1
|
Ten Haaf K, de Nijs K, Simoni G, Alban A, Cao P, Sun Z, Yong J, Jeon J, Toumazis I, Han SS, Gazelle GS, Kong CY, Plevritis SK, Meza R, de Koning HJ. The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations. Med Decis Making 2024:272989X241249182. [PMID: 38738534 DOI: 10.1177/0272989x241249182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Abstract
BACKGROUND Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. DESIGN Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior. RESULTS Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers. CONCLUSIONS Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals). IMPLICATIONS Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers. HIGHLIGHTS Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.
Collapse
Affiliation(s)
- Kevin Ten Haaf
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Koen de Nijs
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Giulia Simoni
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Andres Alban
- MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA
| | - Pianpian Cao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Zhuolu Sun
- Canadian Partnership Against Cancer, Toronto, ON, Canada
| | - Jean Yong
- Canadian Partnership Against Cancer, Toronto, ON, Canada
| | - Jihyoun Jeon
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Iakovos Toumazis
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Summer S Han
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA
| | - G Scott Gazelle
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Chung Ying Kong
- Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, NY, USA
| | - Sylvia K Plevritis
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Rafael Meza
- Department of Integrative Oncology, BC Cancer Research Institute, BC, Canada
- School of Population and Public Health, University of British Columbia, BC, Canada
| | - Harry J de Koning
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| |
Collapse
|
2
|
Miao X, Guo Y, Ding L, Xu X, Zhao K, Zhu H, Chen L, Chen Y, Zhu S, Xu Q. A dynamic online nomogram for predicting the heterogeneity trajectories of frailty among elderly gastric cancer survivors. Int J Nurs Stud 2024; 153:104716. [PMID: 38412776 DOI: 10.1016/j.ijnurstu.2024.104716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/30/2023] [Accepted: 02/02/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Frailty is very common among older people with gastric cancer and seriously affects their prognosis. The development of frailty is continuous and dynamic, increasing the difficulty and burden of care. OBJECTIVES The aims of this study were to delineate the developmental trajectory of frailty in older people with gastric cancer 1 year after surgery, identify heterogeneous frailty trajectories, and further explore their predictors to construct a nomogram for prediction. DESIGN We conducted a prospective longitudinal observation study. Clinical evaluation and data collection were performed at discharge, and at 1, 3, 6, and 12 months. SETTING AND PARTICIPANTS This study was conducted in a tertiary hospital and 381 gastric cancer patients over 60 years who underwent radical gastrectomy completed the 1-year follow-up. METHODS A growth mixture model (GMM) was used to delineate the frailty trajectories, and identify heterogeneous trajectories. A regression model was performed to determine their predictors and further construct a nomogram based on the predictors. Bootstrap with 1000 resamples was used for internal validation of nomogram, a receiver operating characteristic (ROC) curve to evaluate discrimination, calibration curves to evaluate calibration and decision curve analysis (DCA) to evaluate the clinical value. RESULTS GMM identified three classes of frailty trajectories: "frailty improving", "frailty persisting" and "frailty deteriorating". The latter two were referred to as heterogeneous frailty trajectories. Regression analysis showed 8 independent predictors of heterogeneous frailty trajectories and a nomogram was constructed based on these predictors. The area under ROC curve (AUC) of the nomogram was 0.731 (95 % CI = 0.679-0.781), the calibration curves demonstrated that probabilities predicted by the nomogram agreed well with the actual observation with a mean absolute error of 0.025, and the DCA of nomogram indicated that the net benefits were higher than that of the other eight single factors. CONCLUSIONS Older gastric cancer patients have heterogeneous frailty trajectories of poor prognosis during one-year postoperative survival. Therefore, early assessment to predict the occurrence of heterogeneous frailty trajectories is essential to improve the outcomes of elderly gastric cancer patients. Scientific and effective frailty interventions should be further explored in the future to improve the prognosis of older gastric cancer patients. CONTRIBUTION OF THE PAPER STATEMENTS This study constructed a static and dynamic online nomogram with good discrimination and calibration, which can help to screen high-risk patients, implement preoperative risk stratification easily and promote the rational allocation of medical resources greatly. REGISTRATION ClinicalTrials.gov (Number: NCT05982899). TWEETABLE ABSTRACT Our findings identified three frailty trajectories and constructed a nomogram to implement preoperative risk stratification and improve patient outcomes.
Collapse
Affiliation(s)
- Xueyi Miao
- School of Nursing, Nanjing Medical University, Nanjing 211166, China
| | - Yinning Guo
- School of Nursing, Nanjing Medical University, Nanjing 211166, China
| | - Lingyu Ding
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China
| | - Xinyi Xu
- Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Kang Zhao
- School of Nursing, Nanjing Medical University, Nanjing 211166, China
| | - Hanfei Zhu
- School of Nursing, Nanjing Medical University, Nanjing 211166, China
| | - Li Chen
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210000, China
| | - Yimeng Chen
- School of Nursing, Nanjing Medical University, Nanjing 211166, China
| | - Shuqin Zhu
- School of Nursing, Nanjing Medical University, Nanjing 211166, China.
| | - Qin Xu
- School of Nursing, Nanjing Medical University, Nanjing 211166, China.
| |
Collapse
|
3
|
Rodriguez Alvarez AA, Crosby B, Singh S, Weinberg J, Byrne N, Vazirani A, Suzuki K. Safety net hospital risk model demonstrates stronger, population-specific applicability in characterizing lung cancer risk. Transl Cancer Res 2024; 13:1596-1605. [PMID: 38737675 PMCID: PMC11082666 DOI: 10.21037/tcr-23-2304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/14/2024] [Indexed: 05/14/2024]
Abstract
Background Determining lung cancer (LC) risk using personalized risk stratification may improve screening effectiveness. While the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) is a well-established stratification model for LC screening, it was derived from a predominantly Caucasian population and its effectiveness in a safety net hospital (SNH) population is unknown. We have developed a model more tailored to the SNH population and compared its performance to the PLCO model in a SNH setting. Methods Retrospective dataset was compiled from patients screened for LC at SNH from 2015 to 2019. Descriptive statistics were calculated using the following variables: age, sex, race, education, body mass index (BMI), smoking history, personal cancer history, family LC history, chronic obstructive pulmonary disease (COPD), and emphysema. Variables distribution was compared using t- and chi-square tests. LC risk scores were calculated using SNH and PLCO models and categorized as low (scores <0.65%), moderate (0.65-1.49%), and high (>1.5%). Linear regression was applied to evaluate the relationship between models and covariates. Results Of 896 individuals, 38 were diagnosed with LC. Data reflected the SNH patient demographics, which predominantly were African American (53.5%), current smokers (69.9%), and with emphysema (70.1%). Among the non-LC cohort, SNH model most frequently categorized patients as low risk, while PLCO model most frequently classified patients as moderate risk. Among the LC cohort, there was no significant difference between mean scores or risk stratification. SNH model showed 92.1% sensitivity and 96.8% specificity while PLCO model showed 89.4% sensitivity and 26.1% specificity. Emphysema demonstrated a strong association in SNH model (P<0.001) while race showed no relation. Conclusions SNH model demonstrated greater specificity for characterizing LC risk in a SNH population. The results demonstrated the importance of study sample representation when identifying risk factors in a stratification model.
Collapse
Affiliation(s)
| | - Benjamin Crosby
- Department of Clinical Research, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Sarah Singh
- Department of Surgery, University of California Davis, Sacramento, CA, USA
| | - Janice Weinberg
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Nicole Byrne
- Department of Clinical Research, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Aniket Vazirani
- Department of Surgery, Boston Medical Center, Boston, MA, USA
| | - Kei Suzuki
- Department of Thoracic Surgery, Inova Fairfax Medical Campus, Falls Church, VA, USA
- Clinical Administration, Inova Fairfax Medical Campus, Falls Church, VA, USA
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
Na KJ, Kim YT, Goo JM, Kim H. Clinical Utility of a CT-based AI Prognostic Model for Segmentectomy in Non-Small Cell Lung Cancer. Radiology 2024; 311:e231793. [PMID: 38625008 DOI: 10.1148/radiol.231793] [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: 04/17/2024]
Abstract
Background Currently, no tool exists for risk stratification in patients undergoing segmentectomy for non-small cell lung cancer (NSCLC). Purpose To develop and validate a deep learning (DL) prognostic model using preoperative CT scans and clinical and radiologic information for risk stratification in patients with clinical stage IA NSCLC undergoing segmentectomy. Materials and Methods In this single-center retrospective study, transfer learning of a pretrained model was performed for survival prediction in patients with clinical stage IA NSCLC who underwent lobectomy from January 2008 to March 2017. The internal set was divided into training, validation, and testing sets based on the assignments from the pretraining set. The model was tested on an independent test set of patients with clinical stage IA NSCLC who underwent segmentectomy from January 2010 to December 2017. Its prognostic performance was analyzed using the time-dependent area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for freedom from recurrence (FFR) at 2 and 4 years and lung cancer-specific survival and overall survival at 4 and 6 years. The model sensitivity and specificity were compared with those of the Japan Clinical Oncology Group (JCOG) eligibility criteria for sublobar resection. Results The pretraining set included 1756 patients. Transfer learning was performed in an internal set of 730 patients (median age, 63 years [IQR, 56-70 years]; 366 male), and the segmentectomy test set included 222 patients (median age, 65 years [IQR, 58-71 years]; 114 male). The model performance for 2-year FFR was as follows: AUC, 0.86 (95% CI: 0.76, 0.96); sensitivity, 87.4% (7.17 of 8.21 patients; 95% CI: 59.4, 100); and specificity, 66.7% (136 of 204 patients; 95% CI: 60.2, 72.8). The model showed higher sensitivity for FFR than the JCOG criteria (87.4% vs 37.6% [3.08 of 8.21 patients], P = .02), with similar specificity. Conclusion The CT-based DL model identified patients at high risk among those with clinical stage IA NSCLC who underwent segmentectomy, outperforming the JCOG criteria. © RSNA, 2024 Supplemental material is available for this article.
Collapse
Affiliation(s)
- Kwon Joong Na
- From the Department of Thoracic and Cardiovascular Surgery (K.J.N., Y.T.K.) and Department of Radiology (J.M.G., H.K.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Seoul National University Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea (K.J.N., Y.T.K., J.M.G.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.)
| | - Young Tae Kim
- From the Department of Thoracic and Cardiovascular Surgery (K.J.N., Y.T.K.) and Department of Radiology (J.M.G., H.K.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Seoul National University Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea (K.J.N., Y.T.K., J.M.G.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.)
| | - Jin Mo Goo
- From the Department of Thoracic and Cardiovascular Surgery (K.J.N., Y.T.K.) and Department of Radiology (J.M.G., H.K.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Seoul National University Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea (K.J.N., Y.T.K., J.M.G.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.)
| | - Hyungjin Kim
- From the Department of Thoracic and Cardiovascular Surgery (K.J.N., Y.T.K.) and Department of Radiology (J.M.G., H.K.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Seoul National University Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea (K.J.N., Y.T.K., J.M.G.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.)
| |
Collapse
|
6
|
Markowitz S, Ringen K, Dement JM, Straif K, Christine Oliver L, Algranti E, Nowak D, Ehrlich R, McDiarmid MA, Miller A. Occupational lung cancer screening: A Collegium Ramazzini statement. Am J Ind Med 2024; 67:289-303. [PMID: 38440821 DOI: 10.1002/ajim.23572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/01/2024] [Indexed: 03/06/2024]
Affiliation(s)
- Steven Markowitz
- Barry Commoner Center for Health & the Environment, Queens College, City University of New York, New York, New York State, USA
| | - Knut Ringen
- CPWR-The Center for Construction Research and Training, Silver Spring, Maryland, USA
| | - John M Dement
- Duke University School of Medicine, Division of Occupational & Environmental Medicine, Durham, North Carolina, USA
| | - Kurt Straif
- ISGlobal, Barcelona, Spain
- Boston College, Chestnut Hill, Massachusetts, USA
| | - L Christine Oliver
- Dalla Lana School of Public Health, Division of Occupational and Environmental Health, University of Toronto, Toronto, Ontario, Canada
| | | | - Dennis Nowak
- Institute and Clinic for Occupational, Social and Environmental Medicine, LMU Klinikum, LMU Munich, CPC Munich, Comprehensive Pneumology Center Munich, #DZL, Deutsches Zentrum für Lungenforschung, Munich, Germany
| | - Rodney Ehrlich
- Division of occupational Medicine, School of Public Health, University of Cape Town, Cape Town, South Africa
| | - Melissa A McDiarmid
- Division of Occupational & Environmental Medicine, University of Maryland School of Medicine, USA
| | - Albert Miller
- Barry Commoner Center for Health & the Environment, Queens College, City University of New York, New York, New York State, USA
- Department of Medicine, Mount Sinai School of Medicine, New York, New York State, USA
| |
Collapse
|
7
|
Jaksik R, Szumała K, Dinh KN, Śmieja J. Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival. Int J Mol Sci 2024; 25:3661. [PMID: 38612473 PMCID: PMC11011391 DOI: 10.3390/ijms25073661] [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: 02/15/2024] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.
