Research progress on liquid biopsy and its combination of radiology in diagnosis of pulmonary nodules_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

SUN Shuo ^1,2 , WANG Feng ^1,2 , HE Li ^1,2 ,  YANG Wenwen ^1,2 , HAN Biao ^1,2 , ZHAO Mengmeng ³ ,  CHEN Chang ^1,2,3 , MA Minjie ^1,2

1. The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, P. R. China;
2. Department of Throcic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, P. R. China;
3. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, P. R. China;

Corresponding author：

CHEN Chang, Email: chenthoracic@163.com

Keywords：

Liquid biopsy; circulating tumor cells; radiology; radiomics; pulmonary nodules; review

DOI：

10.7507/1007-4848.202205044

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung cancer is a malignant tumor with the highest mortality worldwide, and its early diagnosis and evaluation have a crucial impact on the comprehensive treatment of patients. Early preoperative diagnosis of lung cancer depends on a variety of imaging and tumor marker indicators, but it cannot be accurately assessed due to its high false positive rate. Liquid biopsy biomarkers can detect circulating tumor cells and DNA in peripheral blood by non-invasive methods and are gradually becoming a powerful diagnostic tool in the field of precision medicine for tumors. This article reviews the research progress of liquid biopsy biomarkers and their combination with clinical imaging features in the early diagnosis of lung cancer.

Citation： SUN Shuo, WANG Feng, HE Li, YANG Wenwen, HAN Biao, ZHAO Mengmeng, CHEN Chang, MA Minjie. Research progress on liquid biopsy and its combination of radiology in diagnosis of pulmonary nodules. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(2): 313-319. doi: 10.7507/1007-4848.202205044 Copy

1.	Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
3.	Shen J, Jiang F. Applications of microRNAs in the diagnosis and prognosis of lung cancer. Expert Opin Med Diagn, 2012, 6(3): 197-207.
4.	Larsen JE, Minna JD. Molecular biology of lung cancer: Clinical implications. Clin Chest Med, 2011, 32(4): 703-740.
5.	Hawkins S, Wang H, Liu Y, et al. Predicting malignant nodules from screening CT scans. J Thorac Oncol, 2016, 11(12): 2120-2128.
6.	Lin Y, Leng Q, Jiang Z, et al. A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules. Int J Cancer, 2017, 141(6): 1240-1248.
7.	Nakamura K, Sawada K, Yoshimura A, et al. Clinical relevance of circulating cell-free microRNAs in ovarian cancer. Mol Cancer, 2016, 15(1): 48.
8.	Usó M, Jantus-Lewintre E, Sirera R, et al. miRNA detection methods and clinical implications in lung cancer. Future Oncol, 2014, 10(14): 2279-2292.
9.	Lu S, Kong H, Hou Y, et al. Two plasma microRNA panels for diagnosis and subtype discrimination of lung cancer. Lung Cancer, 2018, 123: 44-51.
10.	Zhang X, Wang Q, Zhang S. MicroRNAs in sputum specimen as noninvasive biomarkers for the diagnosis of nonsmall cell lung cancer: An updated meta-analysis. Medicine (Baltimore), 2019, 98(6): e14337.
11.	Pan J, Zhou C, Zhao X, et al. A two-miRNA signature (miR-33a-5p and miR-128-3p) in whole blood as potential biomarker for early diagnosis of lung cancer. Sci Rep, 2018, 8(1): 16699.
12.	Fehlmann T, Kahraman M, Ludwig N, et al. Evaluating the use of circulating microRNA profiles for lung cancer detection in symptomatic patients. JAMA Oncol, 2020, 6(5): 714-723.
13.	Xie Y, Zhang Y, Du L, et al. Circulating long noncoding RNA act as potential novel biomarkers for diagnosis and prognosis of non-small cell lung cancer. Mol Oncol, 2018, 12(5): 648-658.
14.	Belinsky SA. Gene-promoter hypermethylation as a biomarker in lung cancer. Nat Rev Cancer, 2004, 4(9): 707-717.
15.	Weiss G, Schlegel A, Kottwitz D, et al. Validation of the SHOX2/PTGER4 DNA methylation marker panel for plasma-based discrimination between patients with malignant and nonmalignant lung disease. J Thorac Oncol, 2017, 12(1): 77-84.
