Preamble

Research on General Practitioner Tools: Analysis of Screening Tools for Chronic Obstructive Pulmonary Disease Comorbidity with Lung Cancer

XU Baichuan¹,², WANG Yan¹,², ZHANG Peng¹,², LI Yiting¹,², LIU Feilai³, XIE Yang¹,⁴,⁵*

¹Respiratory Department, First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou 450003, China
²First Clinical School of Henan University of Traditional Chinese Medicine, Zhengzhou 450046, China
³Rehabilitation Diagnosis and Treatment Center, First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou 450003, China
⁴Henan University of Traditional Chinese Medicine Respiratory Disease Diagnosis and Treatment and New Drug Research and Development Collaborative Innovation Center (Jointly Established by Province and Ministry), Zhengzhou 450046, China
⁵International Joint Laboratory of Evidence-based Evaluation for Respiratory Diseases of Henan Province, Zhengzhou 450003, China

Corresponding author: XIE Yang, Chief Physician/Doctoral Supervisor; E-mail: xieyanghn@163.com

[Abstract] Chronic Obstructive Pulmonary Disease (COPD) and Lung Cancer (LC) are respiratory diseases with high incidence and mortality rates. COPD is an independent high-risk factor for LC, and the two conditions influence each other, posing challenges for clinical diagnosis and treatment. Early detection and treatment are crucial for improving prognosis, making preliminary screening particularly important. This article reviews the current research status of COPD-LC, comprehensively introducing existing screening tools including Low-Dose Computed Tomography (LDCT), the COPD-LUCSS score and its improved version COPD-LUCSS-DLCO score, and other COPD-LC risk prediction models. By objectively analyzing the advantages and limitations of existing screening tools, this article proposes countermeasures and considerations for developing new screening tools and envisions future application prospects, aiming to provide support for future COPD-LC screening research.

[Key words] Pulmonary disease, chronic obstructive; Lung neoplasms; Multiple chronic conditions; Screening tools; Risk factors; Predictive model; Clinical application

[Chinese Library Classification] R 563.9 R 734.2
[Document code] A
DOI: 10.12114/j.issn.1007-9572.2025.0110

Chronic Obstructive Pulmonary Disease (COPD) and Lung Cancer (LC) are both common respiratory diseases characterized by high incidence, disability, and mortality rates, imposing a heavy burden on patients and society and becoming focal diseases of clinical concern. COPD is one of the main comorbidities of chronic fatal diseases globally[1] and ranks as the third leading cause of death worldwide. With the continuous intensification of global aging, the incidence of COPD will continue to rise[2], with projections indicating that over 5.4 million people will die from COPD by 2060[3]. The proportion of cancer patients dying from COPD is gradually increasing, a trend that is particularly evident among LC patients[4].

As an independent high-risk factor for LC development, COPD increases LC incidence by more than twofold compared to the general population[5]. Epidemiological data show that there are approximately 2 million new cases and millions of LC deaths worldwide each year[6]. Over half of LC patients have comorbid COPD, and LC incidence increases with COPD severity[7]; 0.8%~2.6% of COPD patients develop LC annually[8]. COPD and LC influence each other in multiple aspects including pathogenesis, treatment efficacy, and prognosis, necessitating further research on their comorbidity.

1.1 Formation and Mutual Influence of COPD-LC Comorbidity

As early as 1975, research from the London Chest Hospital indicated that COPD might be a risk factor for LC, with COPD patients having 3-6 times higher probability of developing LC than those with normal lung function[9]. In the 1980s, SKILLRUD et al.[10] and TOCKMAN et al.[11] first proposed that increased LC incidence and mortality were associated with airway obstruction and impaired lung function, with COPD patients facing higher LC risk. A comprehensive analysis by the International Lung Cancer Consortium showed that COPD was independently associated with small cell lung cancer[12].