Collapse
Affiliation(s)
- Roman Jaksik
- Department of Systems Biology and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland;
| | - Kamila Szumała
- Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland;
| | - Khanh Ngoc Dinh
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, NY 10027, USA;
| | - Jarosław Śmieja
- Department of Systems Biology and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland;
| |
Collapse
|
8
|
Ten Haaf K. Considerations for Enhancing Lung Cancer Risk Prediction and Screening in Asian Populations. J Thorac Oncol 2024; 19:373-375. [PMID: 38453324 DOI: 10.1016/j.jtho.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 03/09/2024]
Affiliation(s)
- Kevin Ten Haaf
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.
| |
Collapse
|
9
|
Yang JJ, Wen W, Zahed H, Zheng W, Lan Q, Abe SK, Rahman MS, Islam MR, Saito E, Gupta PC, Tamakoshi A, Koh WP, Gao YT, Sakata R, Tsuji I, Malekzadeh R, Sugawara Y, Kim J, Ito H, Nagata C, You SL, Park SK, Yuan JM, Shin MH, Kweon SS, Yi SW, Pednekar MS, Kimura T, Cai H, Lu Y, Etemadi A, Kanemura S, Wada K, Chen CJ, Shin A, Wang R, Ahn YO, Shin MH, Ohrr H, Sheikh M, Blechter B, Ahsan H, Boffetta P, Chia KS, Matsuo K, Qiao YL, Rothman N, Inoue M, Kang D, Robbins HA, Shu XO. Lung Cancer Risk Prediction Models for Asian Ever-Smokers. J Thorac Oncol 2024; 19:451-464. [PMID: 37944700 PMCID: PMC11126207 DOI: 10.1016/j.jtho.2023.11.002] [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: 07/26/2023] [Revised: 10/19/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Although lung cancer prediction models are widely used to support risk-based screening, their performance outside Western populations remains uncertain. This study aims to evaluate the performance of 11 existing risk prediction models in multiple Asian populations and to refit prediction models for Asians. METHODS In a pooled analysis of 186,458 Asian ever-smokers from 19 prospective cohorts, we assessed calibration (expected-to-observed ratio) and discrimination (area under the receiver operating characteristic curve [AUC]) for each model. In addition, we developed the "Shanghai models" to better refine risk models for Asians on the basis of two well-characterized population-based prospective cohorts and externally validated them in other Asian cohorts. RESULTS Among the 11 models, the Lung Cancer Death Risk Assessment Tool yielded the highest AUC (AUC [95% confidence interval (CI)] = 0.71 [0.67-0.74] for lung cancer death and 0.69 [0.67-0.72] for lung cancer incidence) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model had good calibration overall (expected-to-observed ratio [95% CI] = 1.06 [0.90-1.25]). Nevertheless, these models substantially underestimated lung cancer risk among Asians who reported less than 10 smoking pack-years or stopped smoking more than or equal to 20 years ago. The Shanghai models were found to have marginal improvement overall in discrimination (AUC [95% CI] = 0.72 [0.69-0.74] for lung cancer death and 0.70 [0.67-0.72] for lung cancer incidence) but consistently outperformed the selected Western models among low-intensity smokers and long-term quitters. CONCLUSIONS The Shanghai models had comparable performance overall to the best existing models, but they improved much in predicting the lung cancer risk of low-intensity smokers and long-term quitters in Asia.
Collapse
Affiliation(s)
- Jae Jeong Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee; Department of Surgery, University of Florida College of Medicine, Gainesville, Florida; University of Florida Health Cancer Center, Gainesville, Florida
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hana Zahed
- International Agency for Research on Cancer, Lyon, France
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, Occupational and Environmental Epidemiology Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Sarah K Abe
- Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Md Shafiur Rahman
- Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan; Research Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Md Rashedul Islam
- Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan; Hitotsubashi Institute for Advanced Study, Hitotsubashi University, Tokyo, Japan
| | - Eiko Saito
- Institute for Global Health Policy Research, National Center for Global Health and Medicine, Tokyo, Japan
| | - Prakash C Gupta
- Healis - Sekhsaria Institute for Public Health Mahaleb, Navi Mumbai, India
| | - Akiko Tamakoshi
- Department of Public Health, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A∗STAR), Singapore, Singapore
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People's Republic of China
| | - Ritsu Sakata
- Radiation Effects Research Foundation, Hiroshima, Japan
| | - Ichiro Tsuji
- Tohoku University Graduate School of Medicine, Miyagi Prefecture, Japan
| | - Reza Malekzadeh
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yumi Sugawara
- Tohoku University Graduate School of Medicine, Miyagi Prefecture, Japan
| | - Jeongseon Kim
- Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea
| | - Hidemi Ito
- Division of Cancer Information and Control, Department of Preventive Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan; Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Chisato Nagata
- Department of Epidemiology and Preventive Medicine, Gifu University Graduate School of Medicine, Gifu, Japan
| | - San-Lin You
- School of Medicine & Big Data Research Center, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Myung-Hee Shin
- Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Sang-Wook Yi
- Department of Preventive Medicine and Public Health, Catholic Kwandong University College of Medicine, Gangneung, Republic of Korea
| | - Mangesh S Pednekar
- Healis - Sekhsaria Institute for Public Health Mahaleb, Navi Mumbai, India
| | - Takashi Kimura
- Department of Public Health, Hokkaido University Faculty of Medicine, Sapporo, Japan
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Yukai Lu
- Tohoku University Graduate School of Medicine, Miyagi Prefecture, Japan
| | - Arash Etemadi
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Seiki Kanemura
- Tohoku University Graduate School of Medicine, Miyagi Prefecture, Japan
| | - Keiko Wada
- Department of Epidemiology and Preventive Medicine, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Chien-Jen Chen
- Genomics Research Center, Academia Sinica, Taipei City, Taiwan
| | - Aesun Shin
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Renwei Wang
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Yoon-Ok Ahn
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Heechoul Ohrr
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Mahdi Sheikh
- International Agency for Research on Cancer, Lyon, France
| | - Batel Blechter
- Division of Cancer Epidemiology and Genetics, Occupational and Environmental Epidemiology Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Illinois
| | - Paolo Boffetta
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, New York; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Keitaro Matsuo
- Division Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan; Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - You-Lin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, Occupational and Environmental Epidemiology Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Manami Inoue
- Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | | | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.
| |
Collapse
|
10
|
Tomonaga Y, de Nijs K, Bucher HC, de Koning H, Ten Haaf K. Cost-effectiveness of risk-based low-dose computed tomography screening for lung cancer in Switzerland. Int J Cancer 2024; 154:636-647. [PMID: 37792671 DOI: 10.1002/ijc.34746] [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: 03/16/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 10/06/2023]
Abstract
Throughout Europe, computed tomography (CT) screening for lung cancer is in a phase of clinical implementation or reimbursement evaluation. To efficiently select individuals for screening, the use of lung cancer risk models has been suggested, but their incremental (cost-)effectiveness relative to eligibility based on pack-year criteria has not been thoroughly evaluated for a European setting. We evaluate the cost-effectiveness of pack-year and risk-based screening (PLCOm2012 model-based) strategies for Switzerland, which aided in informing the recommendations of the Swiss Cancer Screening Committee (CSC). We use the MISCAN (MIcrosimulation SCreening ANalysis)-Lung model to estimate benefits and harms of screening among individuals born 1940 to 1979 in Switzerland. We evaluate 1512 strategies, differing in the age ranges employed for screening, the screening interval and the strictness of the smoking requirements. We estimate risk-based strategies to be more cost-effective than pack-year-based screening strategies. The most efficient strategy compliant with CSC recommendations is biennial screening for ever-smokers aged 55 to 80 with a 1.6% PLCOm2012 risk. Relative to no screening this strategy is estimated to reduce lung cancer mortality by 11.0%, with estimated costs per Quality-Adjusted Life-Year (QALY) gained of €19 341, and a €1.990 billion 15-year budget impact. Biennial screening ages 55 to 80 for those with 20 pack-years shows a lower mortality reduction (10.5%) and higher cost per QALY gained (€20 869). Despite model uncertainties, our estimates suggest there may be cost-effective screening policies for Switzerland. Risk-based biennial screening ages 55 to 80 for those with ≥1.6% PLCOm2012 risk conforms to CSC recommendations and is estimated to be more efficient than pack-year-based alternatives.
Collapse
Affiliation(s)
- Yuki Tomonaga
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Koen de Nijs
- Department of Public Health, Erasmus MC: University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Heiner C Bucher
- Division of Clinical Epidemiology, Department of Clinical Research University Hospital Basel and University of Basel, Basel, Switzerland
| | - Harry de Koning
- Department of Public Health, Erasmus MC: University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Kevin Ten Haaf
- Department of Public Health, Erasmus MC: University Medical Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
11
|
Liu Y, Hsu HY, Lin T, Peng B, Saqi A, Salvatore MM, Jambawalikar S. Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans. J Transl Med 2024; 22:51. [PMID: 38216992 PMCID: PMC10787502 DOI: 10.1186/s12967-023-04798-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/11/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND Chest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung cancer risk, current clinical practice characterizes nodule risk of malignancy based on nodule size and smoking history; little consideration is given to the fibrotic microenvironment. PURPOSE To evaluate the effect of incorporating fibrotic microenvironment into classifying malignancy of lung nodules in chest CT images using deep learning techniques. MATERIALS AND METHODS We developed a visualizable 3D classification model trained with in-house CT dataset for the nodule malignancy classification task. Three slightly-modified datasets were created: (1) nodule alone (microenvironment removed); (2) nodule with surrounding lung microenvironment; and (3) nodule in microenvironment with semantic fibrosis metadata. For each of the models, tenfold cross-validation was performed. Results were evaluated using quantitative measures, such as accuracy, sensitivity, specificity, and area-under-curve (AUC), as well as qualitative assessments, such as attention maps and class activation maps (CAM). RESULTS The classification model trained with nodule alone achieved 75.61% accuracy, 50.00% sensitivity, 88.46% specificity, and 0.78 AUC; the model trained with nodule and microenvironment achieved 79.03% accuracy, 65.46% sensitivity, 85.86% specificity, and 0.84 AUC. The model trained with additional semantic fibrosis metadata achieved 80.84% accuracy, 74.67% sensitivity, 84.95% specificity, and 0.89 AUC. Our visual evaluation of attention maps and CAM suggested that both the nodules and the microenvironment contributed to the task. CONCLUSION The nodule malignancy classification performance was found to be improving with microenvironment data. Further improvement was found when incorporating semantic fibrosis information.
Collapse
Affiliation(s)
- Yucheng Liu
- Department of Radiology, Columbia University Irving Medical Center, 3-124B Milstein Hospital Bldg, 177 Fort Washington Avenue, New York, NY, 10032, USA.
| | - Hao Yun Hsu
- Department of Radiology, Columbia University Irving Medical Center, 3-124B Milstein Hospital Bldg, 177 Fort Washington Avenue, New York, NY, 10032, USA
| | - Tiffany Lin
- Department of Radiology, Columbia University Irving Medical Center, 3-124B Milstein Hospital Bldg, 177 Fort Washington Avenue, New York, NY, 10032, USA
| | - Boyu Peng
- Department of Radiology, Columbia University Irving Medical Center, 3-124B Milstein Hospital Bldg, 177 Fort Washington Avenue, New York, NY, 10032, USA
| | - Anjali Saqi
- Department of Pathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mary M Salvatore
- Department of Radiology, Columbia University Irving Medical Center, 3-124B Milstein Hospital Bldg, 177 Fort Washington Avenue, New York, NY, 10032, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, 3-124B Milstein Hospital Bldg, 177 Fort Washington Avenue, New York, NY, 10032, USA
| |
Collapse
|
12
|
Cortés-Ibáñez FO, Johnson T, Mascalchi M, Katzke V, Delorme S, Kaaks R. Serum-based biomarkers associated with lung cancer risk and cause-specific mortality in the German randomized Lung Cancer Screening Intervention (LUSI) trial. Transl Lung Cancer Res 2023; 12:2460-2475. [PMID: 38205209 PMCID: PMC10775005 DOI: 10.21037/tlcr-23-548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024]
Abstract
Background Lung cancer (LC) screening can be optimized using individuals' estimated risks of having a detectable lung tumor, as well as of mortality risk by competing causes, to guide decisions on screening eligibility, ideal screening intervals and stopping ages. Besides age, sex and smoking history, blood-based biomarkers may be used to improve the assessment of LC risk and risk of mortality by competing causes. Methods In the German randomized Lung Screening Intervention Trial (LUSI), we measured growth/differentiation factor-15 (GDF-15), interleukin-6 (IL-6), C-reactive protein (CRP) and N-terminal pro-brain natriuretic protein (NT-proBNP), in blood serum samples collected at start of the trial. Participants in the computed tomography (CT)-screening arm also had a pulmonary function test. Regression models were used to examine these markers as predictors for impaired lung function, LC risk and mortality due to LC or other causes, independently of age, sex and smoking history. Results Our models showed increases in LC risk among participants with elevated serum levels of GDF-15 [odds ratio (OR)Q4-Q1 =2.47, 95% confidence interval (CI): 1.49-4.26], IL-6 [ORQ4-Q1 =2.36 (1.43-4.00)] and CRP [ORQ4-Q1 =1.81 (1.08-2.75)]. Likewise, proportional hazards models showed increased risks for LC-related mortality, hazard ratio (HR)Q4-Q1 of 4.63 (95% CI: 2.13-10.07) for GDF-15, 3.56 (1.72-7.37) for IL-6 and 2.34 (1.24-4.39) for CRP. All four markers were associated with increased risk of mortality by causes other than LC, with strongest associations for GDF-15 [HRQ4-Q1 =3.04 (2.09-4.43)] and IL-6 [HRQ4-Q1 =2.98 (2.08-4.28)]. Significant associations were also observed between IL-6, CRP, GDF-15 and impaired pulmonary function [chronic obstructive pulmonary disease (COPD), preserved ratio impaired spirometry (PRISm)]. Multi-marker models identified GDF-15 and IL-6 as joint risk predictors for risk of LC diagnosis, without further discrimination by CRP or NT-proBNP. A model based on age, sex, smoking-related variables, GFD-15 and IL-6 provided moderately strong discrimination for prediction of LC diagnoses within 9 years after blood sampling [area under the curve (AUC) =74.3% (57.3-90.2%)], compared to 67.0% (49.3-84.8%) for a model without biomarkers. For mortality by competing causes, a model including biomarkers resulted in an AUC of 76.2% (66.6-85.3%)], compared to 70.0% (60.9-77.9%) a model including age, sex and smoking variables. Conclusions Serum GDF-15 and IL-6 may be useful indicators for estimating risks for LC and competing mortality among long-term smokers participating in LC screening, to optimize LC screening strategies.