16.	Wielscher M, Vierlinger K, Kegler U, et al. Diagnostic performance of plasma DNA methylation profiles in lung cancer, pulmonary fibrosis and COPD. EBioMedicine, 2015, 2(8): 929-936.
17.	Liang W, Chen Z, Li C, et al. Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test. J Clin Invest, 2021, 131(10): e145973.
18.	Chen C, Huang X, Yin W, et al. Ultrasensitive DNA hypermethylation detection using plasma for early detection of NSCLC: A study in Chinese patients with very small nodules. Clin Epigenetics, 2020, 12(1): 39.
19.	Jahr S, Hentze H, Englisch S, et al. DNA fragments in the blood plasma of cancer patients: Quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res, 2001, 61(4): 1659-1665.
20.	Garcia EP, Minkovsky A, Jia Y, et al. Validation of oncopanel: A targeted next-generation sequencing assay for the detection of somatic variants in cancer. Arch Pathol Lab Med, 2017, 141(6): 751-758.
21.	Leung M, Freidin MB, Freydina DV, et al. Blood-based circulating tumor DNA mutations as a diagnostic and prognostic biomarker for lung cancer. Cancer, 2020, 126(8): 1804-1809.
22.	Sequist LV, Nagrath S, Toner M, et al. The CTC-chip: An exciting new tool to detect circulating tumor cells in lung cancer patients. J Thorac Oncol, 2009, 4(3): 281-283.
23.	Tartarone A, Rossi E, Lerose R, et al. Possible applications of circulating tumor cells in patients with non small cell lung cancer. Lung Cancer, 2017, 107: 59-64.
24.	Gallo M, De Luca A, Maiello MR, et al. Clinical utility of circulating tumor cells in patients with non-small-cell lung cancer. Transl Lung Cancer Res, 2017, 6(4): 486-498.
25.	Marquette CH, Boutros J, Benzaquen J, et al. Circulating tumour cells as a potential biomarker for lung cancer screening: A prospective cohort study. Lancet Respir Med, 2020, 8(7): 709-716.
26.	Leroy S, Benzaquen J, Mazzetta A, et al. Circulating tumour cells as a potential screening tool for lung cancer (the AIR study): Protocol of a prospective multicentre cohort study in France. BMJ Open, 2017, 7(12): e018884.
27.	Yin W, Zhu J, Ma B, et al. Overcoming obstacles in pathological diagnosis of pulmonary nodules through circulating tumor cell enrichment. Small, 2020, 16(25): e2001695.
28.	Li J, Liao Y, Ran Y, et al. Evaluation of sensitivity and specificity of CanPatrol™ technology for detection of circulating tumor cells in patients with non-small cell lung cancer. BMC Pulm Med, 2020, 20(1): 274.
29.	Chen L, Peng M, Li N, et al. Combined use of EpCAM and FRα enables the high-efficiency capture of circulating tumor cells in non-small cell lung cancer. Sci Rep, 2018, 8(1): 1188.
30.	Tong B, Xu Y, Zhao J, et al. Prognostic role of circulating tumor cells in patients with EGFR-mutated or ALK-rearranged non-small cell lung cancer. Thorac Cancer, 2018, 9(5): 640-645.
31.	Lindsay CR, Faugeroux V, Michiels S, et al. A prospective examination of circulating tumor cell profiles in non-small-cell lung cancer molecular subgroups. Ann Oncol, 2017, 28(7): 1523-1531.
32.	Katsarou SD, Messaritakis I, Voumvouraki A, et al. Detyrosinated α-tubulin, vimentin and PD-L1 in circulating tumor cells (CTCs) isolated from non-small cell lung cancer (NSCLC) patients. J Pers Med, 2022, 12(2): 154.
33.	Kallergi G, Vetsika EK, Aggouraki D, et al. Evaluation of PD-L1/PD-1 on circulating tumor cells in patients with advanced non-small cell lung cancer. Ther Adv Med Oncol, 2018, 10: 1758834017750121.
