Comprehensive evaluation of benign and malignant pulmonary nodules using combined biological testing and imaging assessment in 1 017 patients: A retrospective cohort study_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHANG Lei ¹ , LI Zihao ² , LI Nan ³ , CHENG Jun ² , ZHANG Feng ³ , XIA Pinghui ² ,  LÜ Wang ² ,  HU Jian ²

1. Cadre Health Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P. R. China;
2. Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P. R. China;
3. Health Management Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P. R. China;

Corresponding author：

LÜ Wang, Email: xx00139@126.com; HU Jian, Email: dr_hujian@zju.edu.cn

Keywords：

Pulmonary nodules; autoantibody; thoracic imaging; prediction model

DOI：

10.7507/1007-4848.202409051

Video：

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Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

Citation： ZHANG Lei, LI Zihao, LI Nan, CHENG Jun, ZHANG Feng, XIA Pinghui, LÜ Wang, HU Jian. Comprehensive evaluation of benign and malignant pulmonary nodules using combined biological testing and imaging assessment in 1 017 patients: A retrospective cohort study. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(1): 60-66. doi: 10.7507/1007-4848.202409051 Copy

1.	Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2.	Oudkerk M, Liu S, Heuvelmans MA, et al. Lung cancer LDCT screening and mortality reduction—Evidence, pitfalls and future perspectives. Nat Rev Clin Oncol, 2021, 18(3): 135-151.
3.	National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
4.	Boiselle PM. Computed tomography screening for lung cancer. JAMA, 2013, 309(11): 1163-1170.
5.	Crosby D, Bhatia S, Brindle KM, et al. Early detection of cancer. Science, 2022, 375(6586): eaay9040.
6.	Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell, 2011, 144(5): 646-674.
7.	Hanahan D. Hallmarks of cancer: New dimensions. Cancer Discov, 2022, 12(1): 31-46.
8.	Aberle DR, DeMello S, Berg CD, et al. Results of the two incidence screenings in the National Lung Screening Trial. N Engl J Med, 2013, 369(10): 920-931.
9.	Mittal D, Gubin MM, Schreiber RD, et al. New insights into cancer immunoediting and its three component phases—Elimination, equilibrium and escape. Curr Opin Immunol, 2014, 27: 16-25.
10.	Sullivan FM, Farmer E, Mair FS, et al. Detection in blood of autoantibodies to tumour antigens as a case-finding method in lung cancer using the EarlyCDT®-Lung Test (ECLS): Study protocol for a randomized controlled trial. BMC Cancer, 2017, 17(1): 187.
11.	Ren S, Zhang S, Jiang T, et al. Early detection of lung cancer by using an autoantibody panel in Chinese population. Oncoimmunology, 2017, 7(2): e1384108.
12.	DeVita VT, Young RC, Canellos GP. Combination versus single agent chemotherapy: A review of the basis for selection of drug treatment of cancer. Cancer, 1975, 35(1): 98-110.
13.	Ehx G, Perreault C. Discovery and characterization of actionable tumor antigens. Genome Med, 2019, 11(1): 29.
14.	Finn OJ. Human tumor antigens yesterday, today, and tomorrow. Cancer Immunol Res, 2017, 5(5): 347-354.
15.	Massion PP, Healey GF, Peek LJ, et al. Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer. J Thorac Oncol, 2017, 12(3): 578-584.
16.	Wang W, Zhuang R, Ma H, et al. The diagnostic value of a seven-autoantibody panel and a nomogram with a scoring table for predicting the risk of non-small-cell lung cancer. Cancer Sci, 2020, 111(5): 1699-1710.
17.	He T, Wu Z, Xia P, et al. The combination of a seven-autoantibody panel with computed tomography scanning can enhance the diagnostic efficiency of non-small cell lung cancer. Front Oncol, 2022, 12: 1047019.
18.	Patel AJ, Tan TM, Richter AG, et al. A highly predictive autoantibody-based biomarker panel for prognosis in early-stage NSCLC with potential therapeutic implications. Br J Cancer, 2022, 126(2): 238-246.
19.	Chen SS, Li K, Wu J, et al. Stem signatures associated antibodies yield early diagnosis and precise prognosis predication of patients with non-small cell lung cancer. J Cancer Res Clin Oncol, 2021, 147(1): 223-233.
20.	Li Y, Li CQ, Guo SJ, et al. Longitudinal serum autoantibody repertoire profiling identifies surgery-associated biomarkers in lung adenocarcinoma. EBioMedicine, 2020, 53: 102674.
21.	Li S, Ma Y, Xiong Y, et al. Five tumor-associated autoantibodies expression levels in serum predict lung cancer and associate with poor outcome. Transl Cancer Res, 2019, 8(4): 1364-1373.

