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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Construction, validation, and visualization of a web-based nomogram to predict overall survival in small-cell lung
Min Liang1, Mafeng Chen2, Shantanu Singh3
1Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, China. imtuaska@163.com.
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.