34.	Molina R, Filella X, Augé JM, et al. Tumor markers (CEA, CA 125, CYFRA 21-1, SCC and NSE) in patients with non-small cell lung cancer as an aid in histological diagnosis and prognosis. Comparison with the main clinical and pathological prognostic factors. Tumour Biol, 2003, 24(4): 209-218.
35.	Ostroff RM, Bigbee WL, Franklin W, et al. Unlocking biomarker discovery: Large scale application of aptamer proteomic technology for early detection of lung cancer. PLoS One, 2010, 5(12): e15003.
36.	Ostrin EJ, Bantis LE, Wilson DO, et al. Contribution of a blood-based protein biomarker panel to the classification of indeterminate pulmonary nodules. J Thorac Oncol, 2021, 16(2): 228-236.
37.	Broodman I, Lindemans J, van Sten J, et al. Serum protein markers for the early detection of lung cancer: A focus on autoantibodies. J Proteome Res, 2017, 16(1): 3-13.
38.	Huang H, Luo W, Ni Y, et al. The diagnostic efficiency of seven autoantibodies in lung cancer. Eur J Cancer Prev, 2020, 29(4): 315-320.
39.	Yu L, Lin X, Zhang L, et al. The combination of IgA and IgG autoantibodies against transcriptional intermediary factor-1γ contributes to the early diagnosis of lung cancer. Int J Med Sci, 2020, 17(11): 1561-1568.
40.	Lastwika KJ, Kargl J, Zhang Y, et al. Tumor-derived autoantibodies identify malignant pulmonary nodules. Am J Respir Crit Care Med, 2019, 199(10): 1257-1266.
41.	Jin X, Chen Y, Chen H, et al. Evaluation of tumor-derived exosomal miRNA as potential diagnostic biomarkers for early-stage non-small cell lung cancer using next-generation sequencing. Clin Cancer Res, 2017, 23(17): 5311-5319.
42.	Wu Q, Yu L, Lin X, et al. Combination of serum miRNAs with serum exosomal miRNAs in early diagnosis for non-small-cell lung cancer. Cancer Manag Res, 2020, 12: 485-495.
43.	Li C, Lv Y, Shao C, et al. Tumor-derived exosomal lncRNA GAS5 as a biomarker for early-stage non-small-cell lung cancer diagnosis. J Cell Physiol, 2019, 234(11): 20721-20727.
44.	Sandfeld-Paulsen B, Jakobsen KR, Bæk R, et al. Exosomal proteins as diagnostic biomarkers in lung cancer. J Thorac Oncol, 2016, 11(10): 1701-1710.
45.	Jakobsen KR, Paulsen BS, Bæk R, et al. Exosomal proteins as potential diagnostic markers in advanced non-small cell lung carcinoma. J Extracell Vesicles, 2015, 4: 26659.
46.	Niu L, Song X, Wang N, et al. Tumor-derived exosomal proteins as diagnostic biomarkers in non-small cell lung cancer. Cancer Sci, 2019, 110(1): 433-442.
47.	Ma J, Guarnera MA, Zhou W, et al. A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules. Transl Oncol, 2017, 10(1): 40-45.
48.	Xing W, Sun H, Yan C, et al. A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules. BMC Cancer, 2021, 21(1): 263.
49.	Wei Q, Fang W, Chen X, et al. Establishment and validation of a mathematical diagnosis model to distinguish benign pulmonary nodules from early non-small cell lung cancer in Chinese people. Transl Lung Cancer Res, 2020, 9(5): 1843-1852.
50.	Zhang W, Duan X, Zhang Z, et al. Combination of CT and telomerase+ circulating tumor cells improves diagnosis of small pulmonary nodules. JCI Insight, 2021, 6(11): e148182.
51.	孔德志, 刘傲, 崔健, 等. 临床Ⅰ期非小细胞肺癌诊断模型构建: 基于临床影像学特征联合叶酸受体阳性循环肿瘤细胞检测的研究. 中国胸心血管外科临床杂志, 2021, 28(10): 1192-1201.