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2. Oudkerk M, Liu S, Heuvelmans MA, et al. Lung cancer LDCT screening and mortality reduction—Evidence, pitfalls and future perspectives. Nat Rev Clin Oncol, 2021, 18(3): 135-151.
3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
4. Boiselle PM. Computed tomography screening for lung cancer. JAMA, 2013, 309(11): 1163-1170.
5. Crosby D, Bhatia S, Brindle KM, et al. Early detection of cancer. Science, 2022, 375(6586): eaay9040.
6. Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell, 2011, 144(5): 646-674.
7. Hanahan D. Hallmarks of cancer: New dimensions. Cancer Discov, 2022, 12(1): 31-46.
8. Aberle DR, DeMello S, Berg CD, et al. Results of the two incidence screenings in the National Lung Screening Trial. N Engl J Med, 2013, 369(10): 920-931.
9. Mittal D, Gubin MM, Schreiber RD, et al. New insights into cancer immunoediting and its three component phases—Elimination, equilibrium and escape. Curr Opin Immunol, 2014, 27: 16-25.
10. Sullivan FM, Farmer E, Mair FS, et al. Detection in blood of autoantibodies to tumour antigens as a case-finding method in lung cancer using the EarlyCDT®-Lung Test (ECLS): Study protocol for a randomized controlled trial. BMC Cancer, 2017, 17(1): 187.
11. Ren S, Zhang S, Jiang T, et al. Early detection of lung cancer by using an autoantibody panel in Chinese population. Oncoimmunology, 2017, 7(2): e1384108.
12. DeVita VT, Young RC, Canellos GP. Combination versus single agent chemotherapy: A review of the basis for selection of drug treatment of cancer. Cancer, 1975, 35(1): 98-110.
13. Ehx G, Perreault C. Discovery and characterization of actionable tumor antigens. Genome Med, 2019, 11(1): 29.
14. Finn OJ. Human tumor antigens yesterday, today, and tomorrow. Cancer Immunol Res, 2017, 5(5): 347-354.
15. Massion PP, Healey GF, Peek LJ, et al. Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer. J Thorac Oncol, 2017, 12(3): 578-584.
16. Wang W, Zhuang R, Ma H, et al. The diagnostic value of a seven-autoantibody panel and a nomogram with a scoring table for predicting the risk of non-small-cell lung cancer. Cancer Sci, 2020, 111(5): 1699-1710.
17. He T, Wu Z, Xia P, et al. The combination of a seven-autoantibody panel with computed tomography scanning can enhance the diagnostic efficiency of non-small cell lung cancer. Front Oncol, 2022, 12: 1047019.
18. Patel AJ, Tan TM, Richter AG, et al. A highly predictive autoantibody-based biomarker panel for prognosis in early-stage NSCLC with potential therapeutic implications. Br J Cancer, 2022, 126(2): 238-246.
19. Chen SS, Li K, Wu J, et al. Stem signatures associated antibodies yield early diagnosis and precise prognosis predication of patients with non-small cell lung cancer. J Cancer Res Clin Oncol, 2021, 147(1): 223-233.
20. Li Y, Li CQ, Guo SJ, et al. Longitudinal serum autoantibody repertoire profiling identifies surgery-associated biomarkers in lung adenocarcinoma. EBioMedicine, 2020, 53: 102674.
21. Li S, Ma Y, Xiong Y, et al. Five tumor-associated autoantibodies expression levels in serum predict lung cancer and associate with poor outcome. Transl Cancer Res, 2019, 8(4): 1364-1373.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Comprehensive evaluation of benign and malignant pulmonary nodules using combined biological testing and imaging assessment in 1 017 patients: A retrospective cohort study

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