Collapse
Affiliation(s)
- Francisco O. Cortés-Ibáñez
- Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Theron Johnson
- Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mario Mascalchi
- Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and Network in Oncology (ISPRO), Florence, Italy
- Department of Clinical and Experimental Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy
| | - Verena Katzke
- Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Delorme
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology (C020), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), the German Center for Lung Research (DZL), Heidelberg, Germany
| |
Collapse
|
13
|
Choi E, Ding VY, Luo SJ, ten Haaf K, Wu JT, Aredo JV, Wilkens LR, Freedman ND, Backhus LM, Leung AN, Meza R, Lui NS, Haiman CA, Park SSL, Le Marchand L, Neal JW, Cheng I, Wakelee HA, Tammemägi MC, Han SS. Risk Model-Based Lung Cancer Screening and Racial and Ethnic Disparities in the US. JAMA Oncol 2023; 9:1640-1648. [PMID: 37883107 PMCID: PMC10603577 DOI: 10.1001/jamaoncol.2023.4447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/11/2023] [Indexed: 10/27/2023]
Abstract
Importance The revised 2021 US Preventive Services Task Force (USPSTF) guidelines for lung cancer screening have been shown to reduce disparities in screening eligibility and performance between African American and White individuals vs the 2013 guidelines. However, potential disparities across other racial and ethnic groups in the US remain unknown. Risk model-based screening may reduce racial and ethnic disparities and improve screening performance, but neither validation of key risk prediction models nor their screening performance has been examined by race and ethnicity. Objective To validate and recalibrate the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 (PLCOm2012) model-a well-established risk prediction model based on a predominantly White population-across races and ethnicities in the US and evaluate racial and ethnic disparities and screening performance through risk-based screening using PLCOm2012 vs the USPSTF 2021 criteria. Design, Setting, and Participants In a population-based cohort design, the Multiethnic Cohort Study enrolled participants in 1993-1996, followed up through December 31, 2018. Data analysis was conducted from April 1, 2022, to May 19. 2023. A total of 105 261 adults with a smoking history were included. Exposures The 6-year lung cancer risk was calculated through recalibrated PLCOm2012 (ie, PLCOm2012-Update) and screening eligibility based on a 6-year risk threshold greater than or equal to 1.3%, yielding similar eligibility as the USPSTF 2021 guidelines. Outcomes Predictive accuracy, screening eligibility-incidence (E-I) ratio (ie, ratio of the number of eligible to incident cases), and screening performance (sensitivity, specificity, and number needed to screen to detect 1 lung cancer). Results Of 105 261 participants (60 011 [57.0%] men; mean [SD] age, 59.8 [8.7] years), consisting of 19 258 (18.3%) African American, 27 227 (25.9%) Japanese American, 21 383 (20.3%) Latino, 8368 (7.9%) Native Hawaiian/Other Pacific Islander, and 29 025 (27.6%) White individuals, 1464 (1.4%) developed lung cancer within 6 years from enrollment. The PLCOm2012-Update showed good predictive accuracy across races and ethnicities (area under the curve, 0.72-0.82). The USPSTF 2021 criteria yielded a large disparity among African American individuals, whose E-I ratio was 53% lower vs White individuals (E-I ratio: 9.5 vs 20.3; P < .001). Under the risk-based screening (PLCOm2012-Update 6-year risk ≥1.3%), the disparity between African American and White individuals was substantially reduced (E-I ratio: 15.9 vs 18.4; P < .001), with minimal disparities observed in persons of other minoritized groups, including Japanese American, Latino, and Native Hawaiian/Other Pacific Islander. Risk-based screening yielded superior overall and race and ethnicity-specific performance to the USPSTF 2021 criteria, with higher overall sensitivity (67.2% vs 57.7%) and lower number needed to screen (26 vs 30) at similar specificity (76.6%). Conclusions The findings of this cohort study suggest that risk-based lung cancer screening can reduce racial and ethnic disparities and improve screening performance across races and ethnicities vs the USPSTF 2021 criteria.
Collapse
Affiliation(s)
- Eunji Choi
- Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California
| | - Victoria Y. Ding
- Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California
| | - Sophia J. Luo
- Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California
| | - Kevin ten Haaf
- Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Julie T. Wu
- Stanford University School of Medicine, Stanford, California
| | | | - Lynne R. Wilkens
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Neal D. Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Leah M. Backhus
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California
| | - Ann N. Leung
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Rafael Meza
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor
| | - Natalie S. Lui
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles
| | - Sung-Shim Lani Park
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Loïc Le Marchand
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Joel W. Neal
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Heather A. Wakelee
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Martin C. Tammemägi
- Department of Health Sciences, Brock University, St Catharines, Ontario, Canada
| | - Summer S. Han
- Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
| |
Collapse
|
14
|
Jani BD, Sullivan MK, Hanlon P, Nicholl BI, Lees JS, Brown L, MacDonald S, Mark PB, Mair FS, Sullivan FM. Personalised lung cancer risk stratification and lung cancer screening: do general practice electronic medical records have a role? Br J Cancer 2023; 129:1968-1977. [PMID: 37880510 PMCID: PMC10703821 DOI: 10.1038/s41416-023-02467-9] [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: 03/30/2023] [Revised: 10/06/2023] [Accepted: 10/13/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND In the United Kingdom (UK), cancer screening invitations are based on general practice (GP) registrations. We hypothesize that GP electronic medical records (EMR) can be utilised to calculate a lung cancer risk score with good accuracy/clinical utility. METHODS The development cohort was Secure Anonymised Information Linkage-SAIL (2.3 million GP EMR) and the validation cohort was UK Biobank-UKB (N = 211,597 with GP-EMR availability). Fast backward method was applied for variable selection and area under the curve (AUC) evaluated discrimination. RESULTS Age 55-75 were included (SAIL: N = 574,196; UKB: N = 137,918). Six-year lung cancer incidence was 1.1% (6430) in SAIL and 0.48% (656) in UKB. The final model included 17/56 variables in SAIL for the EMR-derived score: age, sex, socioeconomic status, smoking status, family history, body mass index (BMI), BMI:smoking interaction, alcohol misuse, chronic obstructive pulmonary disease, coronary heart disease, dementia, hypertension, painful condition, stroke, peripheral vascular disease and history of previous cancer and previous pneumonia. The GP-EMR-derived score had AUC of 80.4% in SAIL and 74.4% in UKB and outperformed ever-smoked criteria (currently the first step in UK lung cancer screening pilots). DISCUSSION A GP-EMR-derived score may have a role in UK lung cancer screening by accurately targeting high-risk individuals without requiring patient contact.
Collapse
Affiliation(s)
- Bhautesh Dinesh Jani
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, UK.
| | - Michael K Sullivan
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Peter Hanlon
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Barbara I Nicholl
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Jennifer S Lees
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Lamorna Brown
- Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews, UK
| | - Sara MacDonald
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Patrick B Mark
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Frances S Mair
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Frank M Sullivan
- Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews, UK
| |
Collapse
|
15
|
Wang N, Yao C, Luo C, Liu S, Wu L, Hu W, Zhang Q, Rong Y, Yuan C, Wang F. Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer. Clin Chem Lab Med 2023; 61:2216-2228. [PMID: 37387637 DOI: 10.1515/cclm-2023-0291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023]
Abstract
OBJECTIVES Non-small cell lung cancer (NSCLC) accounts for more than 80 % of all lung cancers, and its 5-year survival rate can be greatly improved by early diagnosis. However, early diagnosis remains elusive because of the lack of effective biomarkers. In this study, we aimed to develop an effective diagnostic model for NSCLC based on a combination of circulating biomarkers. METHODS Tissue-deregulated long noncoding RNAs (lncRNAs) in NSCLC were identified in datasets retrieved from the Gene Expression Omnibus (GEO, n=727) and The Cancer Genome Atlas (TCGA, n=1,135) databases, and their differential expression was verified in paired local plasma and exosome samples from NSCLC patients. Subsequently, LASSO regression was used to screen for biomarkers in a large clinical population, and a logistic regression model was used to establish a multi-marker diagnostic model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), clinical impact curves, and integrated discrimination improvement (IDI) were used to evaluate the efficiency of the diagnostic model. RESULTS Three lncRNAs-PGM5-AS1, SFTA1P, and CTA-384D8.35 were consistently expressed in online tissue datasets, plasma, and exosomes from local patients. LASSO regression identified nine variables (Plasma CTA-384D8.35, Plasma PGM5-AS1, Exosome CTA-384D8.35, Exosome PGM5-AS1, Exosome SFTA1P, Log10CEA, Log10CA125, SCC, and NSE) in clinical samples that were eventually included in the multi-marker diagnostic model. Logistic regression analysis revealed that Plasma CTA-384D8.35, exosome SFTA1P, Log10CEA, Exosome CTA-384D8.35, SCC, and NSE were independent risk factors for NSCLC (p<0.01), and their results were visualized using a nomogram to obtain personalized prediction outcomes. The constructed diagnostic model demonstrated good NSCLC prediction ability in both the training and validation sets (AUC=0.97). CONCLUSIONS In summary, the constructed circulating lncRNA-based diagnostic model has good NSCLC prediction ability in clinical samples and provides a potential diagnostic tool for NSCLC.
Collapse
Affiliation(s)
- Na Wang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Cong Yao
- Health Care Department, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Changliang Luo
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Department of Laboratory Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, P.R. China
| | - Shaoping Liu
- Medical Science Research Center, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Long Wu
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, P.R. China
| | - Weidong Hu
- Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Qian Zhang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Yuan Rong
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
| | - Chunhui Yuan
- Department of Laboratory Medicine, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan, P.R. China
- Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, P.R. China
| |
Collapse
|
16
|
Susai CJ, Velotta JB, Sakoda LC. Clinical Adjuncts to Lung Cancer Screening: A Narrative Review. Thorac Surg Clin 2023; 33:421-432. [PMID: 37806744 PMCID: PMC10926946 DOI: 10.1016/j.thorsurg.2023.03.002] [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] [Indexed: 10/10/2023]
Abstract
The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.
Collapse
Affiliation(s)
- Cynthia J Susai
- UCSF East Bay General Surgery, 1411 East 31st Street QIC 22134, Oakland, CA 94612, USA
| | - Jeffrey B Velotta
- Department of Thoracic Surgery, Kaiser Permanente Northern California, 3600 Broadway, Oakland, CA 94611, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA.
| |
Collapse
|
17
|
Makimoto K, Hogg JC, Bourbeau J, Tan WC, Kirby M. CT Imaging With Machine Learning for Predicting Progression to COPD in Individuals at Risk. Chest 2023; 164:1139-1149. [PMID: 37421974 DOI: 10.1016/j.chest.2023.06.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/26/2023] [Accepted: 06/05/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND Identifying individuals at risk of progressing to COPD may allow for initiation of treatment to potentially slow the progression of the disease or the selection of subgroups for discovery of novel interventions. RESEARCH QUESTION Does the addition of CT imaging features, texture-based radiomic features, and established quantitative CT scan to conventional risk factors improve the performance for predicting progression to COPD in individuals who smoke with machine learning? STUDY DESIGN AND METHODS Participants at risk (individuals who currently or formerly smoked, without COPD) from the Canadian Cohort Obstructive Lung Disease (CanCOLD) population-based study underwent CT imaging at baseline and spirometry at baseline and follow-up. Various combinations of CT scan features, texture-based CT scan radiomics (n = 95), and established quantitative CT scan (n = 8), as well as demographic (n = 5) and spirometry (n = 3) measurements, with machine learning algorithms were evaluated to predict progression to COPD. Performance metrics included the area under the receiver operating characteristic curve (AUC) to evaluate the models. DeLong test was used to compare the performance of the models. RESULTS Among the 294 at-risk participants who were evaluated (mean age, 65.6 ± 9.2 years; 42% female; mean pack-years, 17.9 ± 18.7), 52 participants (23.7%) in the training data set and 17 participants (23.0%) in the testing data set progressed to spirometric COPD at follow-up (2.5 ± 0.9 years from baseline). Compared with machine learning models with demographics alone (AUC, 0.649), the addition of CT imaging features to demographics (AUC, 0.730; P < .05) or CT imaging features and spirometry to demographics (AUC, 0.877; P < .05) significantly improved the performance for predicting progression to COPD. INTERPRETATION Heterogeneous structural changes occur in the lungs of individuals at risk that can be quantified using CT imaging features, and evaluation of these features together with conventional risk factors improves performance for predicting progression to COPD.
Collapse
Affiliation(s)
| | - James C Hogg
- Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada
| | - Jean Bourbeau
- Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Wan C Tan
- Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada
| | - Miranda Kirby
- Toronto Metropolitan University, Toronto, ON, Canada.
| |
Collapse
|
18
|
Senthil P, Kuhan S, Potter AL, Jeffrey Yang CF. Update on Lung Cancer Screening Guideline. Thorac Surg Clin 2023; 33:323-331. [PMID: 37806735 DOI: 10.1016/j.thorsurg.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Lung cancer screening has been shown to reduce lung cancer mortality and is recommended for individuals meeting age and smoking history criteria. Despite the expansion of lung cancer screening guidelines in 2021, racial/ethnic and sex disparities persist. High-risk racial minorities and women are more likely to be diagnosed with lung cancer at younger ages and have lower smoking histories when compared with White and male counterparts, resulting in higher rates of ineligibility for screening. Risk prediction models, biomarkers, and deep learning may help refine the selection of individuals who would benefit from screening.