52.	Yonemori K, Tateishi U, Uno H, et al. Development and validation of diagnostic prediction model for solitary pulmonary nodules. Respirology, 2007, 12(6): 856-862.
53.	Pecot CV, Li M, Zhang XJ, et al. Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules. Cancer Epidemiol Biomarkers Prev, 2012, 21(5): 786-792.
54.	He X, Xue N, Liu X, et al. A novel clinical model for predicting malignancy of solitary pulmonary nodules: A multicenter study in chinese population. Cancer Cell Int, 2021, 21(1): 115.
55.	Du Q, Yu R, Wang H, et al. Significance of tumor-associated autoantibodies in the early diagnosis of lung cancer. Clin Respir J, 2018, 12(6): 2020-2028.
56.	Tan M, Ma W, Sun Y, et al. Prediction of the growth rate of early-stage lung adenocarcinoma by radiomics. Front Oncol, 2021, 11: 658138.
57.	Liu J, Xu H, Qing H, et al. Comparison of radiomic models based on low-dose and standard-dose CT for prediction of adenocarcinomas and benign lesions in solid pulmonary nodules. Front Oncol, 2021, 10: 634298.
58.	Jiao Z, Li H, Xiao Y, et al. Integration of deep learning radiomics and counts of circulating tumor cells improves prediction of outcomes of early stage NSCLC patients treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys, 2022, 112(4): 1045-1054.
59.	Maldonado F, Varghese C, Rajagopalan S, et al. Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules. Eur Respir J, 2021, 57(4): 2002485.
60.	Kammer MN, Lakhani DA, Balar AB, et al. Integrated biomarkers for the management of indeterminate pulmonary nodules. Am J Respir Crit Care Med, 2021, 204(11): 1306-1316.
61.	Kandathil A, Kay FU, Butt YM, et al. Role of FDG PET/CT in the eighth edition of TNM staging of non-small cell lung cancer. Radiographics, 2018, 38(7): 2134-2149.
62.	Kang F, Wang S, Tian F, et al. Comparing the diagnostic potential of 68Ga-Alfatide Ⅱand 18F-FDG in differentiating between non-small cell lung cancer and tuberculosis. J Nucl Med, 2016, 57(5): 672-677.
63.	Albano D, Gatta R, Marini M, et al. Role of 18 F-FDG PET/CT radiomics features in the differential diagnosis of solitary pulmonary nodules: Diagnostic accuracy and comparison between two different PET/CT scanners. J Clin Med, 2021, 10(21): 5064.
64.	Paez R, Shan C, Cords AJ, et al. 18F-FSPG PET imaging for the evaluation of indeterminate pulmonary nodules. PLoS One, 2022, 17(3): e0265427.
65.	Zhang F, Wu X, Zhu J, et al. 18F-FDG PET/CT and circulating tumor cells in treatment-naive patients with non-small-cell lung cancer. Eur J Nucl Med Mol Imaging, 2021, 48(10): 3250-3259.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med, 2020, 382(6): 503-513.
3. Shen J, Jiang F. Applications of microRNAs in the diagnosis and prognosis of lung cancer. Expert Opin Med Diagn, 2012, 6(3): 197-207.
4. Larsen JE, Minna JD. Molecular biology of lung cancer: Clinical implications. Clin Chest Med, 2011, 32(4): 703-740.
5. Hawkins S, Wang H, Liu Y, et al. Predicting malignant nodules from screening CT scans. J Thorac Oncol, 2016, 11(12): 2120-2128.
6. Lin Y, Leng Q, Jiang Z, et al. A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules. Int J Cancer, 2017, 141(6): 1240-1248.
7. Nakamura K, Sawada K, Yoshimura A, et al. Clinical relevance of circulating cell-free microRNAs in ovarian cancer. Mol Cancer, 2016, 15(1): 48.
8. Usó M, Jantus-Lewintre E, Sirera R, et al. miRNA detection methods and clinical implications in lung cancer. Future Oncol, 2014, 10(14): 2279-2292.
9. Lu S, Kong H, Hou Y, et al. Two plasma microRNA panels for diagnosis and subtype discrimination of lung cancer. Lung Cancer, 2018, 123: 44-51.
