Division of Pulmonary and Critical Care, Charleston, SC, U.S.A
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Yuan H, Gao Z, He X, Li D, Duan S, Effah CY, Wang W, Wang J, Qu L, Wu Y. Application of logistic regression and convolutional neural network in prediction and diagnosis of high-risk populations of lung cancer. Eur J Cancer Prev 2022; 31:145-151. [PMID: 33859129 DOI: 10.1097/cej.0000000000000684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
OBJECTIVES The early detection, early diagnosis, and early treatment of lung cancer are the best strategies to improve the 5-year survival rate. Logistic regression analysis can be a helpful tool in the early detection of high-risk groups of lung cancer. Convolutional neural network (CNN) could distinguish benign from malignant pulmonary nodules, which is critical for early precise diagnosis and treatment. Here, we developed a risk assessment model of lung cancer and a high-precision classification diagnostic model using these technologies so as to provide a basis for early screening of lung cancer and for intelligent differential diagnosis. METHODS A total of 355 lung cancer patients, 444 patients with benign lung disease and 472 healthy people from The First Affiliated Hospital of Zhengzhou University were included in this study. Moreover, the dataset of 607 lung computed tomography images was collected from the above patients. The logistic regression method was employed to screen the high-risk groups of lung cancer, and the CNN model was designed to classify pulmonary nodules into benign or malignant nodules. RESULTS The area under the curve of the lung cancer risk assessment model in the training set and the testing set were 0.823 and 0.710, respectively. After finely optimizing the settings of the CNN, the area under the curve could reach 0.984. CONCLUSIONS This performance demonstrated that the lung cancer risk assessment model could be used to screen for high-risk individuals with lung cancer and the CNN framework was suitable for the differential diagnosis of pulmonary nodules.
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Affiliation(s)
| | | | | | - Di Li
- Departments of Toxicology
| | | | | | | | - Jing Wang
- Occupational and Environmental Health
| | - Lingbo Qu
- Nutrition and Food Hygiene, College of Public Health, Zhengzhou University
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Han DH, Duan F, Wu Y, Goo JM, Kim HY, Patz EF. Clinical Significance of Lung-RADS Category 3 Lesions in the National Lung Screening Trial. J Thorac Oncol 2021; 16:1118-1126. [PMID: 33722708 DOI: 10.1016/j.jtho.2021.02.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 02/21/2021] [Accepted: 02/24/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION To determine the clinical significance of category 3 (CAT3) abnormalities and the necessity of a 6-month follow-up computed tomography (CT). We also explored features associated with increased lung cancer risk. METHODS From the National Lung Screening Trial database, we identified participants with CAT3 lesions at prevalence screen. Rates of lung cancer and lung cancer-specific deaths (LSDs) were compared between those who underwent first follow-up CT before 6 months (early diagnostic group) and those who underwent annual screening (annual diagnostic group). We estimated the change in LSD if the 6-month CT was eliminated. Regression analysis was performed to identify features associated with participants with CAT3 who developed lung cancer. RESULTS A total of 1763 CAT3s were identified (6.6% of all participants who had low-dose CT), with 108 lung cancers (6.1%) and 41 LSDs (2.3%) in a 7-year period. Rates of lung cancer (7.5% versus 3.1%) and LSD (4.0% versus 1.0%) were higher in the early diagnostic group than in the annual diagnostic group. We estimated an increase in LSD of 0.6% of all participants with CAT3 (24.4% of all LSDs) if the 6-month CT was not performed. Multivariate regression analysis found that increased age, emphysema, and a part-solid nodule greater than 5 mm were associated with participants with CAT3 who developed lung cancer. CONCLUSIONS CAT3 lesions are uncommon, and eliminating the 6-month CT would potentially increase LSD by 0.6% of all patients with CAT3. Age, emphysema, and part-solid nodule greater than 5 mm may be useful in risk prediction models to determine which participants with CAT3 are more likely to develop lung cancer and suggest which patients may need more intense follow-up.
