Abstract
Introduction
Brain metastasis (BM) is an aggressive complication with an extremely poor prognosis in patients with small-cell lung cancer (SCLC). A well-constructed prognostic model could help in providing timely survival consultation or optimizing treatments.
Methods
We analyzed clinical data from SCLC patients between 2000 and 2018 based on the Surveillance, Epidemiology, and End Results (SEER) database. We identified significant prognostic factors and integrated them using a multivariable Cox regression approach. Internal validation of the model was performed through a bootstrap resampling procedure. Model performance was evaluated based on the area under the curve (AUC) and calibration curve.
Results
A total of 2,454 SCLC patients' clinical data was collected from the database. It was determined that seven clinical parameters were associated with prognosis in SCLC patients with BM. A satisfactory level of discrimination was achieved by the predictive model, with 6-, 12-, and 18-month AUC values of 0.726, 0.707, and 0.737 in the training cohort; and 0.759, 0.742, and 0.744 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. Furthermore, prognostic scores were found to significantly alter the survival curves of different risk groups. We then deployed the prognostic model onto a website server so that users can access it easily.
Conclusions
In this study, a nomogram and a web-based predictor were developed to predict overall survival in SCLC patients with BM. It may assist physicians in making informed clinical decisions and determining the best treatment plan for each patient.
Similar content being viewed by others
Data availability
The datasets generated and analyzed during the current study are available in the Surveillance, Epidemiology, and End Results (SEER) repository[https://seer.cancer.gov/data/].
Abbreviations
- SCLC:
-
Small-cell lung cancer
- BM:
-
Brain metastasis
- AUC:
-
Area under the curve
- SEER:
-
Surveillance, Epidemiology, and End Results
- AJCC:
-
American Joint Committee on Cancer
- VALSG:
-
Veterans Administration Lung Study Group
- NCI:
-
National Cancer Institute
- ROC:
-
Receiver operating characteristic
- DCA:
-
Decision curve analysis
- OS:
-
Overall survival
- PCI:
-
Prophylactic cranial irradiation
References
GBD 2019 Cancer Risk Factors Collaborators (2022) The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 400: 563–91.
Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66:7–30
Stella GM, Corino A, Berzero G, Kolling S, Filippi AR, Benvenuti S (2019) Brain metastases from lung cancer: is MET an actionable target? Cancers (Basel) 11:271
Govindan R, Page N, Morgensztern D, Read W, Tierney R, Vlahiotis A et al (2006) Changing epidemiology of small-cell lung cancer in the United States over the last 30 years: analysis of the surveillance, epidemiologic, and end results database. J Clin Oncol 24:4539–4544
Kalemkerian GP (2016) Small cell lung cancer. Semin Respir Crit Care Med 37:783–796
Zeng H, Zheng D, Witlox WJA, Levy A, Traverso A, Kong FS et al (2022) Risk factors for brain metastases in patients with small cell lung cancer: a systematic review and meta-analysis. Front Oncol 12:889161
Reddy SP, Dowell JE, Pan E (2020) Predictors of prognosis of synchronous brain metastases in small-cell lung cancer patients. Clin Exp Metastasis 37:531–539
Xiao HF, Zhang BH, Liao XZ, Yan SP, Zhu SL, Zhou F et al (2017) Development and validation of two prognostic nomograms for predicting survival in patients with non-small cell and small cell lung cancer. Oncotarget 8:64303–64316
Pan H, Shi X, Xiao D, He J, Zhang Y, Liang W et al (2017) Nomogram prediction for the survival of the patients with small cell lung cancer. J Thorac Dis 9:507–518
Wang T, Lu R, Lai S, Schiller JH, Zhou FL, Ci B et al (2019) Development and validation of a nomogram prognostic model for patients with advanced non-small-cell lung cancer. Cancer Inform 18:1176935119837547
Shan Q, Shi J, Wang X, Guo J, Han X, Wang Z et al (2021) A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study. BMC Cancer 21:640
Qiu J, Ke D, Yu Y, Lin H, Zheng Q, Li H et al (2022) A new nomogram and risk stratification of brain metastasis by clinical and inflammatory parameters in stage III small cell lung cancer without prophylactic cranial irradiation. Front Oncol 12:882744