Collapse
Affiliation(s)
- Priyanka Senthil
- Division of Thoracic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Sangkavi Kuhan
- Division of Thoracic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Alexandra L Potter
- Division of Thoracic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Chi-Fu Jeffrey Yang
- Division of Thoracic Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
| |
Collapse
|
19
|
Stephens EKH, Guayco Sigcha J, Lopez-Loo K, Yang IA, Marshall HM, Fong KM. Biomarkers of lung cancer for screening and in never-smokers-a narrative review. Transl Lung Cancer Res 2023; 12:2129-2145. [PMID: 38025810 PMCID: PMC10654441 DOI: 10.21037/tlcr-23-291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023]
Abstract
Background and Objective Lung cancer is the leading cause of cancer-related mortality worldwide, partially attributed to late-stage diagnoses. In order to mitigate this, lung cancer screening (LCS) of high-risk patients is performed using low dose computed tomography (CT) scans, however this method is burdened by high false-positive rates and radiation exposure for patients. Further, screening programs focus on individuals with heavy smoking histories, and as such, never-smokers who may otherwise be at risk of lung cancer are often overlooked. To resolve these limitations, biomarkers have been posited as potential supplements or replacements to low-dose CT, and as such, a large body of research in this area has been produced. However, comparatively little information exists on their clinical efficacy and how this compares to current LCS strategies. Methods Here we conduct a search and narrative review of current literature surrounding biomarkers of lung cancer to supplement LCS, and biomarkers of lung cancer in never-smokers (LCINS). Key Content and Findings Many potential biomarkers of lung cancer have been identified with varying levels of sensitivity, specificity, clinical efficacy, and supporting evidence. Of the markers identified, multi-target panels of circulating microRNAs, lipids, and metabolites are likely the most clinically efficacious markers to aid current screening programs, as these provide the highest sensitivity and specificity for lung cancer detection. However, circulating lipid and metabolite levels are known to vary in numerous systemic pathologies, highlighting the need for further validation in large cohort randomised studies. Conclusions Lung cancer biomarkers is a fast-expanding area of research and numerous biomarkers with potential clinical applications have been identified. However, in all cases the level of evidence supporting clinical efficacy is not yet at a level at which it can be translated to clinical practice. The priority now should be to validate existing candidate markers in appropriate clinical contexts and work to integrating these into clinical practice.
Collapse
Affiliation(s)
- Edward K. H. Stephens
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Jazmin Guayco Sigcha
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Kenneth Lopez-Loo
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Ian A. Yang
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, Australia
| | - Henry M. Marshall
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, Australia
| | - Kwun M. Fong
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, Australia
| |
Collapse
|
20
|
Malki A, Shaik RA, Sami W. Association between metabolically healthy obesity and metastasis in lung cancer patients - a systematic review and meta-analysis. Front Endocrinol (Lausanne) 2023; 14:1238459. [PMID: 37842311 PMCID: PMC10571134 DOI: 10.3389/fendo.2023.1238459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/15/2023] [Indexed: 10/17/2023] Open
Abstract
Background Many clinical trials have looked at the relationship between obesity and lung cancer (LC), however, there is scarcity of literature specifically addressing the association between metabolically healthy obesity and metastasis in LC patients. To address this gap in the body of evidence, the study was conducted to observe the association between metabolically healthy obesity and metastasis in LC patients. Methods We conducted a pre-registered systematic review by searching six major online databases to identify studies relevant related to our investigation, in adherence with the PRISMA guidelines. A proper data extraction protocol was further established to synthesize the findings from the selected papers through a meta-analysis. Results Eleven (11) studies met the requisite selection criterion and were included in the study. A random-effect model was used. Obesity was found to have a significant impact on readmission in LC patients. The combined analysis showed a significant effect size of 0.08 (95% CI 0.07 to 0.08), indicating a noticeable impact of obesity. It was also assessed that obese individuals had a 34% reduced risk of LC compared to normal weight individuals. Obesity was associated with a lower risk of surgical complications with a pooled risk ratio of 0.13 (95% CI 0.12 to 0.14). A statistically significant decreased risk of LC (pooled RR = 0.72, 95% CI: 0.68 to 0.77) was also observed in the obese individuals. Conclusion The analysis reveals that obesity is associated with a noticeable increase in readmissions, although the impact on LC risk itself is negligible. Moreover, obesity appears to have a beneficial effect by reducing the risk of surgical complications. These results highlight the complex relationship between the two aforementioned factors, emphasizing the importance of considering obesity as a significant factor in patient management and healthcare decision-making. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023427612.
Collapse
Affiliation(s)
- Ahmed Malki
- Biomedical Science Department, College of Health Sciences, QU-Health, Qatar University, Doha, Qatar
| | - Riyaz Ahamed Shaik
- Department of Family and Community Medicine, College of Medicine, Majmaah University, Majmaah, Saudi Arabia
| | - Waqas Sami
- Department of Pre-clinical Affairs, College of Nursing, QU-Health, Qatar University, Doha, Qatar
| |
Collapse
|
21
|
Callender T, Imrie F, Cebere B, Pashayan N, Navani N, van der Schaar M, Janes SM. Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study. PLoS Med 2023; 20:e1004287. [PMID: 37788223 PMCID: PMC10547178 DOI: 10.1371/journal.pmed.1004287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/29/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. METHODS AND FINDINGS For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. CONCLUSIONS We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.
Collapse
Affiliation(s)
- Thomas Callender
- Department of Respiratory Medicine, University College London, London, United Kingdom
| | - Fergus Imrie
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California, United States of America
| | - Bogdan Cebere
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London, United Kingdom
| | - Neal Navani
- Department of Respiratory Medicine, University College London, London, United Kingdom
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, United Kingdom
- Alan Turing Institute, London, United Kingdom
| | - Sam M. Janes
- Department of Respiratory Medicine, University College London, London, United Kingdom
| |
Collapse
|
22
|
Hirsch EA, New ML, Brown SL, Malkoski SP. Results of a pilot risk-based lung cancer screening study: outcomes and comparisons to a Medicare eligible cohort. Discov Oncol 2023; 14:160. [PMID: 37642787 PMCID: PMC10465462 DOI: 10.1007/s12672-023-00773-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/18/2023] [Indexed: 08/31/2023] Open
Abstract
PURPOSE Risk-based lung cancer screening holds potential to detect more cancers and avert more cancer deaths than screening based on age and smoking history alone, but has not been widely assessed or implemented in the United States. The purpose of this study was to prospectively identify patients for lung cancer screening based on lung cancer risk using the PLCOm2012 model and to compare characteristics, risk profiles, and screening outcomes to a traditionally eligible screening cohort. METHODS Participants who had a 6 year lung cancer risk score ≥ 1.5% calculated by the PLCOm2012 model and were ineligible for screening under 2015 Medicare guidelines were recruited from a lung cancer screening clinic. After informed consent, participants completed shared decision-making counseling and underwent a low-dose CT (LDCT). Characteristics and screening outcomes of the study population were compared to the traditionally eligible Medicare cohort with Fisher's Exact, t-tests, or Brown Mood tests, as appropriate. RESULTS From August 2016 to July 2019, the study completed 48 baseline LDCTs. 10% of LDCTs recommended further pulmonary nodule evaluation (Lung-RADs 3 or 4) with two early-stage lung cancers diagnosed in individuals that had quit smoking > 15 years prior. The study population was approximately 5 years older (p = 0.001) and had lower pack years (p = 0.002) than the Medicare cohort. CONCLUSION Prospective application of risk-based screening identifies screening candidates who are similar to a traditionally eligible Medicare cohort and future research should focus on the impact of risk calculators on lung cancer outcomes and optimal usability in clinical environments. This study was retrospectively registered on clinicaltrials.gov (NCT03683940) on 09/25/2018.
Collapse
Affiliation(s)
- Erin A Hirsch
- Division of Medical Oncology, University of Colorado Anschutz Medical Campus, 12700 E 19th Ave, Aurora, CO, 80045, USA.
| | - Melissa L New
- Pulmonary Section, Rocky Mountain Regional VA Medical Center, 1700 N. Wheeling Street, Aurora, CO, 80045, USA
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 12700 E 19th Ave, Mail Stop C272, Aurora, CO, 80045, USA
| | - Stephanie L Brown
- University of Colorado Hospital, UCHealth Denver Metro, Aurora, CO, 80045, USA
| | - Stephen P Malkoski
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Anschutz Medical Campus, 12700 E 19th Ave, Mail Stop C272, Aurora, CO, 80045, USA
- Department of Medicine, University of Washington, WWAMI - Spokane, 502 E Boone Ave, Spokane, WA, 99258, USA
- Sound Critical Care, Sacred Heart Medical Center, 101 W. 8th Avenue, Spokane, WA, 99204, USA
| |
Collapse
|
23
|
Ruparel M. Lung cancer screening in advanced chronic obstructive pulmonary disease: helpful or harmful? Thorax 2023; 78:637-639. [PMID: 36944495 DOI: 10.1136/thorax-2022-219778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/28/2023] [Indexed: 03/23/2023]
Affiliation(s)
- Mamta Ruparel
- Respiratory and Critical Care Medicine, National University Hospital, Singapore
| |
Collapse
|
24
|
Horsfall LJ, Clarke CS, Nazareth I, Ambler G. The value of blood-based measures of liver function and urate in lung cancer risk prediction: A cohort study and health economic analysis. Cancer Epidemiol 2023; 84:102354. [PMID: 36989955 PMCID: PMC10636591 DOI: 10.1016/j.canep.2023.102354] [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: 09/29/2022] [Revised: 03/03/2023] [Accepted: 03/15/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Several studies have reported associations between low-cost blood-based measurements and lung cancer but their role in risk prediction is unclear. We examined the value of expanding lung cancer risk models for targeting low-dose computed tomography (LDCT), including blood measurements of liver function and urate. METHODS We analysed a cohort of 388,199 UK Biobank participants with 1873 events and calculated the c-index and fraction of new information (FNI) for models expanded to include combinations of blood measurements, lung function (forced expiratory volume in 1 s - FEV1), alcohol status and waist circumference. We calculated the hypothetical cost per lung cancer case detected by LDCT for different scenarios using a threshold of ≥ 1.51 % risk at 6 years. RESULTS The c-index was 0.805 (95 %CI:0.794-0.816) for the model containing conventional predictors. Expanding to include blood measurements increased the c-index to 0.815 (95 %CI: 0.804-0.826;p < 0.0001;FNI:0.06). Expanding to include FEV1, alcohol status, and waist circumference increased the c-index to 0.811 (95 %CI: 0.800-0.822;p < 0.0001;FNI: 0.04). The c-index for the fully expanded model containing all variables was 0.819 (95 %CI:0.808-0.830;p < 0.0001;FNI:0.09). Model expansion had a greater impact on the c-index and FNI for people with a history of smoking cigarettes relative to the full cohort. Compared with the conventional risk model, the expanded models reduced the number of participants meeting the criteria for LDCT screening by 15-21 %, and lung cancer cases detected by 7-8 %. The additional cost per lung cancer case detected relative to the conventional model was £ 1018 for adding blood tests and £ 9775 for the fully expanded model. CONCLUSION Blood measurements of liver function and urate made a modest improvement to lung cancer risk prediction compared with a model containing conventional risk factors. There was no evidence that model expansion would improve the cost per lung cancer case detected in UK healthcare settings.
Collapse
Affiliation(s)
- Laura J Horsfall
- Department of Primary Care and Population Health, University College London, UK.
| | - Caroline S Clarke
- Department of Primary Care and Population Health, University College London, UK
| | - Irwin Nazareth
- Department of Primary Care and Population Health, University College London, UK
| | - Gareth Ambler
- Department of Statistical Science, University College London, UK
| |
Collapse
|
25
|
O'Dowd EL, Lee RW, Akram AR, Bartlett EC, Bradley SH, Brain K, Callister MEJ, Chen Y, Devaraj A, Eccles SR, Field JK, Fox J, Grundy S, Janes SM, Ledson M, MacKean M, Mackie A, McManus KG, Murray RL, Nair A, Quaife SL, Rintoul R, Stevenson A, Summers Y, Wilkinson LS, Booton R, Baldwin DR, Crosbie P. Defining the road map to a UK national lung cancer screening programme. Lancet Oncol 2023; 24:e207-e218. [PMID: 37142382 DOI: 10.1016/s1470-2045(23)00104-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 05/06/2023]
Abstract
Lung cancer screening with low-dose CT was recommended by the UK National Screening Committee (UKNSC) in September, 2022, on the basis of data from trials showing a reduction in lung cancer mortality. These trials provide sufficient evidence to show clinical efficacy, but further work is needed to prove deliverability in preparation for a national roll-out of the first major targeted screening programme. The UK has been world leading in addressing logistical issues with lung cancer screening through clinical trials, implementation pilots, and the National Health Service (NHS) England Targeted Lung Health Check Programme. In this Policy Review, we describe the consensus reached by a multiprofessional group of experts in lung cancer screening on the key requirements and priorities for effective implementation of a programme. We summarise the output from a round-table meeting of clinicians, behavioural scientists, stakeholder organisations, and representatives from NHS England, the UKNSC, and the four UK nations. This Policy Review will be an important tool in the ongoing expansion and evolution of an already successful programme, and provides a summary of UK expert opinion for consideration by those organising and delivering lung cancer screenings in other countries.
