Construction of an artificial intelligence-driven lung cancer database_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

YANG Libing ¹ , GUO Chao ¹ , JIANG Huizhen ² , MA Lian ² ,  LI Shanqing ¹

1. Department of Thoracic Surgery, Peking Union Medical College Hospital, Beijing, 100010, P. R. China;
2. Department of Information, Peking Union Medical College Hospital, Beijing, 100010, P. R. China;

Corresponding author：

LI Shanqing, Email: lishanqing@pumch.cn

Keywords：

Lung cancer database; artificial intelligence; natural language processing; real-world research

DOI：

10.7507/1007-4848.202411061

Video：

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Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

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6.	李杨, 杜雷雷, 许飞, 等. 大数据与人工智能在医学领域的应用进展. 协和医学杂志, 2023, 14(1): 184-189.Li Y, Du LL, Xu F, et al. Big data and artificial intelligence in medicine. Med J Pek Union Med College Hospital, 2023, 14(1): 184-189.
7.	薛彦斌, 齐季瑛, 张子政, 等. 上海糖尿病临床专病大数据库建设与真实世界研究. 上海交通大学学报(医学版), 2023, 43(9): 1145-1152.Xue YB, Qi JY, Zhang JZ, et al. Construction of Shanghai Diabetes Clinical Database and real-world study. J Shanghai Jiaotong University (Medical Science), 2023, 43(9): 1145-1152.
8.	Casey JA, Schwartz BS, Stewart WF, et al. Using electronic health records for population health research: A review of methods and applications. Annu Rev Public Health, 2016, 37: 61-81.
9.	Li P, Rao X, Blase J, et al. "CleanML: A study for evaluating the impact of data cleaning on ml classification tasks. " Proceedings of the 37th IEEE International Conference on Data Engineering (ICDE), 2021, pp. 13-24.
10.	Li T, Sahu AK, Talwalkar A, et al. Federated lerning: Challenges, mthods, and future directions. IEEsE Signal Processing Magazine, 2020, 37(3): 50-60.
11.	Huang J, Yang DM, Rong R, et al. A critical assessment of using ChatGPT for extracting structured data from clinical notes. NPJ Digit Med, 2024, 7(1): 106.
12.	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.
13.	Zengin T, Önal-Süzek T. Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma. BMC Bioinformatics, 2020, 21(Suppl 14): 368.

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. Howlader N, Noone AM, Krapcho M, et al . Chief editror. SEER Cancer Statistics Review, 1975-2018, National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2018/, based on November 2020 SEER data submission, posted to the SEER web site, April 2021.
3. Wang X, Liang Y, Wang Y, et al. Outcomes and prognosis of non-small cell lung cancer patients who underwent curable surgery: A protocol for a real-world, retrospective, population-based and nationwide Chinese National Lung Cancer Cohort (CNLCC) study. BMJ Open, 2023, 13(6): e070188.
4. 李国良, 周煊赫, 孙佶, 等. 基于机器学习的数据库技术综述. 计算机学报, 2020, 43(11): 2019-2049.Li GL, Zhou XH, Sun J, et al. A survey of machine learning based database techniques. Chin J Comput, 2020, 43(11): 2019-2049.
5. Chapman WW, Nadkarni PM, Hirschman L, et al. Overcoming barriers to NLP for clinical text: The role of shared tasks and the need for additional creative solutions. J Am Med Inform Assoc, 2011, 18(5): 540-543.
6. 李杨, 杜雷雷, 许飞, 等. 大数据与人工智能在医学领域的应用进展. 协和医学杂志, 2023, 14(1): 184-189.Li Y, Du LL, Xu F, et al. Big data and artificial intelligence in medicine. Med J Pek Union Med College Hospital, 2023, 14(1): 184-189.
7. 薛彦斌, 齐季瑛, 张子政, 等. 上海糖尿病临床专病大数据库建设与真实世界研究. 上海交通大学学报(医学版), 2023, 43(9): 1145-1152.Xue YB, Qi JY, Zhang JZ, et al. Construction of Shanghai Diabetes Clinical Database and real-world study. J Shanghai Jiaotong University (Medical Science), 2023, 43(9): 1145-1152.
8. Casey JA, Schwartz BS, Stewart WF, et al. Using electronic health records for population health research: A review of methods and applications. Annu Rev Public Health, 2016, 37: 61-81.
9. Li P, Rao X, Blase J, et al. "CleanML: A study for evaluating the impact of data cleaning on ml classification tasks. " Proceedings of the 37th IEEE International Conference on Data Engineering (ICDE), 2021, pp. 13-24.
10. Li T, Sahu AK, Talwalkar A, et al. Federated lerning: Challenges, mthods, and future directions. IEEsE Signal Processing Magazine, 2020, 37(3): 50-60.
11. Huang J, Yang DM, Rong R, et al. A critical assessment of using ChatGPT for extracting structured data from clinical notes. NPJ Digit Med, 2024, 7(1): 106.
12. 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.
13. Zengin T, Önal-Süzek T. Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma. BMC Bioinformatics, 2020, 21(Suppl 14): 368.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

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