Li N, Chu Y, Song Q (2021) Brain metastasis in patients with small cell lung cancer. Int J Gen Med 14:10131–10139
Liang M, Chen M, Singh S, Singh S, Zhou C (2023) A visualized dynamic prediction model for overall survival in patients diagnosed with brain metastases from lung squamous cell carcinoma. Clin Respir J 17:556
George J, Lim JS, Jang SJ, Cun Y, Ozretić L, Kong G et al (2015) Comprehensive genomic profiles of small cell lung cancer. Nature 524:47–53
Peifer M, Fernández-Cuesta L, Sos ML, George J, Seidel D, Kasper LH et al (2012) Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet 44:1104–1110
Balachandran VP, Gonen M, Smith JJ, DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. Lancet Oncol 16:e173–e180
Lv B, Hu L, Fang H, Sun D, Hou Y, Deng J et al (2021) Development and validation of a nomogram incorporating colloid osmotic pressure for predicting mortality in critically Ill neurological patients. Front Med (Lausanne) 8:765818
Ma J, Deng Y, Lao H, Ouyang X, Liang S, Wang Y et al (2021) A nomogram incorporating functional and tubular damage biomarkers to predict the risk of acute kidney injury for septic patients. BMC Nephrol 22:176
Hu L, Nie Z, Zhang Y, Zhang Y, Ye H, Chi R et al (2019) Development and validation of a nomogram for predicting self-propelled postpyloric placement of spiral nasoenteric tube in the critically ill: Mixed retrospective and prospective cohort study. Clin Nutr 38:2799–2805
Chen W, Sun C, Wei R, Zhang Y, Ye H, Chi R et al (2018) Establishing decision trees for predicting successful postpyloric nasoenteric tube placement in critically Ill patients. JPEN J Parenter Enteral Nutr 42:132–138
Rong YT, Zhu YC, Wu Y (2022) A novel nomogram predicting cancer-specific survival in small cell lung cancer patients with brain metastasis. Transl Cancer Res 11:4289–4302
Zeng Q, Li J, Tan F, Sun N, Mao Y, Gao Y et al (2021) Development and validation of a nomogram prognostic model for resected limited-stage small cell lung cancer patients. Ann Surg Oncol 28:4893
National Comprehensive Cancer Network website. National Comprehensive Cancer Network Guidelines for small cell lung cancer. Available at: https://www.nccn.org/professionals/physician_gls/pdf/sclc.pdf. Accessed 17 Jan 2021.
Postmus PE, Kerr KM, Oudkerk M, Senan S, Waller DA, Vansteenkiste J et al (2017) Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. https://doi.org/10.1093/annonc/mdx222
Kalemkerian GP, Loo BW, Akerley W, Attia A, Bassetti M, Boumber Y et al (2018) NCCN guidelines insights: small cell lung cancer, version 2.2018. J Natl Compr Canc Net 16:1171–1182
Acknowledgments
The authors thank Mrs. Yunru Fan and Dr. Alexandra Lam for coordinating and supporting the development and preparation of the manuscript.
Funding
The funding was provided by the High-level Hospital Construction Project of Maoming People's Hospital, the Medical Research Fund of Guangdong Province (#B2022278), the Research Project of Maoming Science and Technology Bureau (Grant No. 2021121), and the Outstanding Young Talents Program of Maoming People's hospital (#SY2021021). This study was supported by the High-level Hospital Construction Project of Maoming People's Hospital.
Author information
Authors and Affiliations
Contributions
Study concepts: M.L and MF.C; Study design: M.L, Data acquisition: M.L, Data analysis and interpretation: S.S and S.S, Statistical analysis: M.L. Manuscript preparation: M.L and Shivank Singh. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
Corresponding author
Ethics declarations
Competing interests
All authors declare that they have no competing interests.
Ethical approval
The ethics committee approved the protocol for the study at Maoming People's Hospital. All authors have signed the SEER Research Data Agreement to protect patient privacy, which aligns with ethical principles.
Research involving human/animal participants
This article was based on open-access databases and does not involve any new research with human participants or animals.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liang, M., Chen, M., Singh, S. et al. Construction, validation, and visualization of a web-based nomogram to predict overall survival in small-cell lung cancer patients with brain metastasis. Cancer Causes Control 35, 465–475 (2024). https://doi.org/10.1007/s10552-023-01805-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10552-023-01805-9