Collapse
Affiliation(s)
- Emma L O'Dowd
- Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Richard W Lee
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, UK; National Heart and Lung Institute, Imperial College London, London, UK.
| | - Ahsan R Akram
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK; Department of Respiratory Medicine, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Emily C Bartlett
- Royal Brompton and Harefield Hospitals London and National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Kate Brain
- Division of Population Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | | | - Yan Chen
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Anand Devaraj
- Royal Brompton and Harefield Hospitals London and National Heart and Lung Institute, Imperial College London, London, UK
| | - Sinan R Eccles
- Royal Glamorgan Hospital, Cwm Taf Morgannwg University Health Board, Llantrisant, UK
| | - John K Field
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Jesme Fox
- Roy Castle Lung Cancer Foundation, Liverpool, UK
| | - Seamus Grundy
- Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Sam M Janes
- Lungs for Living Research Centre, Department of Respiratory Medicine, University College London, London, UK
| | - Martin Ledson
- Department of Respiratory Medicine, Liverpool Heart and Chest Hospital, Liverpool, UK
| | | | | | - Kieran G McManus
- Department of Thoracic Surgery, Royal Victoria Hospital, Belfast, UK
| | - Rachael L Murray
- Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Arjun Nair
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Samantha L Quaife
- Centre for Prevention, Detection and Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Robert Rintoul
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Anne Stevenson
- Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Yvonne Summers
- The Christie Hospital NHS Trust, Manchester University NHS Foundation Trust, Manchester, UK
| | - Louise S Wilkinson
- Oxford Breast Imaging Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Richard Booton
- North West Lung Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | | | - Philip Crosbie
- North West Lung Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK; Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| |
Collapse
|
26
|
Trevellin E, Bettini S, Pilatone A, Vettor R, Milan G. Obesity, the Adipose Organ and Cancer in Humans: Association or Causation? Biomedicines 2023; 11:biomedicines11051319. [PMID: 37238992 DOI: 10.3390/biomedicines11051319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Epidemiological observations, experimental studies and clinical data show that obesity is associated with a higher risk of developing different types of cancer; however, proof of a cause-effect relationship that meets the causality criteria is still lacking. Several data suggest that the adipose organ could be the protagonist in this crosstalk. In particular, the adipose tissue (AT) alterations occurring in obesity parallel some tumour behaviours, such as their theoretically unlimited expandability, infiltration capacity, angiogenesis regulation, local and systemic inflammation and changes to the immunometabolism and secretome. Moreover, AT and cancer share similar morpho-functional units which regulate tissue expansion: the adiponiche and tumour-niche, respectively. Through direct and indirect interactions involving different cellular types and molecular mechanisms, the obesity-altered adiponiche contributes to cancer development, progression, metastasis and chemoresistance. Moreover, modifications to the gut microbiome and circadian rhythm disruption also play important roles. Clinical studies clearly demonstrate that weight loss is associated with a decreased risk of developing obesity-related cancers, matching the reverse-causality criteria and providing a causality correlation between the two variables. Here, we provide an overview of the methodological, epidemiological and pathophysiological aspects, with a special focus on clinical implications for cancer risk and prognosis and potential therapeutic interventions.
Collapse
Affiliation(s)
- Elisabetta Trevellin
- Center for the Study and Integrated Treatment of Obesity (CeSTIO), Internal Medicine 3, Department of Medicine, University Hospital of Padova, 35128 Padova, Italy
| | - Silvia Bettini
- Center for the Study and Integrated Treatment of Obesity (CeSTIO), Internal Medicine 3, Department of Medicine, University Hospital of Padova, 35128 Padova, Italy
| | - Anna Pilatone
- Center for the Study and Integrated Treatment of Obesity (CeSTIO), Internal Medicine 3, Department of Medicine, University Hospital of Padova, 35128 Padova, Italy
| | - Roberto Vettor
- Center for the Study and Integrated Treatment of Obesity (CeSTIO), Internal Medicine 3, Department of Medicine, University Hospital of Padova, 35128 Padova, Italy
| | - Gabriella Milan
- Center for the Study and Integrated Treatment of Obesity (CeSTIO), Internal Medicine 3, Department of Medicine, University Hospital of Padova, 35128 Padova, Italy
| |
Collapse
|
27
|
Mikhael PG, Wohlwend J, Yala A, Karstens L, Xiang J, Takigami AK, Bourgouin PP, Chan P, Mrah S, Amayri W, Juan YH, Yang CT, Wan YL, Lin G, Sequist LV, Fintelmann FJ, Barzilay R. Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. J Clin Oncol 2023; 41:2191-2200. [PMID: 36634294 PMCID: PMC10419602 DOI: 10.1200/jco.22.01345] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/10/2022] [Accepted: 11/29/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data. METHODS We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers). RESULTS Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively. CONCLUSION Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available. [Media: see text].
Collapse
Affiliation(s)
- Peter G. Mikhael
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Jeremy Wohlwend
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Adam Yala
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Ludvig Karstens
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Justin Xiang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Angelo K. Takigami
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Patrick P. Bourgouin
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - PuiYee Chan
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Sofiane Mrah
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Wael Amayri
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Yu-Hsiang Juan
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Cheng-Ta Yang
- Chang Gung University, Taoyuan, Taiwan
- Department of Thoracic Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Yung-Liang Wan
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Gigin Lin
- Chang Gung University, Taoyuan, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Lecia V. Sequist
- Harvard Medical School, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Florian J. Fintelmann
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Regina Barzilay
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| |
Collapse
|
28
|
Morgan H, Baldwin DR. Important parameters for cost-effective implementation of lung cancer screening. Br J Radiol 2023; 96:20220489. [PMID: 36607805 PMCID: PMC10161917 DOI: 10.1259/bjr.20220489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
It is now widely accepted that lung cancer screening through low-dose computed tomography (LDCT) results in fewer diagnoses at a late stage, and decreased lung cancer mortality. Whilst reducing deaths from lung cancer is an essential prerequisite, this must be balanced against the considerable economic costs accumulated in screening. Multiple health economic models have shown substantial variation in cost per Quality-Adjusted Life Year (QALY), partly driven by the healthcare costs in the country concerned and partly by other modifiable programme components. Recent modelling using UK costs and a targeted approach suggest that most scenarios are within the willingness to pay threshold for the UK. However, identifying the most clinically and cost-effective programme is a priority to minimise the total financial impact. Programme components that influence cost-effectiveness include the method of selection of the eligible population, the participation rate, the interval between rounds of screening, the method of pulmonary nodule management, and the approach to clinical work up. Future research will clarify if a personalised approach to screening, using baseline and subsequent risk to define screening intervals is more cost-effective. The burden of LDCT screening on the medical infrastructure and workforce has to be quantified and carefully managed during implementation.
Collapse
Affiliation(s)
- Helen Morgan
- Roy Castle Clinical Research Fellow, Division of Lifespan and Population Health, University of Nottingham, Nottingham, United Kingdom
| | | |
Collapse
|
29
|
Liao W, Coupland CAC, Burchardt J, Baldwin DR, Gleeson FV, Hippisley-Cox J. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models. THE LANCET RESPIRATORY MEDICINE 2023:S2213-2600(23)00050-4. [PMID: 37030308 DOI: 10.1016/s2213-2600(23)00050-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 04/08/2023]
Abstract
BACKGROUND Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. METHODS For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R2D]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP]v2, LLPv3, Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO]M2012, PLCOM2014, Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. FINDINGS There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R2D in both sexes in the QResearch validation cohort and 59% of the R2D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLPv2 and PLCOM2012), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk. INTERPRETATION The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme. FUNDING Innovate UK (UK Research and Innovation). TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
Collapse
Affiliation(s)
- Weiqi Liao
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Carol A C Coupland
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; School of Medicine, University of Nottingham, Nottingham, UK
| | - Judith Burchardt
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - David R Baldwin
- School of Medicine, University of Nottingham, Nottingham, UK; Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| |
Collapse
|
30
|
Jantzen R, Ezer N, Camilleri-Broët S, Tammemägi MC, Broët P. Evaluation of the accuracy of the PLCO m2012 6-year lung cancer risk prediction model among smokers in the CARTaGENE population-based cohort. CMAJ Open 2023; 11:E314-E322. [PMID: 37041013 PMCID: PMC10095260 DOI: 10.9778/cmajo.20210335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND The PLCOm2012 prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCOm2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies. METHODS We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCOm2012 calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCOm2012 detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec's pilot program (for people aged 55-74 yr and 50-74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively. RESULTS Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCOm2012 tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59-0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679-0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%-59.8%) to 44.9% (95% CI 37.4%-52.6%), specificities ranged from 81.6% (95% CI 80.8%-82.3%) to 87.7% (95% CI 87.0%-88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%-5.1%) to 5.3% (95% CI 4.2%-6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCOm2012 at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCOm2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%). INTERPRETATION In a cohort of Quebec smokers, the PLCOm2012 risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution.
Collapse
Affiliation(s)
- Rodolphe Jantzen
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| | - Nicole Ezer
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| | - Sophie Camilleri-Broët
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| | - Martin C Tammemägi
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| | - Philippe Broët
- CARTaGENE (Jantzen, Broët), Research Centre, CHU Sainte-Justine; Université de Montréal (Jantzen, Broët); Département de médecine sociale et préventive (Broët), École de santé publique de l'Université de Montréal, Université de Montréal; Departments of Medicine (Ezer) and of Pathology (Camilleri-Broët), McGill University, Montréal, Que.; Prevention and Cancer Control (Tammemägi), Ontario Health (Cancer Care Ontario), Toronto, Ont.; Department of Health Sciences (Tammemägi), Brock University, St. Catharines, Ont.; Department of Public Health (Broët), Faculty of Medicine; Centre de recherche en epidémiologie et santé des populations and INSERM (Broët), Université Paris-Saclay; Assistance Publique-Hôpitaux de Paris (Broët), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
| |
Collapse
|
31
|
Toumazis I, Cao P, de Nijs K, Bastani M, Munshi V, Hemmati M, Ten Haaf K, Jeon J, Tammemägi M, Gazelle GS, Feuer EJ, Kong CY, Meza R, de Koning HJ, Plevritis SK, Han SS. Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis. Ann Intern Med 2023; 176:320-332. [PMID: 36745885 PMCID: PMC11025620 DOI: 10.7326/m22-2216] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening. OBJECTIVE To evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds. DESIGN Comparative modeling analysis. DATA SOURCES National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator. TARGET POPULATION 1960 U.S. birth cohort. TIME HORIZON 45 years. PERSPECTIVE U.S. health care sector. INTERVENTION Annual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model. OUTCOME MEASURES Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost. RESULTS OF BASE-CASE ANALYSIS Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%). RESULTS OF SENSITIVITY ANALYSES Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions. LIMITATION Risk models were restricted to age, sex, and smoking-related risk predictors. CONCLUSION Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration. PRIMARY FUNDING SOURCE National Cancer Institute (NCI).
Collapse
Affiliation(s)
- Iakovos Toumazis
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.)
| | - Pianpian Cao
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.)
| | - Koen de Nijs
- Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.)
| | - Mehrad Bastani
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York (M.B.)
| | - Vidit Munshi
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.)
| | - Mehdi Hemmati
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.)
| | - Kevin Ten Haaf
- Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.)
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.)
| | - Martin Tammemägi
- Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada (M.T.)
| | - G Scott Gazelle
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.)
| | - Eric J Feuer
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland (E.J.F.)
| | - Chung Yin Kong
- Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, New York (C.Y.K.)
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, and Department of Integrative Oncology, BC Cancer Research Institute, British Columbia, Canada (R.M.)
| | - Harry J de Koning
- Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.)
| | - Sylvia K Plevritis
- Department of Biomedical Data Sciences, Stanford University, Stanford, California (S.K.P.)
| | - Summer S Han
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California (S.S.H.)
| |
Collapse
|
32
|
Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. Lancet 2023; 401:390-408. [PMID: 36563698 DOI: 10.1016/s0140-6736(22)01694-4] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/26/2022] [Accepted: 08/25/2022] [Indexed: 12/24/2022]
Abstract
Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.
Collapse
Affiliation(s)
- Scott J Adams
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Emily Stone
- Faculty of Medicine, University of New South Wales and Department of Lung Transplantation and Thoracic Medicine, St Vincent's Hospital, Sydney, NSW, Australia
| | - David R Baldwin
- Respiratory Medicine Unit, David Evans Research Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Pyng Lee
- Division of Respiratory and Critical Care Medicine, National University Hospital and National University of Singapore, Singapore
| | - Florian J Fintelmann
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| |
Collapse
|
33
|
Immediate, Remote Smoking Cessation Intervention in Participants Undergoing a Targeted Lung Health Check: Quit Smoking Lung Health Intervention Trial, a Randomized Controlled Trial. Chest 2023; 163:455-463. [PMID: 35932889 PMCID: PMC9899638 DOI: 10.1016/j.chest.2022.06.048] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/27/2022] [Accepted: 06/30/2022] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Lung cancer screening programs provide an opportunity to support people who smoke to quit, but the most appropriate model for delivery remains to be determined. Immediate face-to-face smoking cessation support for people undergoing screening can increase quit rates, but it is not known whether remote delivery of immediate smoking cessation counselling and pharmacotherapy in this context also is effective. RESEARCH QUESTION Does an immediate telephone smoking cessation intervention increase quit rates compared with usual care among a population enrolled in a targeted lung health check (TLHC)? STUDY DESIGN AND METHODS In a single-masked randomized controlled trial, people 55 to 75 years of age who smoke and attended a TLHC were allocated by day of attendance to receive either immediate telephone smoking cessation intervention (TSI) support (starting immediately and lasting for 6 weeks) with appropriate pharmacotherapy or usual care (UC; very brief advice to quit and signposting to smoking cessation services). The primary outcome was self-reported 7-day point prevalence smoking abstinence at 3 months. Differences between groups were assessed using logistic regression. RESULTS Three hundred fifteen people taking part in the screening program who reported current smoking with a mean ± SD age of 63 ± 5.4 years, 48% of whom were women, were randomized to TSI (n = 152) or UC (n = 163). The two groups were well matched at baseline. Self-reported quit rates were higher in the intervention arm, 21.1% vs 8.9% (OR, 2.83; 95% CI, 1.44-5.61; P = .002). Controlling for participant demographics, neither baseline smoking characteristics nor the discovery of abnormalities on low-dose CT imaging modified the effect of the intervention. INTERPRETATION Immediate provision of an intensive telephone-based smoking cessation intervention including pharmacotherapy, delivered within a targeted lung screening context, is associated with increased smoking abstinence at 3 months. TRIAL REGISTRY ISRCTN registry; No.: ISRCTN12455871; URL: www.IRSCN.com.
