ArticleDiscover oncology2025
Developing and validating a nomogram of suicide risk in lung cancer patients based on the SEER database.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis study sought to determine independent predictors of suicide among patients with lung cancer and to establish a dynamic nomogram to predict suicide risk, with the ultimate goal of enabling early recognition and prevention in high-risk populations.
methodsWe analyzed 188,147 lung cancer cases from the most recent Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) to identify predictors of suicide risk and develop a predictive model. The cohort was randomized into a training and validation group. Validation consisted of assessing the consistency index (C-index), subject-operating characteristic curves (ROC), and calibration curves.
resultsA total of 188,147 eligible lung cancer patients were enrolled and randomized into the training and validation groups. Multifactorial Cox regression analysis showed that age, race, sex, grade, and marital were independent predictors of suicide in lung cancer patients. The accuracy of the nomogram was evaluated by C-index and ROC curves, which showed acceptable performance on both training and validation sets. In addition, calibration plots showed that nomograms containing all these factors showed strong predictive accuracy.
conclusionThis study identified key risk factors for suicide in lung cancer patients and developed a dynamic nomogram that provided individualized risk predictions based on multifactorial clinical and pathological parameters. The model offered a superior alternative to traditional static risk scales, enabling early identification of high-risk individuals to support timely prevention and intervention. Furthermore, it has been implemented as a web-based visual tool, allowing convenient, real-time, and personalized suicide risk assessment. By facilitating early detection and intervention, this approach can help clinicians improve decision-making efficiency and provide more precise, patient-centered care, ultimately contributing to better psychological outcomes and quality of life for lung cancer patients.
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