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Affiliation(s)
- Dae Hee Han
- Department of Radiology, Duke University Health System, Durham, North Carolina; Present Address: Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Fenghai Duan
- Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
| | - Yanning Wu
- Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyae Young Kim
- Department of Radiology, Research Institute & Hospital, National Cancer Center, Goyang, Republic of Korea
| | - Edward F Patz
- Department of Radiology, Duke University Health System, Durham, North Carolina; Department of Pharmacology and Cancer Biology, Duke University Health System, Durham, North Carolina.
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Henschke CI, Yankelevitz DF, Jirapatnakul A, Yip R, Reccoppa V, Benjamin C, Llamo T, Williams A, Liu S, Max D, Aguayo SM, Morales P, Igel BJ, Abbaszadegan H, Fredricks PA, Garcia DP, Permana PA, Fawcett J, Sultan S, Murphy LA. Implementation of low-dose CT screening in two different health care systems: Mount Sinai Healthcare System and Phoenix VA Health Care System. Transl Lung Cancer Res 2021; 10:1064-1082. [PMID: 33718045 PMCID: PMC7947390 DOI: 10.21037/tlcr-20-761] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 11/10/2020] [Indexed: 12/18/2022]
Abstract
Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management SystemTM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management SystemTM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a "VA-ELCAP" version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.
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Affiliation(s)
- Claudia I. Henschke
- Mount Sinai Healthcare System, New York, NY, USA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | - David F. Yankelevitz
- Mount Sinai Healthcare System, New York, NY, USA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | - Artit Jirapatnakul
- Mount Sinai Healthcare System, New York, NY, USA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | - Rowena Yip
- Mount Sinai Healthcare System, New York, NY, USA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | | | | | | | | | - Simon Liu
- Mount Sinai Healthcare System, New York, NY, USA
| | - Daniel Max
- Mount Sinai Healthcare System, New York, NY, USA
| | | | | | - Brian J. Igel
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | | | | | | | | | - Janet Fawcett
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
| | - Samir Sultan
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA
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Henschke CI, Yip R, Shaham D, Zulueta JJ, Aguayo SM, Reeves AP, Jirapatnakul A, Avila R, Moghanaki D, Yankelevitz DF. The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program. J Thorac Imaging 2021; 36:6-23. [PMID: 32520848 PMCID: PMC7771636 DOI: 10.1097/rti.0000000000000538] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed.
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Affiliation(s)
- Claudia I. Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
| | - Rowena Yip
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York
| | - Dorith Shaham
- Department of Medical Imaging, Hadassah Medical Center, Jerusalem, Israel
| | - Javier J. Zulueta
- Clinica Universidad de Navarra, University of Navarra School of Medicine, Pamplona, Spain
| | | | - Anthony P. Reeves
- Department of Electrical and Computer Engineering, Cornell University, Ithaca
| | - Artit Jirapatnakul
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York
| | | | - Drew Moghanaki
- Department of Radiation Oncology, Atlanta VA Medical Center, Decatur, GA
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Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval. Eur Radiol 2020; 31:1956-1968. [PMID: 32997182 PMCID: PMC7979670 DOI: 10.1007/s00330-020-07275-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/21/2020] [Accepted: 09/09/2020] [Indexed: 12/17/2022]
Abstract
Objectives The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS). Methods Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden. Results In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6–21.9% and a potential reduction of LDCT burden of 25.5–41%. Conclusions Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible. Key Points • Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5–41% reduction of LDCT burden, at the cost of 13.6–21.9% rate of delayed diagnosis. Electronic supplementary material The online version of this article (10.1007/s00330-020-07275-w) contains supplementary material, which is available to authorized users.