Collapse
|
34
|
Pan Z, Zhang R, Shen S, Lin Y, Zhang L, Wang X, Ye Q, Wang X, Chen J, Zhao Y, Christiani DC, Li Y, Chen F, Wei Y. OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations. EBioMedicine 2023; 88:104443. [PMID: 36701900 PMCID: PMC9881220 DOI: 10.1016/j.ebiom.2023.104443] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 12/27/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND A reliable risk prediction model is critically important for identifying individuals with high risk of developing lung cancer as candidates for low-dose chest computed tomography (LDCT) screening. Leveraging a cutting-edge machine learning technique that accommodates a wide list of questionnaire-based predictors, we sought to optimize and validate a lung cancer prediction model. METHODS We developed an Optimized early Warning model for Lung cancer risk (OWL) using the XGBoost algorithm with 323,344 participants from the England area in UK Biobank (training set), and independently validated it with 93,227 participants from UKB Scotland and Wales area (validation set 1), as well as 70,605 and 66,231 participants in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial (PLCO) control and intervention subpopulations, respectively (validation sets 2 & 3) and 23,138 and 18,669 participants in the United States National Lung Screening Trial (NLST) control and intervention subpopulations, respectively (validation sets 4 & 5). By comparing with three competitive prediction models, i.e., PLCO modified 2012 (PLCOm2012), PLCO modified 2014 (PLCOall2014), and the Liverpool Lung cancer Project risk model version 3 (LLPv3), we assessed the discrimination of OWL by the area under receiver operating characteristic curve (AUC) at the designed time point. We further evaluated the calibration using relative improvement in the ratio of expected to observed lung cancer cases (RIEO), and illustrated the clinical utility by the decision curve analysis. FINDINGS For general population, with validation set 1, OWL (AUC = 0.855, 95% CI: 0.829-0.880) presented a better discriminative capability than PLCOall2014 (AUC = 0.821, 95% CI: 0.794-0.848) (p < 0.001); with validation sets 2 & 3, AUC of OWL was comparable to PLCOall2014 (AUCPLCOall2014-AUCOWL < 1%). For ever-smokers, OWL outperformed PLCOm2012 and PLCOall2014 among ever-smokers in validation set 1 (AUCOWL = 0.842, 95% CI: 0.814-0.871; AUCPLCOm2012 = 0.792, 95% CI: 0.760-0.823; AUCPLCOall2014 = 0.791, 95% CI: 0.760-0.822, all p < 0.001). OWL remained comparable to PLCOm2012 and PLCOall2014 in discrimination (AUC difference from -0.014 to 0.008) among the ever-smokers in validation sets 2 to 5. In all the validation sets, OWL outperformed LLPv3 among the general population and the ever-smokers. Of note, OWL showed significantly better calibration than PLCOm2012, PLCOall2014 (RIEO from 43.1% to 92.3%, all p < 0.001), and LLPv3 (RIEO from 41.4% to 98.7%, all p < 0.001) in most cases. For clinical utility, OWL exhibited significant improvement in average net benefits (NB) over PLCOall2014 in validation set 1 (NB improvement: 32, p < 0.001); among ever smokers of validation set 1, OWL (average NB = 289) retained significant improvement over PLCOm2012 (average NB = 213) (p < 0.001). OWL had equivalent NBs with PLCOm2012 and PLCOall2014 in PLCO and NLST populations, while outperforming LLPv3 in the three populations. INTERPRETATION OWL, with a high degree of predictive accuracy and robustness, is a general framework with scientific justifications and clinical utility that can aid in screening individuals with high risks of lung cancer. FUNDING National Natural Science Foundation of China, the US NIH.
Collapse
Affiliation(s)
- Zoucheng Pan
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Ruyang Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Sipeng Shen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yunzhi Lin
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Longyao Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xiang Wang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Qian Ye
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xuan Wang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jiajin Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yang Zhao
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - David C. Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA,Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Yi Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Feng Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Yongyue Wei
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Xueyuan Road, Haidian District, Beijing 100191, China.
| |
Collapse
|
35
|
[China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2023 Version)]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2023; 26:1-9. [PMID: 36792074 PMCID: PMC9987116 DOI: 10.3779/j.issn.1009-3419.2023.102.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Lung cancer is the leading cause of cancer-related death in China. The effectiveness of low-dose computed tomography (LDCT) screening has been further validated in recent years, and significant progress has been made in research on identifying high-risk individuals, personalizing screening interval, and management of screen-detected findings. The aim of this study is to revise China national lung cancer screening guideline with LDCT (2018 version). The China Lung Cancer Early Detection and Treatment Expert Group (CLCEDTEG) designated by the China's National Health Commission, and China Lung Oncolgy Group experts, jointly participated in the revision of Chinese lung cancer screening guideline (2023 version). This revision is based on the recent advances in LDCT lung cancer screening at home and abroad, and the epidemiology of lung cancer in China. The following aspects of the guideline were revised: (1) lung cancer risk factors besides smoking were considered for the identification of high risk population; (2) LDCT scan parameters were further classified; (3) longer screening interval is recommended for individuals who had negative LDCT screening results for two consecutive rounds; (4) the follow-up interval for positive nodules was extended from 3 months to 6 months; (5) the role of multi-disciplinary treatment (MDT) in the management of positive nodules, diagnosis and treatment of lung cancer were emphasized. This revision clarifies the screening, intervention and treatment pathways, making the LDCT screening guideline more appropriate for China. Future researches based on emerging technologies, including biomarkers and artificial intelligence, are needed to optimize LDCT screening in China in the future.
.
Collapse
|
36
|
Keller MS, Qureshi N, Albertson E, Pevnick J, Brandt N, Bui A, Sarkisian CA. Comparing risk prediction models aimed at predicting hospitalizations for adverse drug events in community dwelling older adults: a protocol paper. RESEARCH SQUARE 2023:rs.3.rs-2429369. [PMID: 36711695 PMCID: PMC9882666 DOI: 10.21203/rs.3.rs-2429369/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background The objective of this paper is to describe the creation, validation, and comparison of two risk prediction modeling approaches for community-dwelling older adults to identify individuals at highest risk for adverse drug event-related hospitalizations. One approach will use traditional statistical methods, the second will use a machine learning approach. Methods We will construct medication, clinical, health care utilization, and other variables known to be associated with adverse drug event-related hospitalizations. To create the cohort, we will include older adults (≥ 65 years of age) empaneled to a primary care physician within the Cedars-Sinai Health System primary care clinics with polypharmacy (≥ 5 medications) or at least 1 medication commonly implicated in ADEs (certain oral hypoglycemics, anti-coagulants, anti-platelets, and insulins). We will use a Fine-Gray Cox proportional hazards model for one risk modeling approach and DataRobot, a data science and analytics platform, to run and compare several widely used supervised machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), Decision Tree, Naïve Bayes, and K-Nearest Neighbors. We will use a variety of metrics to compare model performance and to assess the risk of algorithmic bias. Discussion In conclusion, we hope to develop a pragmatic model that can be implemented in the primary care setting to risk stratify older adults to further optimize medication management.
Collapse
Affiliation(s)
| | | | | | | | | | - Alex Bui
- David Geffen School of Medicine: University of California Los Angeles David Geffen School of Medicine
| | - Catherine A Sarkisian
- David Geffen School of Medicine: University of California Los Angeles David Geffen School of Medicine
| |
Collapse
|
37
|
James BA, Williams JL, Nemesure B. A systematic review of genetic ancestry as a risk factor for incidence of non-small cell lung cancer in the US. Front Genet 2023; 14:1141058. [PMID: 37082203 PMCID: PMC10110850 DOI: 10.3389/fgene.2023.1141058] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/14/2023] [Indexed: 04/22/2023] Open
Abstract
Background: Non-Small Cell Lung Cancer (NSCLC), the leading cause of cancer-related death in the United States, is the most diagnosed form of lung cancer. While lung cancer incidence has steadily declined over the last decade, disparities in incidence and mortality rates persist among African American (AA), Caucasian American (CA), and Hispanic American (HA) populations. Researchers continue to explore how genetic ancestry may influence differential outcomes in lung cancer risk and development. The purpose of this evaluation is to highlight experimental research that investigates the differential impact of genetic mutations and ancestry on NSCLC incidence. Methods: This systematic review was conducted using PubMed and Google Scholar search engines. The following key search terms were used to select articles published between 2011 and 2022: "African/European/Latin American Ancestry NSCLC"; "Racial Disparities NSCLC"; "Genetic Mutations NSCLC"; "NSCLC Biomarkers"; "African Americans/Hispanic Americans/Caucasian Americans NSCLC incidence." Systematic reviews, meta-analyses, and studies outside of the US were excluded. A total of 195 articles were initially identified and after excluding 156 which did not meet eligibility criteria, 38 were included in this investigation. Results: Studies included in this analysis focused on racial/ethnic disparities in the following common genetic mutations observed in NSCLC: KRAS, EGFR, TP53, PIK3CA, ALK Translocations, ROS-1 Rearrangements, STK11, MET, and BRAF. Results across studies varied with respect to absolute differential expression. No significant differences in frequencies of specific genetic mutational profiles were noted between racial/ethnic groups. However, for HAs, lower mutational frequencies in KRAS and STK11 genes were observed. In genetic ancestry level analyses, multiple studies suggest that African ancestry is associated with a higher frequency of EGFR mutations. Conversely, Latin ancestry is associated with TP53 mutations. At the genomic level, several novel predisposing variants associated with African ancestry and increased risk of NSCLC were discovered. Family history among all racial/ethnic groups was also considered a risk factor for NSCLC. Conclusion: Results from racially and ethnically diverse studies can elucidate driving factors that may increase susceptibility and subsequent lung cancer risk across different racial/ethnic groups. Identification of biomarkers that can be used as diagnostic, prognostic, and therapeutic tools may help improve lung cancer survival among high-risk populations.
Collapse
|
38
|
Development and Validation of the Random Forest Model via Combining CT-PET Image Features and Demographic Data for Distant Metastases among Lung Cancer Patients. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7793533. [PMID: 36561373 PMCID: PMC9767733 DOI: 10.1155/2022/7793533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 11/18/2022] [Accepted: 11/26/2022] [Indexed: 12/15/2022]
Abstract
The work aimed at developing and validating a random forest model of CT-PET image features combined with demographic data to diagnose distant metastases among lung cancer patients. This study involved lung cancer patients from The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset, the lung PET-CT dataset, the lung squamous cell carcinoma (LSCC) dataset, and the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma (CPTAC-LUAD) dataset and collected the information on 178 CT, 178 PET, and the patients' age, history of smoking, and gender. We conducted image processing and feature extraction. Finally, 4 computed tomography (CT) image features and 2 positron emission tomography (PET) image features were extracted. Four prediction models based on CT image features, PET image features, and demographic data were developed, and the area under the receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. A total of 178 eligible samples were randomly divided into a training set (n = 134) and a testing set (n = 44) at a ratio of 3 : 1, with 2021 as a random number. ROC analyses illustrated that the predictive performance for distant metastases of combining CT-PET image features and demographic data for training and testing were 0.923 (95% confidence interval (CI): 0.873-0.973) and 0.873 (95% CI: 0.757-0.990). In addition, the predictive performance of the combined model in the testing set was significantly better than that of the CT-demographic data model (0.716, 95% CI: 0.531-0.902), PET-demographic data model (0.802, 95% CI: 0.633-0.970), and CT-PET model (0.797, 95% CI: 0.666-0.928). The random forest model via combining CT-PET image features and demographic data could have great performance in predicting distant metastases among lung cancer patients.
Collapse
|
39
|
Chien LH, Chen TY, Chen CH, Chen KY, Hsiao CF, Chang GC, Tsai YH, Su WC, Huang MS, Chen YM, Chen CY, Liang SK, Chen CY, Wang CL, Hung HH, Jiang HF, Hu JW, Rothman N, Lan Q, Liu TW, Chen CJ, Yang PC, Chang IS, Hsiung CA. Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan. Cancer Epidemiol Biomarkers Prev 2022; 31:2208-2218. [PMID: 36129788 PMCID: PMC9720426 DOI: 10.1158/1055-9965.epi-22-0281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/20/2022] [Accepted: 09/20/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Methods synthesizing multiple data sources without prospective datasets have been proposed for absolute risk model development. This study proposed methods for adapting risk models for another population without prospective cohorts, which would help alleviate the health disparities caused by advances in absolute risk models. To exemplify, we adapted the lung cancer risk model PLCOM2012, well studied in the west, for Taiwan. METHODS Using Taiwanese multiple data sources, we formed an age-matched case-control study of ever-smokers (AMCCSE), estimated the number of ever-smoking lung cancer patients in 2011-2016 (NESLP2011), and synthesized a dataset resembling the population of cancer-free ever-smokers in 2010 regarding the PLCOM2012 risk factors (SPES2010). The AMCCSE was used to estimate the overall calibration slope, and the requirement that NESLP2011 equals the estimated total risk of individuals in SPES2010 was used to handle the calibration-in-the-large problem. RESULTS The adapted model PLCOT-1 (PLCOT-2) had an AUC of 0.78 (0.75). They had high performance in calibration and clinical usefulness on subgroups of SPES2010 defined by age and smoking experience. Selecting the same number of individuals for low-dose computed tomography screening using PLCOT-1 (PLCOT-2) would have identified approximately 6% (8%) more lung cancers than the US Preventive Services Task Forces 2021 criteria. Smokers having 40+ pack-years had an average PLCOT-1 (PLCOT-2) risk of 3.8% (2.6%). CONCLUSIONS The adapted PLCOT models had high predictive performance. IMPACT The PLCOT models could be used to design lung cancer screening programs in Taiwan. The methods could be applicable to other cancer models.
