Analysis of the frontier and hotspot of screening technology for early lung cancer based on bibliometrics_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LI Wei ¹ ,  ZHANG Longhao ¹ , GONG Xiaoqing ¹ , LI Baihong ¹ , PU Jian ¹ , DENG Taibing ² , WEI Ailin ³ ,  LIU Dan ⁴

1. Double First-class Construction Office, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. Department of Respiratory and Critical Care Medicine, West China Guang'an Hospital, Sichuan University, Guang'an, 638001, Sichuan, P. R. China;
3. Department of Science and Education, West China Guang'an Hospital, Sichuan University, Guang'an, 638001, Sichuan, P. R. China;
4. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

ZHANG Longhao, Email: zhanglonghao1990@hotmail.com; LIU Dan, Email: liudanscu@qq.com

Keywords：

Pulmonary nodules; lung cancer; screening; literature visualization; knowledge gragh; Biblioshiny

DOI：

10.7507/1007-4848.202201085

Video：

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Objective To reveal and demonstrate the hotspots and further research directions in screening technology for early lung cancer, and provide references for the future studies. Methods Researches related to lung cancer screening from 2011 to 2021 in the Web of Science database were included. Biblioshiny, a bibliometrics program based on R language, was used to perform content analysis and visualization of the included literature information. Results Researches related to lung cancer screening were increasing year by year. Six major cooperation groups were formed between countries. The current research hotspots in the field of early lung cancer screening technology mainly focused on the multi-directional fusion of radiographic imaging, liquid biopsy and artificial intelligence. Conclusion Low-dose spiral CT screening is still the most important and mainstream method for the screening of early lung cancer at present. The combination and integration of artificial intelligence with various screening methods and the innovation of novel testing and diagnostic equipment are the current research hotspots and the future research trend in this field.