10. Zhang X, Wang Q, Zhang S. MicroRNAs in sputum specimen as noninvasive biomarkers for the diagnosis of nonsmall cell lung cancer: An updated meta-analysis. Medicine (Baltimore), 2019, 98(6): e14337.
11. Pan J, Zhou C, Zhao X, et al. A two-miRNA signature (miR-33a-5p and miR-128-3p) in whole blood as potential biomarker for early diagnosis of lung cancer. Sci Rep, 2018, 8(1): 16699.
12. Fehlmann T, Kahraman M, Ludwig N, et al. Evaluating the use of circulating microRNA profiles for lung cancer detection in symptomatic patients. JAMA Oncol, 2020, 6(5): 714-723.
13. Xie Y, Zhang Y, Du L, et al. Circulating long noncoding RNA act as potential novel biomarkers for diagnosis and prognosis of non-small cell lung cancer. Mol Oncol, 2018, 12(5): 648-658.
14. Belinsky SA. Gene-promoter hypermethylation as a biomarker in lung cancer. Nat Rev Cancer, 2004, 4(9): 707-717.
15. Weiss G, Schlegel A, Kottwitz D, et al. Validation of the SHOX2/PTGER4 DNA methylation marker panel for plasma-based discrimination between patients with malignant and nonmalignant lung disease. J Thorac Oncol, 2017, 12(1): 77-84.
16. Wielscher M, Vierlinger K, Kegler U, et al. Diagnostic performance of plasma DNA methylation profiles in lung cancer, pulmonary fibrosis and COPD. EBioMedicine, 2015, 2(8): 929-936.
17. Liang W, Chen Z, Li C, et al. Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test. J Clin Invest, 2021, 131(10): e145973.
18. Chen C, Huang X, Yin W, et al. Ultrasensitive DNA hypermethylation detection using plasma for early detection of NSCLC: A study in Chinese patients with very small nodules. Clin Epigenetics, 2020, 12(1): 39.
19. Jahr S, Hentze H, Englisch S, et al. DNA fragments in the blood plasma of cancer patients: Quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res, 2001, 61(4): 1659-1665.
20. Garcia EP, Minkovsky A, Jia Y, et al. Validation of oncopanel: A targeted next-generation sequencing assay for the detection of somatic variants in cancer. Arch Pathol Lab Med, 2017, 141(6): 751-758.
21. Leung M, Freidin MB, Freydina DV, et al. Blood-based circulating tumor DNA mutations as a diagnostic and prognostic biomarker for lung cancer. Cancer, 2020, 126(8): 1804-1809.
22. Sequist LV, Nagrath S, Toner M, et al. The CTC-chip: An exciting new tool to detect circulating tumor cells in lung cancer patients. J Thorac Oncol, 2009, 4(3): 281-283.
23. Tartarone A, Rossi E, Lerose R, et al. Possible applications of circulating tumor cells in patients with non small cell lung cancer. Lung Cancer, 2017, 107: 59-64.
24. Gallo M, De Luca A, Maiello MR, et al. Clinical utility of circulating tumor cells in patients with non-small-cell lung cancer. Transl Lung Cancer Res, 2017, 6(4): 486-498.
25. Marquette CH, Boutros J, Benzaquen J, et al. Circulating tumour cells as a potential biomarker for lung cancer screening: A prospective cohort study. Lancet Respir Med, 2020, 8(7): 709-716.
26. Leroy S, Benzaquen J, Mazzetta A, et al. Circulating tumour cells as a potential screening tool for lung cancer (the AIR study): Protocol of a prospective multicentre cohort study in France. BMJ Open, 2017, 7(12): e018884.
27. Yin W, Zhu J, Ma B, et al. Overcoming obstacles in pathological diagnosis of pulmonary nodules through circulating tumor cell enrichment. Small, 2020, 16(25): e2001695.
28. Li J, Liao Y, Ran Y, et al. Evaluation of sensitivity and specificity of CanPatrol™ technology for detection of circulating tumor cells in patients with non-small cell lung cancer. BMC Pulm Med, 2020, 20(1): 274.