Collapse
Affiliation(s)
- Li-Hsin Chien
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Tzu-Yu Chen
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Chung-Hsing Chen
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Kuan-Yu Chen
- Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chin-Fu Hsiao
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.,Taiwan Lung Cancer Tissue/Specimen Information Resource Center, National Health Research Institutes, Zhunan, Taiwan
| | - Gee-Chen Chang
- School of Medicine and Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.,Institute of Biomedical Sciences, National Chung Hsing University, Taichung, Taiwan.,Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ying-Huang Tsai
- Department of Respiratory Therapy, Chang Gung University, Taoyuan, Taiwan.,Department of Pulmonary and Critical Care, Xiamen Chang Gung Hospital, Xiamen, China
| | - Wu-Chou Su
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ming-Shyan Huang
- Department of Internal Medicine, E-Da Cancer Hospital, School of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chih-Yi Chen
- Institute of Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.,Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Sheng-Kai Liang
- Department of Internal Medicine, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan.,Department of Medicine, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Chung-Yu Chen
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan
| | - Chih-Liang Wang
- Department of Pulmonary and Critical Care, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Hsiao-Han Hung
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Hsin-Fang Jiang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Jia-Wei Hu
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Tsang-Wu Liu
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Chien-Jen Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Pan-Chyr Yang
- Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan.,Corresponding Authors: Chao A. Hsiung, 35 Keyan Road, Zhunan, Miaoli County 35053, Taiwan. Phone: 372-06166, ext. 36120; Fax: 375-86467; E-mail: ; and I-Shou Chang, 35 Keyan Road, Zhunan, Miaoli County 35053, Taiwan. Phone: 372-06166, ext. 36130; E-mail:
| | - Chao A. Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.,Corresponding Authors: Chao A. Hsiung, 35 Keyan Road, Zhunan, Miaoli County 35053, Taiwan. Phone: 372-06166, ext. 36120; Fax: 375-86467; E-mail: ; and I-Shou Chang, 35 Keyan Road, Zhunan, Miaoli County 35053, Taiwan. Phone: 372-06166, ext. 36130; E-mail:
| |
Collapse
|
40
|
Bhardwaj M, Schöttker B, Holleczek B, Brenner H. Comparison of discrimination performance of 11 lung cancer risk models for predicting lung cancer in a prospective cohort of screening-age adults from Germany followed over 17 years. Lung Cancer 2022; 174:83-90. [PMID: 36356492 DOI: 10.1016/j.lungcan.2022.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/02/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
Abstract
Randomized trials have demonstrated considerable reduction in lung cancer (LC) mortality by screening pre-selected heavy smokers with low-dose computed tomography (LDCT). Newer screening guidelines recommend refined LC risk models for selecting the target population for screening. We aimed to evaluate and compare the discrimination performance of LC risk models and previously used trial criteria in predicting LC incidence and mortality in a large German cohort of screening-age adults. Within ESTHER, a population-based prospective cohort study conducted in Saarland, Germany, 4812 ever smokers aged 50-75 years were followed up with respect to LC incidence and mortality for up to 17 years. We quantified the performance of 11 different LC risk models by the area under the curve (AUC) and compared the proportion of correctly predicted LC cases between the best performing models and the LDCT trial criteria. Risk prediction of LC incidence in the ESTHER ever smokers was best for the Bach model, LCRAT and LCDRAT with AUCs ranging from 0.782 to 0.787, from 0.770 to 0.774, and from 0.765 to 0.771 for the follow-up time periods of cases identified at 6, 11, and 17 years, respectively. At cutoffs yielding comparable positivity rates as the LDCT trial criteria, these models would have identified between 11.8 (95% CI 3.0-20.5) and 17.6 (95% CI 10.1-25.2) percent units higher proportions of LC cases occurring during the initial 6 years of follow-up. Use of LC risk models is expected to result in substantially greater potential to identify people at highest risk of LC, suggesting enhanced potential for reducing LC mortality by LC screening.
Collapse
Affiliation(s)
- Megha Bhardwaj
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Network Aging Research, University of Heidelberg, Bergheimer Strasse 20, 69115 Heidelberg, Germany
| | - Bernd Holleczek
- Saarland Cancer Registry, Präsident-Baltz-Strasse 5, 66119 Saarbrücken, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| |
Collapse
|
41
|
Non-Invasive Biomarkers for Early Lung Cancer Detection. Cancers (Basel) 2022; 14:cancers14235782. [PMID: 36497263 PMCID: PMC9739091 DOI: 10.3390/cancers14235782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 11/27/2022] Open
Abstract
Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.
Collapse
|
42
|
Fabbro M, Hahn K, Novaes O, Ó'Grálaigh M, O'Mahony JF. Cost-Effectiveness Analyses of Lung Cancer Screening Using Low-Dose Computed Tomography: A Systematic Review Assessing Strategy Comparison and Risk Stratification. PHARMACOECONOMICS - OPEN 2022; 6:773-786. [PMID: 36040557 PMCID: PMC9596656 DOI: 10.1007/s41669-022-00346-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Our first study objective was to assess the range of lung cancer screening intervals compared within cost-effectiveness analyses (CEAs) of low-dose computed tomography (LDCT) and to examine the implications for the strategies identified as optimally cost effective; the second objective was to examine if and how risk subgroup-specific policies were considered. METHODS PubMed, Embase and Web of Science were searched for model-based CEAs of LDCT lung screening. The retrieved studies were assessed to examine if the analyses considered sufficient strategy variation to permit incremental estimation of cost effectiveness. Regarding risk selection, we examined if analyses considered alternative risk strata in separate analyses or as alternative risk-based eligibility criteria for screening. RESULTS The search identified 33 eligible CEAs, 23 of which only considered one screening frequency. Of the 10 analyses considering multiple screening intervals, only 4 included intervals longer than 2 years. Within the 10 studies considering multiple intervals, the optimal policy choice would differ in 5 if biennial intervals or longer had not been considered. Nineteen studies conducted risk subgroup analyses, 12 of which assumed that subgroup-specific policies were possible and 7 of which assumed that a common screening policy applies to all those screened. CONCLUSIONS The comparison of multiple strategies is recognised as good practice in CEA when seeking optimal policies. Studies that do include multiple intervals indicate that screening intervals longer than 1 year can be relevant. The omission of intervals of 2 years or longer from CEAs of LDCT screening could lead to the adoption of sub-optimal policies. There also is scope for greater consideration of risk-stratified policies which tailor screening intensity to estimated disease risk. Policy makers should take care when interpreting current evidence before implementing lung screening.
Collapse
Affiliation(s)
- Matthew Fabbro
- School of Medicine, Trinity College Dublin, 2-4 Foster Place, Dublin, Ireland
| | - Kirah Hahn
- School of Medicine, Trinity College Dublin, 2-4 Foster Place, Dublin, Ireland
| | - Olivia Novaes
- School of Medicine, Trinity College Dublin, 2-4 Foster Place, Dublin, Ireland
| | - Mícheál Ó'Grálaigh
- School of Medicine, Trinity College Dublin, 2-4 Foster Place, Dublin, Ireland
| | - James F O'Mahony
- School of Medicine, Trinity College Dublin, 2-4 Foster Place, Dublin, Ireland.
| |
Collapse
|
43
|
Mighton C, Shickh S, Aguda V, Krishnapillai S, Adi-Wauran E, Bombard Y. From the patient to the population: Use of genomics for population screening. Front Genet 2022; 13:893832. [PMID: 36353115 PMCID: PMC9637971 DOI: 10.3389/fgene.2022.893832] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 09/26/2022] [Indexed: 10/22/2023] Open
Abstract
Genomic medicine is expanding from a focus on diagnosis at the patient level to prevention at the population level given the ongoing under-ascertainment of high-risk and actionable genetic conditions using current strategies, particularly hereditary breast and ovarian cancer (HBOC), Lynch Syndrome (LS) and familial hypercholesterolemia (FH). The availability of large-scale next-generation sequencing strategies and preventive options for these conditions makes it increasingly feasible to screen pre-symptomatic individuals through public health-based approaches, rather than restricting testing to high-risk groups. This raises anew, and with urgency, questions about the limits of screening as well as the moral authority and capacity to screen for genetic conditions at a population level. We aimed to answer some of these critical questions by using the WHO Wilson and Jungner criteria to guide a synthesis of current evidence on population genomic screening for HBOC, LS, and FH.
Collapse
Affiliation(s)
- Chloe Mighton
- Genomics Health Services Research Program, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Salma Shickh
- Genomics Health Services Research Program, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Vernie Aguda
- Genomics Health Services Research Program, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada
- Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Suvetha Krishnapillai
- Genomics Health Services Research Program, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Ella Adi-Wauran
- Genomics Health Services Research Program, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Yvonne Bombard
- Genomics Health Services Research Program, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
44
|
Racovita M, Wheeler E, Wait S, Albreht T, Baird AM, Jassem J, McNamara A, Novello S, Radu-Loghin C, van Meerbeeck JP. The need for a comprehensive and integrated approach to lung cancer policy in Europe. Eur J Cancer 2022; 175:54-59. [PMID: 36088672 DOI: 10.1016/j.ejca.2022.08.001] [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: 06/24/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/03/2022]
Abstract
Lung cancer is the leading cause of cancer-related deaths in Europe. Europe's Beating Cancer Plan calls for a comprehensive approach to the disease in general but not specifically to lung cancer. Such a comprehensive approach, integrating efforts to strengthen anti-tobacco policies, early detection and underlying models of care, is sorely needed for lung cancer - particularly considering disruptions to care during the COVID-19 pandemic. In a recently published think piece, a multidisciplinary group of experts proposed four key policy priority areas. First, to reduce stigma and improve awareness of potential symptoms, there is a need to foster a better understanding of lung cancer - among the public and healthcare professionals. Second, opportunities for early detection should be enhanced, and the implementation of targeted screening through low-dose computed tomography should be encouraged as a complement to smoking cessation services. This complementarity should be recognised and built into joint policy proposals, with development and better integration of screening and smoking cessation programmes on the ground. Third, the socioeconomic inequalities underpinning disparities in outcomes in people with lung cancer must be addressed, with targeted approaches to overcome barriers to access Finally, the overall quality of lung cancer care must be improved, making multidisciplinary care available to all and ensuring survivorship is given due attention.
Collapse
Affiliation(s)
- Monica Racovita
- The Health Policy Partnerships, 68-69 St Martin's Lane, London WC2N 4JS, United Kingdom.
| | - Eleanor Wheeler
- The Health Policy Partnerships, 68-69 St Martin's Lane, London WC2N 4JS, United Kingdom
| | - Suzanne Wait
- The Health Policy Partnerships, 68-69 St Martin's Lane, London WC2N 4JS, United Kingdom
| | - Tit Albreht
- University of Ljubljana, Faculty of Medicine, Vrazov Trg 2, 1000 Ljubljana, Slovenia
| | - Anne-Marie Baird
- Trinity College Dublin, The University of Dublin, College Green Dublin 2, Ireland.
| | - Jacek Jassem
- Medical University of Gdańsk, Mariana Smoluchowskiego 17, 80-214 Gdańsk, Poland
| | - Aoife McNamara
- Irish Cancer Society, 43 - 45 Northumberland Road, Dublin 4, Ireland.
| | - Silvia Novello
- University of Turin, Department of Oncology, AOU San Luigi, Via Verdi, 8 - 10124 Turin, Italy
| | - Cornel Radu-Loghin
- European Network for Smoking and Tobacco Prevention, Chaussée D'Ixelles 144, B-1050 Brussels, Belgium
| | - Jan P van Meerbeeck
- Department of Pulmonology & Thoracic Oncology, Antwerp University Hospital, Drie Eikenstraat 655, 2650 Edegem, Belgium
| |
Collapse
|
45
|
Wahla AS, Zoumot Z, Uzbeck M, Mallat J, Souilamas R, Shafiq I. The Journey for Lung Cancer Screening where we Stand Today. Open Respir Med J 2022; 16:e187430642207060. [PMID: 37273952 PMCID: PMC10156027 DOI: 10.2174/18743064-v16-e2207060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/21/2022] [Accepted: 04/19/2022] [Indexed: 11/22/2022] Open
Abstract
Background Lung cancer remains a leading cause of cancer mortality worldwide with many patients presenting with advanced disease. Objective We reviewed the available literature for lung cancer screening using low dose computed tomography (LDCT). We reviewed the National Lung Screening Trial (NLST), Early Lung Cancer Action Program (ELCAP) and the (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trials. We also look at different lung cancer risk prediction models that may aid in identifying target populations and also discuss the cost-effectiveness of LDCT screening in different groups of smokers and ex-smokers. Lastly, we discuss recent guideline changes that have occurred in line with new and emerging evidence on lung cancer screening. Conclusion LDCT has been shown reduce lung cancer mortality in certain groups of current and former smokers and should be considered to help in the early diagnosis of lung cancer.