Citation： LI Wei, ZHANG Longhao, GONG Xiaoqing, LI Baihong, PU Jian, DENG Taibing, WEI Ailin, LIU Dan. Analysis of the frontier and hotspot of screening technology for early lung cancer based on bibliometrics. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(11): 1478-1484. doi: 10.7507/1007-4848.202201085 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.	张春芳, 曾强, 王维民, 等. 体检人群肺癌筛查低剂量螺旋CT检出率与成本分析. 中华肿瘤防治杂志, 2015, 22(4): 247-251.
3.	周清华, 范亚光, 王颖, 等. 中国肺癌低剂量螺旋CT筛查指南(2018年版). 中国肺癌杂志, 2018, 21(2): 67-75.
4.	Naidich DP, Marshall CH, Gribbin C, et al. Low-dose CT of the lungs: Preliminary observations. Radiology, 1990, 175(3): 729-731.
5.	Kim JH, Yoon HJ, Lee E, et al. Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: Emphasis on image quality and noise. Korean J Radiol, 2021, 22(1): 131-138.
6.	Khorrami M, Jain P, Bera K, et al. Predicting pathologic response to neoadjuvant chemoradiation in resectable stage Ⅲ non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer, 2019, 135: 1-9.
7.	Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
8.	Amyar A, Modzelewski R, Li H, et al. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput Biol Med, 2020, 126: 104037.
9.	Nam JG, Park S, Hwang EJ, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 2019, 290(1): 218-228.
10.	Beig N, Khorrami M, Alilou M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology, 2019, 290(3): 783-792.
11.	Yedururi S, Morani AC, Katabathina VS, et al. Machine learning and deep learning in oncologic imaging: Potential hurdles, opportunities for improvement, and solutions-abdominal imagers' perspective. J Comput Assist Tomogr, 2021, 45(6): 805-811.
12.	Chabon JJ, Hamilton EG, Kurtz DM, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature, 2020, 580(7802): 245-251.
13.	Wang R, Zhao X, Chen X, et al. Rolling circular amplification (RCA)-assisted CRISPR/Cas9 cleavage (RACE) for highly specific detection of multiple extracellular vesicle microRNAs. Anal Chem, 2020, 92(2): 2176-2185.
14.	Mathios D, Johansen JS, Cristiano S, et al. Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nat Commun, 2021, 12(1): 5060.
15.	Ying L, Du L, Zou R, et al. Development of a serum miRNA panel for detection of early stage non-small cell lung cancer. Proc Natl Acad Sci U S A, 2020, 117(40): 25036-25042.
16.	Sullivan FM, Mair FS, Anderson W, et al. Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging. Eur Respir J, 2021, 57(1): 2000670.
17.	Xie Y, Meng WY, Li RZ, et al. Early lung cancer diagnostic biomarker discovery by machine learning methods. Transl Oncol, 2021, 14(1): 100907.
18.	Wang Y, Luo J, Liu J, et al. Label-free microfluidic paper-based electrochemical aptasensor for ultrasensitive and simultaneous multiplexed detection of cancer biomarkers. Biosens Bioelectron, 2019, 136: 84-90.
19.	中国肺癌防治联盟, 中华医学会呼吸病学分会肺癌学组, 中国医师协会呼吸医师分会肺癌工作委员会. 肺癌筛查与管理中国专家共识. 国际呼吸杂志, 2019, 39(21): 1604-1615.
20.	胡洁, 洪群英. 肺部结节诊治中国专家共识. 中华结核和呼吸杂志, 2015, 38(4): 249-254.
21.	Righettoni M, Amann A, Pratsinis SE. Breath analysis by nanostructured metal oxides as chemo-resistive gas sensors. Mater Today, 2015, 18(3): 163-71.
22.	Yoon JW, Lee JH. Toward breath analysis on a chip for disease diagnosis using semiconductor-based chemiresistors: Recent progress and future perspectives. Lab Chip, 2017, 17(21): 3537-3557.
23.	Saidi T, Moufid M, Beleno-Saenz KD, et al. Non-invasive prediction of lung cancer histological types through exhaled breath analysis by UV-irradiated electronic nose and GC/QTOF/MS. Sens Actuator B-Chem, 2020, 311: 11.
24.	Tai HL, Wang S, Duan ZH, et al. Evolution of breath analysis based on humidity and gas sensors: Potential and challenges. Sens Actuator B-Chem, 2020, 318: 27.
25.	Infante M, Cavuto S, Lutman FR, et al. A randomized study of lung cancer screening with spiral computed tomography: Three-year results from the DANTE trial. Am J Respir Crit Care Med, 2009, 180(5): 445-453.
26.	Blanchon T, Bréchot JM, Grenier PA, et al. Baseline results of the Depiscan study: A French randomized pilot trial of lung cancer screening comparing low dose CT scan (LDCT) and chest X-ray (CXR). Lung Cancer, 2007, 58(1): 50-58.
27.	Ding H, Xia W, Zhang L, et al. CT-based deep learning model for invasiveness classification and micropapillary pattern prediction within lung adenocarcinoma. Front Oncol, 2020, 10: 1186.
28.	Cui S, Ming S, Lin Y, et al. Development and clinical application of deep learning model for lung nodules screening on CT images. Sci Rep, 2020, 10(1): 13657.
29.	Espinoza JL, Dong LT. Artificial intelligence tools for refining lung cancer screening. J Clin Med, 2020, 9(12): 3860.