29. Chen L, Peng M, Li N, et al. Combined use of EpCAM and FRα enables the high-efficiency capture of circulating tumor cells in non-small cell lung cancer. Sci Rep, 2018, 8(1): 1188.
30. Tong B, Xu Y, Zhao J, et al. Prognostic role of circulating tumor cells in patients with EGFR-mutated or ALK-rearranged non-small cell lung cancer. Thorac Cancer, 2018, 9(5): 640-645.
31. Lindsay CR, Faugeroux V, Michiels S, et al. A prospective examination of circulating tumor cell profiles in non-small-cell lung cancer molecular subgroups. Ann Oncol, 2017, 28(7): 1523-1531.
32. Katsarou SD, Messaritakis I, Voumvouraki A, et al. Detyrosinated α-tubulin, vimentin and PD-L1 in circulating tumor cells (CTCs) isolated from non-small cell lung cancer (NSCLC) patients. J Pers Med, 2022, 12(2): 154.
33. Kallergi G, Vetsika EK, Aggouraki D, et al. Evaluation of PD-L1/PD-1 on circulating tumor cells in patients with advanced non-small cell lung cancer. Ther Adv Med Oncol, 2018, 10: 1758834017750121.
34. Molina R, Filella X, Augé JM, et al. Tumor markers (CEA, CA 125, CYFRA 21-1, SCC and NSE) in patients with non-small cell lung cancer as an aid in histological diagnosis and prognosis. Comparison with the main clinical and pathological prognostic factors. Tumour Biol, 2003, 24(4): 209-218.
35. Ostroff RM, Bigbee WL, Franklin W, et al. Unlocking biomarker discovery: Large scale application of aptamer proteomic technology for early detection of lung cancer. PLoS One, 2010, 5(12): e15003.
36. Ostrin EJ, Bantis LE, Wilson DO, et al. Contribution of a blood-based protein biomarker panel to the classification of indeterminate pulmonary nodules. J Thorac Oncol, 2021, 16(2): 228-236.
37. Broodman I, Lindemans J, van Sten J, et al. Serum protein markers for the early detection of lung cancer: A focus on autoantibodies. J Proteome Res, 2017, 16(1): 3-13.
38. Huang H, Luo W, Ni Y, et al. The diagnostic efficiency of seven autoantibodies in lung cancer. Eur J Cancer Prev, 2020, 29(4): 315-320.
39. Yu L, Lin X, Zhang L, et al. The combination of IgA and IgG autoantibodies against transcriptional intermediary factor-1γ contributes to the early diagnosis of lung cancer. Int J Med Sci, 2020, 17(11): 1561-1568.
40. Lastwika KJ, Kargl J, Zhang Y, et al. Tumor-derived autoantibodies identify malignant pulmonary nodules. Am J Respir Crit Care Med, 2019, 199(10): 1257-1266.
41. Jin X, Chen Y, Chen H, et al. Evaluation of tumor-derived exosomal miRNA as potential diagnostic biomarkers for early-stage non-small cell lung cancer using next-generation sequencing. Clin Cancer Res, 2017, 23(17): 5311-5319.
42. Wu Q, Yu L, Lin X, et al. Combination of serum miRNAs with serum exosomal miRNAs in early diagnosis for non-small-cell lung cancer. Cancer Manag Res, 2020, 12: 485-495.
43. Li C, Lv Y, Shao C, et al. Tumor-derived exosomal lncRNA GAS5 as a biomarker for early-stage non-small-cell lung cancer diagnosis. J Cell Physiol, 2019, 234(11): 20721-20727.
44. Sandfeld-Paulsen B, Jakobsen KR, Bæk R, et al. Exosomal proteins as diagnostic biomarkers in lung cancer. J Thorac Oncol, 2016, 11(10): 1701-1710.
45. Jakobsen KR, Paulsen BS, Bæk R, et al. Exosomal proteins as potential diagnostic markers in advanced non-small cell lung carcinoma. J Extracell Vesicles, 2015, 4: 26659.
46. Niu L, Song X, Wang N, et al. Tumor-derived exosomal proteins as diagnostic biomarkers in non-small cell lung cancer. Cancer Sci, 2019, 110(1): 433-442.