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Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: comparison with the conventional reading system. Eur Radiol 2020; 31:475-485. [PMID: 32797309 DOI: 10.1007/s00330-020-07151-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/10/2020] [Accepted: 08/05/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES We aimed to compare the CT interpretation before and after the implementation of a computerized system for lung nodule detection and measurements in a nationwide lung cancer screening program. METHODS Our screening program started in April 2017, with 14 participating institutions. Initially, all CTs were interpreted using interpretation systems in each institution and manual nodule measurement (conventional system). A cloud-based CT interpretation system, equipped with semi-automated measurement and CAD (computer-aided detection) for lung nodules (cloud-based system), was implemented during the project. Positive rates and performances for lung cancer diagnosis based on the Lung-RADS version 1.0 were compared between the conventional and cloud-based systems. RESULTS A total of 1821 (M:F = 1782:39, mean age 62.7 years, 16 confirmed lung cancers) and 4666 participants (M:F = 4560:106, mean age 62.8 years, 31 confirmed lung cancers) were included in the conventional and cloud-based systems, respectively. Significantly more nodules were detected in the cloud-based system (0.76 vs. 1.07 nodule/participant, p < .001). Positive rate did not differ significantly between the two systems (9.9% vs. 11.0%, p = .211), while their variability across institutions was significantly lower in the cloud-based system (coefficients of variability, 0.519 vs. 0.311, p = .018). The Lung-RADS-based sensitivity (93.8% vs. 93.5%, p = .979) and specificity (90.9% vs. 89.6%, p = .132) did not differ significantly between the two systems. CONCLUSION Implementation of CAD and semi-automated measurement for lung nodules in a nationwide lung cancer screening program resulted in increased number of detected nodules and reduced variability in positive rates across institutions. KEY POINTS • Computer-aided CT reading detected more lung nodules than radiologists alone in lung cancer screening. • Positive rate in lung cancer screening did not change with computer-aided reading. • Computer-aided CT reading reduced inter-institutional variability in lung cancer screening.
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Veronesi G, Baldwin DR, Henschke CI, Ghislandi S, Iavicoli S, Oudkerk M, De Koning HJ, Shemesh J, Field JK, Zulueta JJ, Horgan D, Fiestas Navarrete L, Infante MV, Novellis P, Murray RL, Peled N, Rampinelli C, Rocco G, Rzyman W, Scagliotti GV, Tammemagi MC, Bertolaccini L, Triphuridet N, Yip R, Rossi A, Senan S, Ferrante G, Brain K, van der Aalst C, Bonomo L, Consonni D, Van Meerbeeck JP, Maisonneuve P, Novello S, Devaraj A, Saghir Z, Pelosi G. Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe. Cancers (Basel) 2020; 12:E1672. [PMID: 32599792 PMCID: PMC7352874 DOI: 10.3390/cancers12061672] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 12/11/2022] Open
Abstract
Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.
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Affiliation(s)
- Giulia Veronesi
- Faculty of Medicine and Surgery—Vita-Salute San Raffaele University, 20132 Milan, Italy;
- Division of Thoracic Surgery, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - David R. Baldwin
- Department of Respiratory Medicine, David Evans Research Centre, Nottingham University Hospitals NHS Trust, Nottingham NG5 1PB, UK;
| | - Claudia I. Henschke
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (C.I.H.); (N.T.); (R.Y.)
| | - Simone Ghislandi
- Department of Social and Political Sciences, Bocconi University, 20136 Milan, Italy; (S.G.); (L.F.N.)
| | - Sergio Iavicoli
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, Italian Workers’ Compensation Authority (INAIL), 00078 Rome, Italy;
| | - Matthijs Oudkerk
- Center for Medical Imaging, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands;
| | - Harry J. De Koning
- Department of Public Health, Erasmus MC—University Medical Centre Rotterdam, 3015 GD Rotterdam, The Netherlands; (H.J.D.K.); (C.v.d.A.)