Furthermore, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) also identified emphysema as an independent predictor of LC[13], with closer correlations observed in patients with GOLD I/II, older age, low BMI, and diffusing capacity of the lung for carbon monoxide (DLCO) <80%[14]. COPD is the second most common competing cause of death in LC patients[4], while LC is also a major cause of death in COPD patients. Approximately 40% of COPD patients die within one year of LC diagnosis, with LC accounting for 33% of all COPD-related deaths[8].

1.2 COPD-LC Pathogenesis

Current research indicates that COPD and LC may share comorbidity mechanisms in genetic susceptibility, oxidative stress, chronic inflammatory response, and epithelial-mesenchymal transition[5]. New studies have also suggested that changes in respiratory microbiota and abnormal long non-coding RNA expression may be associated with COPD-LC pathogenesis[17]. To date, no universally accepted COPD-LC comorbidity mechanism has been established, and further research is needed.

1.3 Clinical Diagnosis and Treatment of COPD-LC

Currently, there is no unified standard for clinical diagnosis and treatment of COPD-LC, with low rates of diagnosis and standardized treatment for the comorbidity. Existing studies show that only about 7.1% of COPD-LC patients can receive comprehensive diagnosis, while only 28%~35% can obtain standardized treatment[18]. Current treatment for COPD-LC primarily focuses on LC treatment, with insufficient attention to standardized COPD management, mainly because LC remains the primary cause of death in these patients. However, it cannot be denied that COPD affects LC treatment choices and increases the risk of adverse reactions to some extent. Studies have shown that COPD increases the incidence of postoperative complications and adverse reactions after LC treatment[19-20]. The "Consensus" also notes that the presence of LC masks COPD treatment to some extent, and there may be drug interactions between treatments for the two diseases, making the management of COPD-LC coexistence more complex and challenging[16]. How to simultaneously manage both diseases in COPD-LC patients remains a clinical challenge, urgently requiring the formation of a unified clinical consensus.

DE-TORRES et al.[14] found that among COPD-LC high-risk patients with consistent treatment status, those with early LC detection through screening had better long-term survival rates. This demonstrates that LC screening in COPD patients can improve survival and long-term prognosis. Numerous reviews have shown that COPD and LC are closely related. Although the interaction and mechanisms between COPD and LC remain controversial due to disease heterogeneity, the consensus that COPD is an independent high-risk factor for LC has been widely established. Therefore, LC screening in COPD patients has extremely high research value, promising development prospects, and warrants further investigation.

2 Current Status of COPD-LC Screening Tool Research

Currently, low-dose computed tomography (LDCT) is globally recognized for LC screening in the general population and has accumulated substantial clinical evidence. COPD and LC share common risk factors and pathogenic mechanisms, influencing each other as both cause and effect[15]. The "International Expert Consensus on Diagnosis and Treatment of Lung Cancer Complicated by Chronic Obstructive Pulmonary Disease"[16] (hereinafter referred to as the "Consensus") points out that smoking, air pollution, occupational dust exposure, and previous lung disease history are all risk factors for COPD-LC. However, research on LC screening specifically for COPD populations is relatively limited, with existing screening tools lacking sufficient clinical evidence for large-scale clinical application, necessitating high-quality evidence-based research.

2.1 LDCT Screening

LC screening trials began as early as the 1970s, primarily based on chest X-rays and sputum analysis, but without evidence of reduced LC mortality[21]. It was not until the 1990s that a landmark paper on LDCT screening potential was published[22]. Subsequently, two large LDCT screening trials provided evidence that LDCT screening significantly reduces LC mortality[23-24]. Based on this, the U.S. Preventive Services Task Force (USPSTF) recommended LDCT for LC screening[25], making LDCT the most commonly used LC screening tool globally. As an independent high-risk factor for LC, COPD represents a special high-risk population. The GOLD guidelines recommend annual LDCT screening for patients aged 50-80 years with a 20 pack-year smoking history or who quit within 15 years[26].

