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      Additional Postoperative Radiotherapy Prolonged the Survival of Patients with I-IIA Small Cell Lung Cancer: Analysis of the SEER Database

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          Abstract

          Purpose

          Complete resection and adjuvant chemotherapy are recommended as the standard strategy for patients with stage I-IIA small cell lung cancer (SCLC). However, the role of additional postoperative radiotherapy (PORT) in treatment remains controversial.

          Methods

          Patients with stage I-IIA SCLC undergoing surgery and adjuvant chemotherapy were extracted from the Surveillance, Epidemiology, and End Results database. Stage I-IIA, defined as T1-2N0M0, was recalculated according to the 8th AJCC TNM staging system. Propensity score matching (PSM) was conducted to identify the therapeutic impact of PORT. Univariate Cox hazards regression and least absolute shrinkage and selection operator regression were utilized for primary screening of prognostic variables for I-IIA SCLC disease. A nomogram to predict overall survival (OS) was constructed based on the multivariate Cox proportional hazards model, evaluated with area under the curve, calibration curve, and decision curve analysis, and validated with bootstrap resampling.

          Results

          Our results demonstrated that compared with no PORT, PORT significantly prolonged the median OS (8.58 vs. 5.17 years, HR = 0.61 [0.39–0.96], P = 0.032) and median cancer-specific survival (11.33 vs. 8.08, HR = 0.47 [0.27–0.82], P = 0.0086) after PSM. The 5-year OS rate was 61.56% vs. 46.60%. Five variables including age at diagnosis, gender, T stage, surgical type, and PORT were elucidated to impact on prognosis and included in a nomogram to predict 3-/5-/10-year OS probability. The area under the curve values were 0.72, 0.71, and 0.81, respectively. The nomogram also exhibited satisfactory accuracy and clinical usefulness.

          Conclusion

          PORT was verified to improve the OS of patients with T1-2N0M0 SCLC after surgery and chemotherapy. A prognostic nomogram was developed and validated for OS prediction for these patients.

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          Most cited references23

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          The Effect of Advances in Lung-Cancer Treatment on Population Mortality

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            Brief introduction of medical database and data mining technology in big data era

            Abstract Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.
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              Data mining in clinical big data: the frequently used databases, steps, and methodological models

              Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.
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                Author and article information

                Contributors
                Journal
                J Oncol
                J Oncol
                jo
                Journal of Oncology
                Hindawi
                1687-8450
                1687-8469
                2022
                18 June 2022
                : 2022
                : 6280538
                Affiliations
                1Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
                2Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
                3Tumor Precision Diagnosis and Treatment Technology and Translational Medicine, Hubei Engineering Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China
                4Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China
                5Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
                Author notes

                Academic Editor: Cristina Magi-Galluzzi

                Author information
                https://orcid.org/0000-0003-0874-9772
                https://orcid.org/0000-0003-1900-8478
                https://orcid.org/0000-0001-6406-1011
                https://orcid.org/0000-0002-4805-0459
                https://orcid.org/0000-0001-6623-9864
                Article
                10.1155/2022/6280538
                9233591
                35761902
                f75dd966-c46f-4bc5-a450-55cbcde68d59
                Copyright © 2022 Jiali Li et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 1 March 2022
                : 20 May 2022
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81972852
                Award ID: 81800429
                Funded by: Key Research and Development Project of Hubei Province
                Award ID: 2020BCA069
                Funded by: Young and Middle-Aged Medical Backbone Talents of Wuhan
                Award ID: WHQG201902
                Funded by: Application Foundation Frontier Project of Wuhan
                Award ID: 2020020601012221
                Funded by: Zhongnan Hospital of Wuhan University
                Award ID: ZNJC201922
                Award ID: ZNJC202007
                Categories
                Research Article

                Oncology & Radiotherapy
                Oncology & Radiotherapy

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