ArticleJournal of cancer research and clinical oncology2023
Clinical characteristics analysis and prognostic nomogram for predicting survival in patients with second primary prostate cancer: a population study based on SEER database.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- Risk analysis of adverse pathological changes in cT2cN0M0 prostate cancer after robot-assisted radical prostatectomy: Results from a population-based study.Asian journal of urology · 2025Article
- Risk-based screening and prognostic analysis for second primary malignancies in kidney cancer patients: a retrospective cohort study based on large-scale population and Mendelian randomization analysis.International journal of medical sciences · 2025Article
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5 authors at 2 institutions in 1 country.
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Abstract
BACKGROUND AND
aimsSecond primary prostate cancer (SPPCa) is a common type of secondary malignancy that negatively impacts patient prognosis. This study aimed to identify prognostic factors for SPPCa patients and develop nomograms to assess their prognosis.
methodsPatients diagnosed with SPPCa between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The study cohort was randomly divided into a training set and a validation set. Cox regression analysis, Kaplan‒Meier survival analysis, and least absolute shrinkage and selection operator regression analysis were used to identify independent prognostic factors and develop the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curve, area under the curve (AUC), and Kaplan-Meier analysis.
resultsA total of 5342 SPPCa patients were included in the study. Independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) were identified as age, interval between diagnoses, first primary tumor site, and AJCC stage, N stage, M stage, PSA, Gleason score, and SPPCa surgery. Nomograms were constructed based on these prognostic factors, and the performance was evaluated using the C-index (OS: 0.733, CSS: 0.838), AUC, calibration curve, and Kaplan-Meier analysis, which demonstrated excellent predictive accuracy.
conclusionWe successfully established and validated nomograms to predict OS and CSS in SPPCa patients using the SEER database. These nomograms provide an effective tool for risk stratification and prognosis assessment in SPPCa patients, which will aid clinicians in optimizing treatment strategies for this patient population.
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