However, although LDCT screening is effective for LC diagnosis, it still has radiation exposure issues and is not conducive to short-term follow-up. Additionally, the diversity of small pulmonary nodules on imaging affects LDCT screening judgment, often resulting in false positives and subsequent overdiagnosis. Meanwhile, the location, phenotype, and severity of emphysema all affect LC risk. In summary, while LDCT screening can identify COPD patients at greater risk for LC, numerous studies have confirmed that due to imaging specificity, it is not entirely suitable for COPD patient screening. Some studies indicate that for COPD patients with GOLD III/IV, LDCT screening provides low benefit while increasing exposure opportunities, and thus is not recommended[27]. Relatively, patients with mild to moderate COPD and emphysema benefit more from screening[28]. Therefore, LDCT screening is not particularly suitable for COPD patients, necessitating the development of LC screening tools specifically for COPD populations.

2.4 Current Status of COPD-LC Risk Prediction Model Research

In recent years, research on LC risk prediction models for COPD patients has been continuous, primarily concentrated in the last three years, indicating that LC screening in COPD patients is a current hot topic in clinical research. HUANG Junan[37] constructed a model including six indicators: smoking index, hemoptysis, weight loss, atelectasis, pleural effusion, and GOLD grade, with a sample size of 108 pure COPD patients and 93 COPD-LC patients. The results showed that COPD-LC patients mostly occurred in those with smoking index ≥400 pack-years. When patients present with symptoms such as chest pain, hemoptysis, weight loss, or signs like atelectasis and pleural effusion, and elevated platelet count, LC screening should be considered.

LI Mengqi et al.[38] found that decreased BMI, increased total lung emphysema index, increased total lung mean density, increased forced vital capacity, and increased prothrombin time activity were risk factors for COPD patients developing LC, establishing a machine learning-based prediction model for LC risk in COPD patients. Sample size: 99 pure COPD patients and 55 COPD-LC patients. This was the first study to investigate clinical features combined with chest quantitative CT for predicting LC risk. Other models include the elderly COPD comorbidity LC screening score model[39], COPD-LC machine learning prediction model[38], DLCO LC screening model[40], COPD-LC nomogram prediction model[41], COPD-LC "syndrome-gene-environment" prediction model[42], and middle-aged and elderly male COPD-LC mathematical prediction model[43], detailed in Table 1 [TABLE:1].

The key problem with existing screening models is that most are based on single-center retrospective data, lacking prospective validation, with selection bias and missing data. Moreover, the high-risk factors included in prediction models often require precise testing and are not convenient for repeated measurement, making them suitable only for screening hospitalized patients in large hospitals but not for grassroots promotion.

3.1 Deficiencies in Current COPD-LC Screening

Currently, LDCT is the recognized LC screening tool that effectively reduces LC mortality, but it still has issues such as radiation exposure and low detection frequency. COPD, as an independent high-risk factor for LC, increases LC incidence in its patients. By comparing the applicability of existing screening tools and analyzing their advantages and limitations, it is evident that current screening tools cannot meet clinical needs, urgently requiring the development of convenient and efficient LC screening tools suitable for COPD patients.

3.2 Considerations for Developing COPD-LC Screening Tools

One of the biggest challenges in screening programs is selecting patients with optimal risk-benefit ratios[44]. LC screening for COPD patients should first identify those worth screening, while determining inclusion/exclusion criteria requires substantial clinical evidence support. High-quality clinical data is the prerequisite for obtaining high-quality clinical evidence. Currently, we should actively establish COPD population cohorts (multi-center, large-sample), with prospective studies as the main approach and retrospective studies as supplementary, to obtain large amounts of first-hand high-quality clinical data.

Second, when developing screening tools, we should fully consider COPD characteristics, including disease progression and prognosis, particularly paying attention to airflow limitation severity. Combining cutting-edge research, include as many risk factors as possible, conduct regression analysis for each, and ultimately determine the most relevant independent risk factors. Moreover, LC risk should be analyzed dynamically with COPD disease progression; a single COPD acute exacerbation should not significantly affect LC risk assessment, which should be based on long-term, high-frequency monitoring results. Of course, we must not neglect that developed screening tools should be continuously updated and improved through clinical validation. A good screening tool is obtained through long-term clinical practice and optimization, not一劳永逸based on a few clinical study results.

