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Risk factors and nomograms for diagnosis and early death in patients with combined small cell lung cancer with distant metastasis: a population-based study. | LitMetric

AI Article Synopsis

  • The study focuses on predicting distant metastasis (DM) and early death in patients with combined small cell lung cancer (CSCLC) using nomograms, which are statistical models that can help in decision-making.
  • It analyzed data from 788 CSCLC patients between 2004 and 2015, identifying key risk factors like sex, tumor site, and treatment methods.
  • The results showed that the developed nomograms were effective in predicting DM and early death, potentially helping doctors tailor treatments for patients.

Article Abstract

Objective: Combined small cell lung cancer (CSCLC) with distant metastasis (DM) is an aggressive disease with a poor prognosis. Effective nomograms are needed to predict DM and early death in patients with CSCLC and DM.

Methods: This retrospective study included patients with CSCLC from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. Risk factors for DM and early death were analyzed by univariate and multivariate logistic regression. Nomograms were constructed based on the results in a training cohort and confirmed in a validation cohort, and their performances were assessed by concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results: A total of 788 patients with CSCLC were selected, including 364 patients with metastatic CSCLC. Sex, tumor site, T stage, and N stage were independent risk factors for DM, while age, surgery, chemotherapy, and liver metastasis were independent risk factors for early death. C-index, ROC, calibration, and DCA curve analyses all showed good predictive performances for both nomograms.

Conclusions: These nomograms could reliably predict DM risk in CSCLC patients and early death in CSCLC patients with DM, and may thus help clinicians to assess these risks and implement individualized therapies.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418558PMC
http://dx.doi.org/10.1177/03000605241238689DOI Listing

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