Collapse
Affiliation(s)
- Ali S. Wahla
- Respiratory and Critical Care Institute, Cleveland Clinic, Dubai Abu Dhabi
| | - Zaid Zoumot
- Respiratory and Critical Care Institute, Cleveland Clinic, Dubai Abu Dhabi
| | - Mateen Uzbeck
- Respiratory and Critical Care Institute, Cleveland Clinic, Dubai Abu Dhabi
| | - Jihad Mallat
- Respiratory and Critical Care Institute, Cleveland Clinic, Dubai Abu Dhabi
| | - Redha Souilamas
- Respiratory and Critical Care Institute, Cleveland Clinic, Dubai Abu Dhabi
| | - Irfan Shafiq
- Respiratory and Critical Care Institute, Cleveland Clinic, Dubai Abu Dhabi
| |
Collapse
|
46
|
Afrose S, Song W, Nemeroff CB, Lu C, Yao D. Subpopulation-specific machine learning prognosis for underrepresented patients with double prioritized bias correction. COMMUNICATIONS MEDICINE 2022; 2:111. [PMID: 36059892 PMCID: PMC9436942 DOI: 10.1038/s43856-022-00165-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 07/27/2022] [Indexed: 11/09/2022] Open
Abstract
Abstract
Background
Many clinical datasets are intrinsically imbalanced, dominated by overwhelming majority groups. Off-the-shelf machine learning models that optimize the prognosis of majority patient types (e.g., healthy class) may cause substantial errors on the minority prediction class (e.g., disease class) and demographic subgroups (e.g., Black or young patients). In the typical one-machine-learning-model-fits-all paradigm, racial and age disparities are likely to exist, but unreported. In addition, some widely used whole-population metrics give misleading results.
Methods
We design a double prioritized (DP) bias correction technique to mitigate representational biases in machine learning-based prognosis. Our method trains customized machine learning models for specific ethnicity or age groups, a substantial departure from the one-model-predicts-all convention. We compare with other sampling and reweighting techniques in mortality and cancer survivability prediction tasks.
Results
We first provide empirical evidence showing various prediction deficiencies in a typical machine learning setting without bias correction. For example, missed death cases are 3.14 times higher than missed survival cases for mortality prediction. Then, we show DP consistently boosts the minority class recall for underrepresented groups, by up to 38.0%. DP also reduces relative disparities across race and age groups, e.g., up to 88.0% better than the 8 existing sampling solutions in terms of the relative disparity of minority class recall. Cross-race and cross-age-group evaluation also suggests the need for subpopulation-specific machine learning models.
Conclusions
Biases exist in the widely accepted one-machine-learning-model-fits-all-population approach. We invent a bias correction method that produces specialized machine learning prognostication models for underrepresented racial and age groups. This technique may reduce potentially life-threatening prediction mistakes for minority populations.
Collapse
|
47
|
Liu F, Dai L, Wang Y, Liu M, Wang M, Zhou Z, Qi Y, Chen R, OuYang S, Fan Q. Derivation and validation of a prediction model for patients with lung nodules malignancy regardless of mediastinal/hilar lymphadenopathy. J Surg Oncol 2022; 126:1551-1559. [PMID: 35993806 DOI: 10.1002/jso.27072] [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: 03/05/2022] [Revised: 06/15/2022] [Accepted: 08/12/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included. METHODS A single-center retrospective study was conducted. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit test was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn. RESULTS There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The aera under the curve (AUC) of the validation set was 0.91 (95% confidence interval [CI]: 0.85-0.98). In the validation set with mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI: 0.90-0.99). The goodness-of-fit test was 0.22. CONCLUSIONS We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy in clinical practice.
Collapse
Affiliation(s)
- Fenghui Liu
- Department of Respiratory and Sleep Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Liping Dai
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Yulin Wang
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Man Liu
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Meng Wang
- Department of Imaging and Nuclear Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhigang Zhou
- Department of Imaging and Nuclear Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Yu Qi
- Department of Thoracic Surgery in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Ruiying Chen
- Department of Respiratory and Sleep Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Songyun OuYang
- Department of Respiratory and Sleep Medicine in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, Henan, China
| | - Qingxia Fan
- Department of Oncology in the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| |
Collapse
|
48
|
Yu H, Raut JR, Bhardwaj M, Zhang Y, Sandner E, Schöttker B, Holleczek B, Schrotz-King P, Brenner H. A serum microRNA signature for enhanced selection of people for lung cancer screening. CANCER COMMUNICATIONS (LONDON, ENGLAND) 2022; 42:1222-1225. [PMID: 35929101 PMCID: PMC9648391 DOI: 10.1002/cac2.12346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/18/2022] [Accepted: 07/26/2022] [Indexed: 11/26/2022]
Affiliation(s)
- Haixin Yu
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany.,Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, 69120, Baden-Württemberg, Germany
| | - Janhavi R Raut
- Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, 69120, Baden-Württemberg, Germany.,Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, 69120, Baden-Württemberg, Germany
| | - Megha Bhardwaj
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany
| | - Yan Zhang
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany
| | - Evelin Sandner
- Pneumology and Thoracic Oncology, Robert-Bosch Krankenhaus, Klinik Schillerhoehe, Gerlingen, 70839, Baden-Württemberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany.,Network Aging Research, University of Heidelberg, Bergheimer Straße 20, Heidelberg, 69115, Baden-Württemberg, Germany
| | - Bernd Holleczek
- Saarland Cancer Registry, Präsident-Baltz-Straße 5, Saarbrucken, 66119, Saarland, Germany
| | - Petra Schrotz-King
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, 69120, Baden-Württemberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany.,Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, 69120, Baden-Württemberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, 69120, Baden-Württemberg, Germany
| |
Collapse
|
49
|
Bonney A, Malouf R, Marchal C, Manners D, Fong KM, Marshall HM, Irving LB, Manser R. Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality. Cochrane Database Syst Rev 2022; 8:CD013829. [PMID: 35921047 PMCID: PMC9347663 DOI: 10.1002/14651858.cd013829.pub2] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Lung cancer is the most common cause of cancer-related death in the world, however lung cancer screening has not been implemented in most countries at a population level. A previous Cochrane Review found limited evidence for the effectiveness of lung cancer screening with chest radiography (CXR) or sputum cytology in reducing lung cancer-related mortality, however there has been increasing evidence supporting screening with low-dose computed tomography (LDCT). OBJECTIVES: To determine whether screening for lung cancer using LDCT of the chest reduces lung cancer-related mortality and to evaluate the possible harms of LDCT screening. SEARCH METHODS We performed the search in collaboration with the Information Specialist of the Cochrane Lung Cancer Group and included the Cochrane Lung Cancer Group Trial Register, Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, current issue), MEDLINE (accessed via PubMed) and Embase in our search. We also searched the clinical trial registries to identify unpublished and ongoing trials. We did not impose any restriction on language of publication. The search was performed up to 31 July 2021. SELECTION CRITERIA: Randomised controlled trials (RCTs) of lung cancer screening using LDCT and reporting mortality or harm outcomes. DATA COLLECTION AND ANALYSIS: Two review authors were involved in independently assessing trials for eligibility, extraction of trial data and characteristics, and assessing risk of bias of the included trials using the Cochrane RoB 1 tool. We assessed the certainty of evidence using GRADE. Primary outcomes were lung cancer-related mortality and harms of screening. We performed a meta-analysis, where appropriate, for all outcomes using a random-effects model. We only included trials in the analysis of mortality outcomes if they had at least 5 years of follow-up. We reported risk ratios (RRs) and hazard ratios (HRs), with 95% confidence intervals (CIs) and used the I2 statistic to investigate heterogeneity. MAIN RESULTS: We included 11 trials in this review with a total of 94,445 participants. Trials were conducted in Europe and the USA in people aged 40 years or older, with most trials having an entry requirement of ≥ 20 pack-year smoking history (e.g. 1 pack of cigarettes/day for 20 years or 2 packs/day for 10 years etc.). One trial included male participants only. Eight trials were phase three RCTs, with two feasibility RCTs and one pilot RCT. Seven of the included trials had no screening as a comparison, and four trials had CXR screening as a comparator. Screening frequency included annual, biennial and incrementing intervals. The duration of screening ranged from 1 year to 10 years. Mortality follow-up was from 5 years to approximately 12 years. None of the included trials were at low risk of bias across all domains. The certainty of evidence was moderate to low across different outcomes, as assessed by GRADE. In the meta-analysis of trials assessing lung cancer-related mortality, we included eight trials (91,122 participants), and there was a reduction in mortality of 21% with LDCT screening compared to control groups of no screening or CXR screening (RR 0.79, 95% CI 0.72 to 0.87; 8 trials, 91,122 participants; moderate-certainty evidence). There were probably no differences in subgroups for analyses by control type, sex, geographical region, and nodule management algorithm. Females appeared to have a larger lung cancer-related mortality benefit compared to males with LDCT screening. There was also a reduction in all-cause mortality (including lung cancer-related) of 5% (RR 0.95, 95% CI 0.91 to 0.99; 8 trials, 91,107 participants; moderate-certainty evidence). Invasive tests occurred more frequently in the LDCT group (RR 2.60, 95% CI 2.41 to 2.80; 3 trials, 60,003 participants; moderate-certainty evidence). However, analysis of 60-day postoperative mortality was not significant between groups (RR 0.68, 95% CI 0.24 to 1.94; 2 trials, 409 participants; moderate-certainty evidence). False-positive results and recall rates were higher with LDCT screening compared to screening with CXR, however there was low-certainty evidence in the meta-analyses due to heterogeneity and risk of bias concerns. Estimated overdiagnosis with LDCT screening was 18%, however the 95% CI was 0 to 36% (risk difference (RD) 0.18, 95% CI -0.00 to 0.36; 5 trials, 28,656 participants; low-certainty evidence). Four trials compared different aspects of health-related quality of life (HRQoL) using various measures. Anxiety was pooled from three trials, with participants in LDCT screening reporting lower anxiety scores than in the control group (standardised mean difference (SMD) -0.43, 95% CI -0.59 to -0.27; 3 trials, 8153 participants; low-certainty evidence). There were insufficient data to comment on the impact of LDCT screening on smoking behaviour. AUTHORS' CONCLUSIONS: The current evidence supports a reduction in lung cancer-related mortality with the use of LDCT for lung cancer screening in high-risk populations (those over the age of 40 with a significant smoking exposure). However, there are limited data on harms and further trials are required to determine participant selection and optimal frequency and duration of screening, with potential for significant overdiagnosis of lung cancer. Trials are ongoing for lung cancer screening in non-smokers.
Collapse
Affiliation(s)
- Asha Bonney
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
- Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Reem Malouf
- National Perinatal Epidemiology Unit (NPEU), University of Oxford, Oxford, UK
| | | | - David Manners
- Respiratory Medicine, Midland St John of God Public and Private Hospital, Midland, Australia
| | - Kwun M Fong
- Thoracic Medicine Program, The Prince Charles Hospital, Brisbane, Australia
- UQ Thoracic Research Centre, School of Medicine, The University of Queensland, Brisbane, Australia
| | - Henry M Marshall
- School of Medicine, The University of Queensland, Brisbane, Australia
| | - Louis B Irving
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
| | - Renée Manser
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkville, Australia
- Department of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
| |
Collapse
|
50
|
Huo B, Manos D, Xu Z, Matheson K, Chun S, Fris J, Wallace AMR, French DG. Screening Criteria Evaluation for Expansion in Pulmonary Neoplasias (SCREEN). Semin Thorac Cardiovasc Surg 2022; 35:769-780. [PMID: 35878739 DOI: 10.1053/j.semtcvs.2022.06.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 06/06/2022] [Indexed: 12/14/2022]
Abstract
The SCREEN study investigated screening eligibility and survival outcomes between heavy smokers and light-or-never-smokers with lung cancer to determine whether expanded risk factor analysis is needed to refine screening criteria. SCREEN is a retrospective study of 917 lung cancer patients diagnosed between 2005 and 2018 in Nova Scotia, Canada. Screening eligibility was determined using the National Lung Screening Trial (NSLT) criteria. Mortality risk between heavy smokers and light-or-never-smokers was compared using proportional-hazards models. The median follow-up was 2.9 years. The cohort was comprised of 179 (46.1%) female heavy smokers and 306 (57.8%) female light-or-never-smokers. Light-or-never-smokers were more likely to have a diagnosis of adenocarcinoma [n=378 (71.6%)] compared to heavy smokers [n=234 (60.5%); P< 0.001]. Heavy smokers were more frequently diagnosed with squamous cell carcinoma [n=111 (28.7%)] compared to light-or-never-smokers, [n=100 (18.9%); P< 0.001]. Overall, 36.9% (338) of patients met NLST screening criteria. There was no difference in 5-year survival between light-or-never-smokers and heavy smokers [55.2% (338) vs 58.5% (529); P = 0.408; HR 1.06, 95% CI 0.80-1.40; P = 0.704]. Multivariate analysis showed that males had an increased mortality risk [HR 2.00 (95% CI 1.57-2.54); P< 0.001]. Half of lung cancer patients were missed with the conventional screening criteria. There were more curable, stage 1 tumors among light-or-never-smokers. Smoking status and age alone may be insufficient predictors of lung cancer risk and prognosis. Expanded risk factor analysis is needed to refine lung cancer screening criteria.
Collapse
Affiliation(s)
- Bright Huo
- Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Daria Manos
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Zhaolin Xu
- Department of Pathology, Dalhousie University, Halifax, NS, Canada
| | - Kara Matheson
- Research Methods Unit, Nova Scotia Health Authority, Halifax, NS, Canada
| | - Samuel Chun
- Department of Urology, Dalhousie University, Halifax, NS, Canada
| | - John Fris
- Department of Pathology, Dalhousie University, Halifax, NS, Canada
| | - Alison M R Wallace
- Department of Pathology, Dalhousie University, Halifax, NS, Canada; Division of Thoracic Surgery, Department of Surgery, Dalhousie University, Halifax, NS, Canada
| | - Daniel G French
- Division of Thoracic Surgery, Department of Surgery, Dalhousie University, Halifax, NS, Canada.
| |
Collapse
|