In summary, when developing LC screening tools for COPD populations, we should fully integrate COPD disease characteristics, pay attention to clinical applicability, enable long-term high-frequency monitoring as much as possible, and facilitate large-scale promotion and application.

4.1 New Assistance for Screening Tool Development

Currently, COPD-LC has gradually become a serious public health problem that deserves sufficient attention. COPD has important impacts on LC occurrence, development, diagnosis, treatment, and prognosis, making LC screening for COPD populations crucial. Screening and assessing COPD populations without diagnosed LC, with early intervention for high-risk groups, can effectively reduce LC incidence, thus urgently requiring more practical, convenient, and efficient screening tools.

With rapid scientific and technological development, modern medicine has integrated many new technologies, including artificial intelligence and big data, increasingly tending toward whole-cycle management and individualized diagnosis and treatment models. More detailed patient information can be obtained by clinicians, which greatly assists screening tool development. For LC screening tool development in COPD patients, when selecting high-risk factors, we can consider portable spirometers, exhaled breath condensate detection, using non-invasive, low-risk indicators such as lung function parameters and exhaled breath condensate biomarkers. Additionally, considering China's medical context, Traditional Chinese Medicine (TCM) four-diagnosis information can also be incorporated. Tongue diagnostic instruments, pulse diagnostic instruments, facial diagnostic instruments, and constitution identification systems are already being promoted and applied, with their convenience, safety, and low-risk characteristics being exactly what screening tool development requires.

Relying on current big data technology, patients' basic clinical characteristics, easily accessible biological indicators from examinations, TCM basic syndromes, constitution identification, and other information can all be included in large databases for regular physical examinations, facilitating extraction and analysis. Future screening tools should be developed through patient-centered, multi-dimensional information integration, primarily for real-time monitoring and updating. More and more precise data information enables more accurate screening, with individualized screening tools for different diseases and populations. People's physical examination and medical information can be uploaded to cloud databases in real-time, enabling risk prediction and health data management for various high-risk diseases based on existing cloud data. Future screening tool development can consider research in this direction.

4.2 Application Prospects of COPD-LC Screening Tools

COPD-LC screening tools have extremely high clinical research value, and high-quality screening tools are urgently needed in future clinical practice. As an independent risk factor for LC, early screening in COPD patients can significantly improve detection rates, enabling early detection and treatment, reducing social costs, and alleviating patient disease burden, while further improving patient quality of life. Unlike LC screening in the general population, LC screening for COPD patients should be more precise. With continuous in-depth research on COPD-LC, future molecular mechanisms, biomarkers, therapeutic targets, etc., will become more precise, all of which are expected to be incorporated into screening tools to achieve more accurate individualized screening.

Additionally, management of classified populations after screening should also receive special attention. Even low-risk populations still have relatively high LC incidence risk compared to the general population. Screening is a means, not an end. Systematic management of different risk populations after screening should also become part of the screening work. For COPD-LC screening, as long as patients have not developed LC, continuous management should be maintained. This not only facilitates more detailed collection and organization of relevant data for continuously updating and improving screening tools but also brings enormous benefits to patients. Future medical directions will be whole-cycle management and individualized diagnosis and treatment, with early screening being an indispensable component of future medical models. Therefore, COPD-LC screening tools have good application prospects and warrant high-quality clinical research.

Author Contributions: XU Baichuan was responsible for manuscript writing; WANG Yan was responsible for literature retrieval and related data compilation; ZHANG Peng was responsible for conception and design; LI Yiting was responsible for supplementary table organization; LIU Feilai was responsible for manuscript review and revision; XIE Yang was responsible for quality control and supervision, and overall responsibility for the article.

Conflict of Interest: None declared.

XU Baichuan: https://orcid.org/0009-0003-3943-560X

References:

[1] CHRISTENSON S A, SMITH B M, BAFADHEL M, et al. Chronic obstructive pulmonary disease[J]. Lancet, 2022, 399(10342): 2227-2242. DOI: 10.1016/S0140-6736(22)00470-6.