47. Ma J, Guarnera MA, Zhou W, et al. A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules. Transl Oncol, 2017, 10(1): 40-45.
48. Xing W, Sun H, Yan C, et al. A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules. BMC Cancer, 2021, 21(1): 263.
49. Wei Q, Fang W, Chen X, et al. Establishment and validation of a mathematical diagnosis model to distinguish benign pulmonary nodules from early non-small cell lung cancer in Chinese people. Transl Lung Cancer Res, 2020, 9(5): 1843-1852.
50. Zhang W, Duan X, Zhang Z, et al. Combination of CT and telomerase+ circulating tumor cells improves diagnosis of small pulmonary nodules. JCI Insight, 2021, 6(11): e148182.
51. 孔德志, 刘傲, 崔健, 等. 临床Ⅰ期非小细胞肺癌诊断模型构建: 基于临床影像学特征联合叶酸受体阳性循环肿瘤细胞检测的研究. 中国胸心血管外科临床杂志, 2021, 28(10): 1192-1201.
52. Yonemori K, Tateishi U, Uno H, et al. Development and validation of diagnostic prediction model for solitary pulmonary nodules. Respirology, 2007, 12(6): 856-862.
53. Pecot CV, Li M, Zhang XJ, et al. Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules. Cancer Epidemiol Biomarkers Prev, 2012, 21(5): 786-792.
54. He X, Xue N, Liu X, et al. A novel clinical model for predicting malignancy of solitary pulmonary nodules: A multicenter study in chinese population. Cancer Cell Int, 2021, 21(1): 115.
55. Du Q, Yu R, Wang H, et al. Significance of tumor-associated autoantibodies in the early diagnosis of lung cancer. Clin Respir J, 2018, 12(6): 2020-2028.
56. Tan M, Ma W, Sun Y, et al. Prediction of the growth rate of early-stage lung adenocarcinoma by radiomics. Front Oncol, 2021, 11: 658138.
57. Liu J, Xu H, Qing H, et al. Comparison of radiomic models based on low-dose and standard-dose CT for prediction of adenocarcinomas and benign lesions in solid pulmonary nodules. Front Oncol, 2021, 10: 634298.
58. Jiao Z, Li H, Xiao Y, et al. Integration of deep learning radiomics and counts of circulating tumor cells improves prediction of outcomes of early stage NSCLC patients treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys, 2022, 112(4): 1045-1054.
59. Maldonado F, Varghese C, Rajagopalan S, et al. Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules. Eur Respir J, 2021, 57(4): 2002485.
60. Kammer MN, Lakhani DA, Balar AB, et al. Integrated biomarkers for the management of indeterminate pulmonary nodules. Am J Respir Crit Care Med, 2021, 204(11): 1306-1316.
61. Kandathil A, Kay FU, Butt YM, et al. Role of FDG PET/CT in the eighth edition of TNM staging of non-small cell lung cancer. Radiographics, 2018, 38(7): 2134-2149.
62. Kang F, Wang S, Tian F, et al. Comparing the diagnostic potential of 68Ga-Alfatide Ⅱand 18F-FDG in differentiating between non-small cell lung cancer and tuberculosis. J Nucl Med, 2016, 57(5): 672-677.
63. Albano D, Gatta R, Marini M, et al. Role of 18 F-FDG PET/CT radiomics features in the differential diagnosis of solitary pulmonary nodules: Diagnostic accuracy and comparison between two different PET/CT scanners. J Clin Med, 2021, 10(21): 5064.
64. Paez R, Shan C, Cords AJ, et al. 18F-FSPG PET imaging for the evaluation of indeterminate pulmonary nodules. PLoS One, 2022, 17(3): e0265427.
65. Zhang F, Wu X, Zhu J, et al. 18F-FDG PET/CT and circulating tumor cells in treatment-naive patients with non-small-cell lung cancer. Eur J Nucl Med Mol Imaging, 2021, 48(10): 3250-3259.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress on liquid biopsy and its combination of radiology in diagnosis of pulmonary nodules

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