ArticleCancer causes & control : CCC2024
Construction, validation, and visualization of a web-based nomogram to predict overall survival in small-cell lung cancer patients with brain metastasis.
Article in Cancer causes & control : CCC, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Development and validation of nomograms for predicting survival in small cell lung cancer patients with brain metastases: a SEER population-based analysis.American journal of translational research · 2024Article
- Machine learning-based prognostic models and factors influencing the benefit of surgery on primary lesion for patients with lung cancer brain metastases.American journal of cancer research · 2024Article
- Interpretable Machine Learning for Survival Prediction in Small Cell Lung Cancer Patients With Brain Metastases: A Population-Based Study With External Validation.Cancer control : journal of the Moffitt Cancer CenterArticle
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4 authors.
Funding
Abstract
introductionBrain 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.
methodsWe 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.
resultsA 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.
conclusionsIn 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.
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