[2] GUAN W J, ZHENG X Y, CHUNG K F, et al. Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action[J]. Lancet, 2016, 388(10054): 1939-1951. DOI: 10.1016/S0140-6736(16)31597-5.

[3] LIANG Z Y, CHEN R C. Revision process and reflections on the Guidelines for the Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease (2021 Revised Edition)[J]. Chinese Journal of Tuberculosis and Respiratory Diseases, 2021, 44(3): 170-175.

[4] ZHENG Y Q, HUANG Y, ZHENG X W, et al. Deaths from COPD in patients with cancer: a population-based study[J]. Aging (Albany NY), 2021, 13(9): 12641-12659. DOI: 10.18632/aging.202939.

[5] FORDER A, ZHUANG R, SOUZA V G P, et al. Mechanisms contributing to the comorbidity of COPD and lung cancer[J]. Int J Mol Sci, 2023, 24(3): 2859. DOI: 10.3390/ijms24032859.

[6] THAI A A, SOLOMON B J, SEQUIST L V, et al. Lung cancer[J]. Lancet, 2021, 398(10299): 535-554. DOI: 10.1016/S0140-6736(21)00312-3.

[7] PATEL B, PRIEFER R. Impact of chronic obstructive pulmonary disease, lung infection, and/or inhaled corticosteroids use on potential risk of lung cancer[J]. Life Sci, 2022, 294: 120374. DOI: 10.1016/j.lfs.2022.120374.

[8] MACHIDA H, INOUE S, SHIBATA Y, et al. The incidence and risk analysis of lung cancer development in patients with chronic obstructive pulmonary disease: possible effectiveness of annual CT-screening[J]. Int J Chron Obstruct Pulmon Dis, 2021, 16: 739-749. DOI: 10.2147/COPD.S287492.

[9] CAPLIN M, FESTENSTEIN F. Relation between lung cancer, chronic bronchitis, and airways obstruction[J]. Br Med J, 1975, 3(5985): 678-680. DOI: 10.1136/bmj.3.5985.678.

[10] SKILLRUD D M, OFFORD K P, MILLER R D. Higher risk of lung cancer in chronic obstructive pulmonary disease. A prospective, matched, controlled study[J]. Ann Intern Med, 1986, 105(4): 503-507. DOI: 10.7326/0003-4819-105-4-503.

[11] TOCKMAN M S, ANTHONISEN N R, WRIGHT E C, et al. Airways obstruction and the risk for lung cancer[J]. Ann Intern Med, 1987, 106(4): 512-518. DOI: 10.7326/0003-4819-106-4-512.

[12] HUANG R Y, WEI Y Y, HUNG R J, et al. Associated links among smoking, chronic obstructive pulmonary disease, and small cell lung cancer: a pooled analysis in the international lung cancer consortium[J]. EBioMedicine, 2015, 2(11): 1677-1685. DOI: 10.1016/j.ebiom.2015.09.031.

[13] TAMURA T, MIYAZAKI K, SATOH H. Features of COPD as predictors of lung cancer[J]. Chest, 2018, 154(3): 720-721. DOI: 10.1016/j.chest.2018.04.047.

[14] DE TORRES J P, MARÍN J M, CASANOVA C, et al. Lung cancer in patients with chronic obstructive pulmonary disease—incidence and predicting factors[J]. Am J Respir Crit Care Med, 2011, 184(8): 913-919. DOI: 10.1164/rccm.201103-0430OC.

[15] SHEN P X, QIN Y Y, ZHOU C Z. Brief analysis of clinical diagnosis and treatment of lung cancer and chronic obstructive pulmonary disease comorbidity[J]. Chinese Journal of Tuberculosis and Respiratory Diseases, 2024, 47(11): 1045-1048.