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. 张春芳, 曾强, 王维民, 等. 体检人群肺癌筛查低剂量螺旋CT检出率与成本分析. 中华肿瘤防治杂志, 2015, 22(4): 247-251.
3. 周清华, 范亚光, 王颖, 等. 中国肺癌低剂量螺旋CT筛查指南(2018年版). 中国肺癌杂志, 2018, 21(2): 67-75.
4. Naidich DP, Marshall CH, Gribbin C, et al. Low-dose CT of the lungs: Preliminary observations. Radiology, 1990, 175(3): 729-731.
5. Kim JH, Yoon HJ, Lee E, et al. Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: Emphasis on image quality and noise. Korean J Radiol, 2021, 22(1): 131-138.
6. Khorrami M, Jain P, Bera K, et al. Predicting pathologic response to neoadjuvant chemoradiation in resectable stage Ⅲ non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer, 2019, 135: 1-9.
7. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
8. Amyar A, Modzelewski R, Li H, et al. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput Biol Med, 2020, 126: 104037.
9. Nam JG, Park S, Hwang EJ, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 2019, 290(1): 218-228.
10. Beig N, Khorrami M, Alilou M, et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology, 2019, 290(3): 783-792.
11. Yedururi S, Morani AC, Katabathina VS, et al. Machine learning and deep learning in oncologic imaging: Potential hurdles, opportunities for improvement, and solutions-abdominal imagers' perspective. J Comput Assist Tomogr, 2021, 45(6): 805-811.
12. Chabon JJ, Hamilton EG, Kurtz DM, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature, 2020, 580(7802): 245-251.
13. Wang R, Zhao X, Chen X, et al. Rolling circular amplification (RCA)-assisted CRISPR/Cas9 cleavage (RACE) for highly specific detection of multiple extracellular vesicle microRNAs. Anal Chem, 2020, 92(2): 2176-2185.
14. Mathios D, Johansen JS, Cristiano S, et al. Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nat Commun, 2021, 12(1): 5060.
15. Ying L, Du L, Zou R, et al. Development of a serum miRNA panel for detection of early stage non-small cell lung cancer. Proc Natl Acad Sci U S A, 2020, 117(40): 25036-25042.
16. Sullivan FM, Mair FS, Anderson W, et al. Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging. Eur Respir J, 2021, 57(1): 2000670.
17. Xie Y, Meng WY, Li RZ, et al. Early lung cancer diagnostic biomarker discovery by machine learning methods. Transl Oncol, 2021, 14(1): 100907.
18. Wang Y, Luo J, Liu J, et al. Label-free microfluidic paper-based electrochemical aptasensor for ultrasensitive and simultaneous multiplexed detection of cancer biomarkers. Biosens Bioelectron, 2019, 136: 84-90.
19. 中国肺癌防治联盟, 中华医学会呼吸病学分会肺癌学组, 中国医师协会呼吸医师分会肺癌工作委员会. 肺癌筛查与管理中国专家共识. 国际呼吸杂志, 2019, 39(21): 1604-1615.
20. 胡洁, 洪群英. 肺部结节诊治中国专家共识. 中华结核和呼吸杂志, 2015, 38(4): 249-254.
21. Righettoni M, Amann A, Pratsinis SE. Breath analysis by nanostructured metal oxides as chemo-resistive gas sensors. Mater Today, 2015, 18(3): 163-71.
22. Yoon JW, Lee JH. Toward breath analysis on a chip for disease diagnosis using semiconductor-based chemiresistors: Recent progress and future perspectives. Lab Chip, 2017, 17(21): 3537-3557.
23. Saidi T, Moufid M, Beleno-Saenz KD, et al. Non-invasive prediction of lung cancer histological types through exhaled breath analysis by UV-irradiated electronic nose and GC/QTOF/MS. Sens Actuator B-Chem, 2020, 311: 11.
24. Tai HL, Wang S, Duan ZH, et al. Evolution of breath analysis based on humidity and gas sensors: Potential and challenges. Sens Actuator B-Chem, 2020, 318: 27.
25. Infante M, Cavuto S, Lutman FR, et al. A randomized study of lung cancer screening with spiral computed tomography: Three-year results from the DANTE trial. Am J Respir Crit Care Med, 2009, 180(5): 445-453.
26. Blanchon T, Bréchot JM, Grenier PA, et al. Baseline results of the Depiscan study: A French randomized pilot trial of lung cancer screening comparing low dose CT scan (LDCT) and chest X-ray (CXR). Lung Cancer, 2007, 58(1): 50-58.
27. Ding H, Xia W, Zhang L, et al. CT-based deep learning model for invasiveness classification and micropapillary pattern prediction within lung adenocarcinoma. Front Oncol, 2020, 10: 1186.
28. Cui S, Ming S, Lin Y, et al. Development and clinical application of deep learning model for lung nodules screening on CT images. Sci Rep, 2020, 10(1): 13657.
29. Espinoza JL, Dong LT. Artificial intelligence tools for refining lung cancer screening. J Clin Med, 2020, 9(12): 3860.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Analysis of the frontier and hotspot of screening technology for early lung cancer based on bibliometrics

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