| | - Joseph Shemesh
- The Grace Ballas Cardiac Research Unit, Sheba Medical Center, Affiliated with the Sackler Faculty of Medicine, Tel-Aviv University, 52621 Tel Aviv-Yafo, Israel;
| | - John K. Field
- Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, The University of Liverpool, Liverpool L69 3BX, UK;
| | - Javier J. Zulueta
- Department of Pulmonology, Clinica Universidad de Navarra, 31008 Pamplona, Spain;
- Visiongate Inc., Phoenix, AZ 85044, USA
| | - Denis Horgan
- European Alliance for Personalised Medicine (EAPM), Avenue de l’Armée Legerlaan 10, 1040 Brussels, Belgium;
| | - Lucia Fiestas Navarrete
- Department of Social and Political Sciences, Bocconi University, 20136 Milan, Italy; (S.G.); (L.F.N.)
| | | | - Pierluigi Novellis
- Division of Thoracic Surgery, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;
| | - Rachael L. Murray
- Division of Epidemiology and Public Health, UK Centre for Tobacco and Alcohol Studies, Clinical Sciences Building, City Hospital, University of Nottingham, Nottingham NG5 1PB, UK;
| | - Nir Peled
- The Legacy Heritage Oncology Center & Dr. Larry Norton Institute, Soroka Medical Center & Ben-Gurion University, 84101 Beer-Sheva, Israel;
| | - Cristiano Rampinelli
- Department of Medical Imaging and Radiation Sciences, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy;
| | - Gaetano Rocco
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Witold Rzyman
- Department of Thoracic Surgery, Medical University of Gdańsk, 80-210 Gdańsk, Poland;
| | | | - Martin C. Tammemagi
- Department of Health Sciences, Brock University, 1812 Sir Isaac Brock Way, St Catharines, ON L2S 3A1, Canada;
| | - Luca Bertolaccini
- Division of Thoracic Surgery, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy;
| | - Natthaya Triphuridet
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (C.I.H.); (N.T.); (R.Y.)
- Faculty of Medicine and Public Health, Chulabhorn Royal Academy, HRH Princess Chulabhorn College of Medical Science, Bangkok 10210, Thailand
| | - Rowena Yip
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (C.I.H.); (N.T.); (R.Y.)
| | - Alexia Rossi
- Department of Biomedical Sciences, Humanitas University, 20090 Pieve Emanuele (MI), Italy;
| | - Suresh Senan
- Department of Radiation Oncology, Amsterdam University Medical Centers, VU location, De Boelelaan 1117, Postbox 7057, 1007 MB Amsterdam, The Netherlands;
| | - Giuseppe Ferrante
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS, 20089 Rozzano (MI), Italy;
| | - Kate Brain
- Division of Population Medicine, School of Medicine, Cardiff University, Cardiff CF14 4YS, UK;
| | - Carlijn van der Aalst
- Department of Public Health, Erasmus MC—University Medical Centre Rotterdam, 3015 GD Rotterdam, The Netherlands; (H.J.D.K.); (C.v.d.A.)
| | - Lorenzo Bonomo
- Department of Bioimaging and Radiological Sciences, Catholic University, 00168 Rome, Italy;
| | - Dario Consonni
- Epidemiology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Jan P. Van Meerbeeck
- Thoracic Oncology, Antwerp University Hospital and Ghent University, 2650 Edegem, Belgium;
| | - Patrick Maisonneuve
- Division of Epidemiology and Biostatistics, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy;
| | - Silvia Novello
- Department of Oncology, University of Torino, 10124 Torino, Italy; (G.V.S.); (S.N.)
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, London SW3 6NP, UK;
| | - Zaigham Saghir
- Department of Respiratory Medicine, Herlev-Gentofte University Hospital, 2900 Hellerup, Denmark;
| | - Giuseppe Pelosi
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Inter-Hospital Pathology Division, IRCCS MultiMedica, 20138 Milan, Italy
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Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe. Cancers (Basel) 2020; 12:0. [PMID: 32599792 PMCID: PMC7352874 DOI: 10.3390/cancers12060000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.
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