[16] ZHOU C Z, QIN Y Y, ZHAO W, et al. International expert consensus on diagnosis and treatment of lung cancer complicated by chronic obstructive pulmonary disease[J]. Transl Lung Cancer Res, 2023, 12(8): 1661-1701. DOI: 10.21037/tlcr-23-339.

[17] ZHENG L L, CHEN H Y. Research progress on pathogenesis and treatment of chronic obstructive pulmonary disease complicated with lung cancer[J]. Laboratory Medicine and Clinic, 2024, 21(19): 2935-2939.

[18] ZHANG J, ZHOU J B, LIN X F, et al. Prevalence of undiagnosed and undertreated chronic obstructive pulmonary disease in lung cancer population[J]. Respirology, 2013, 18(2): 297-302. DOI: 10.1111/j.1440-1843.2012.02282.x.

[19] XU W G, ZHU J, LI L, et al. The prognostic role of chronic obstructive pulmonary disease for lung cancer after pulmonary resection[J]. J Surg Res, 2022, 275: 137-148. DOI: 10.1016/j.jss.2022.01.014.

[20] AJIMIZU H, OZASA H, SATO S, et al. Survival impact of treatment for chronic obstructive pulmonary disease in patients with advanced non-small-cell lung cancer[J]. Sci Rep, 2021, 11(1): 23677. DOI: 10.1038/s41598-021-03139-5.

[21] FROST J K, BALL W C Jr, LEVIN M L, et al. Early lung cancer detection: results of the initial (prevalence) radiologic and cytologic screening in the Johns Hopkins study[J]. Am Rev Respir Dis, 1984, 130(4): 549-554. DOI: 10.1164/arrd.1984.130.4.549.

[22] HENSCHKE C I. Early lung cancer action project: overall design and findings from baseline screening[J]. Cancer, 2000, 89(11 suppl): 2474-2482. DOI: 10.1002/1097-0142(20001201)89:11+<2474::aid-cncr26>3.3.co;2-u.

[23] ABERLE D R, ADAMS A M, BERG C D, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening[J]. N Engl J Med, 2011, 365(5): 395-409. DOI: 10.1056/nejmoa1102873.

[24] DE KONING H J, VAN DER AALST C M, DE JONG P A, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial[J]. N Engl J Med, 2020, 382(6): 503-513. DOI: 10.1056/NEJMoa1911793.

[25] MITCHELL E P. U.S. preventive services task force final recommendation statement, evidence summary, and modeling studies on screening for lung cancer[J]. J Natl Med Assoc, 2021, 113(3): 239-240. DOI: 10.1016/j.jnma.2021.05.012.

[26] Global Strategy for Prevention, Diagnosis and Management of COPD: 2025 Report[EB/OL]. [2025-03-07]. https://goldcopd.org/2025-gold-report/.

[27] RUPAREL M. Lung cancer screening in advanced chronic obstructive pulmonary disease: helpful or harmful[J]. Thorax, 2023, 78(7): 637-639. DOI: 10.1136/thorax-2022-219778.

[28] DE-TORRES J P, ALCAIDE A B, CAMPO A, et al. Lung cancer screening in people with COPD: the Pamplona-IELCAP experience[J]. Arch Bronconeumol, 2024, 60(2): 95-100. DOI: 10.1016/j.arbres.2023.12.012.

[29] DE-TORRES J P, WILSON D O, SANCHEZ-SALCEDO P, et al. Lung cancer in patients with chronic obstructive pulmonary disease. Development and validation of the COPD Lung Cancer Screening Score[J]. Am J Respir Crit Care Med, 2015, 191(3): 285-291. DOI: 10.1164/rccm.201407-1210OC.

[30] GAGNAT A A, GULSVIK A, BAKKE P, et al. Comparison of two lung cancer screening scores among patients with chronic obstructive pulmonary disease: a community study[J]. Clin Respir J, 2019, 13(2): 114-119. DOI: 10.1111/crj.12988.

[31] NAMBU A, ZACH J, SCHROEDER J, et al. Relationships between diffusing capacity for carbon monoxide (DLCO), and quantitative computed tomography measurements and visual assessment for chronic obstructive pulmonary disease[J]. Eur J Radiol, 2015, 84(5): 980-985. DOI: 10.1016/j.ejrad.2015.01.010.

[32] DE-TORRES J P, MARÍN J M, CASANOVA C, et al. Identification of COPD patients at high risk for lung cancer mortality using the COPD-LUCSS-DLCO[J]. Chest, 2016, 149(4): 936-942. DOI: 10.1378/chest.15-1868.

[33] D'ANNA S E, ASNAGHI R, CARAMORI G, et al. High-resolution computed tomography quantitation of emphysema is correlated with selected lung function values in stable COPD[J]. Respiration, 2012, 83(5): 383-390. DOI: 10.1159/000329871.

[34] CHEN Y, ZENG X L, BAO H R, et al. Predictive value of diffusing capacity in chronic obstructive pulmonary disease complicated with lung cancer[J]. International Journal of Respiration, 2022, 42(10): 721-725.

[35] FIGUEIRA GONÇALVES J M, PÉREZ MENDEZ L I, GURBANI N, et al. Applicability of the COPD-LUCSS-DLCO score for patients with chronic obstructive pulmonary disease: Analysis in standard clinical practice conditions[J]. Rev Clin Esp (Barc), 2018, 218(7): 336-341. DOI: 10.1016/j.rce.2018.04.008.

[36] SOUSA S R, CALDEIRA J N, RODRIGUES C, et al. Lung cancer screening in clinical practice: identification of high-risk chronic obstructive pulmonary disease patients[J]. Rev Assoc Med Bras (1992), 2022, 68(4): 502-506. DOI: 10.1590/1806-9282.20211296.

[37] HUANG J N. Analysis of clinical characteristics of COPD complicated with lung cancer and establishment of a prediction model[D]. Changchun: Jilin University, 2024. DOI: 10.27162/d.cnki.gjlin.2024.002525.

[38] LI M Q, HE F T, WU Y N, et al. Establishment of a prediction model for risk of chronic obstructive pulmonary disease complicated with lung cancer based on machine learning methods[J]. Chinese Journal of Respiratory and Critical Care Medicine, 2022, 21(11): 782-789.

[39] ZHANG Y, LIU X J. Establishment of a screening score model for elderly chronic obstructive pulmonary disease complicated with lung cancer and study of related serological markers[J]. International Journal of Respiration, 2022, 42(1): 62-66. DOI: 10.3760/cma.j.cn131368-20201111-01019.

[40] SUN L. Establishment of the COPD-LUCSS-DLCO lung cancer screening model and study of related serological markers[D]. Taiyuan: Shanxi Medical University, 2023. DOI: 10.27288/d.cnki.gsxyu.2023.001587.

[41] QIN S X, YE Y Q. Analysis of clinical characteristics of chronic obstructive pulmonary disease complicated with lung cancer and establishment of a nomogram prediction model[J]. Journal of Clinical Pulmonary Medicine, 2023, 28(11): 1656-1661.

[42] GU Z, FENG D, ZHAO Y, et al. Construction of a "syndrome-gene-environment" prediction model for chronic obstructive pulmonary disease complicated with lung cancer[J]. Journal of Tongji University (Medical Edition), 2024, 45(5): 706-712.

[43] CHENG W Y, WANG X Y, TANG H P. Analysis of clinical characteristics and establishment of a mathematical prediction model for middle-aged and elderly male patients with chronic obstructive pulmonary disease complicated with lung cancer[J]. Advances in Clinical Medicine, 2024, 14(3): 1813-1823.

[44] HUMPHREY L L, DEFFEBACH M, PAPPAS M, et al. Screening for lung cancer with low-dose computed tomography: a systematic review to update the US Preventive services task force recommendation[J]. Ann Intern Med, 2013, 159(6): 411-420. DOI: 10.7326/0003-4819-159-6-201309170-00690.

(Received date: 2025-04-03; Revised date: 2025-06-15)
(Editor: ZHAO Yuecui)