Establishment and validation of nomograms to survival probability of lung adenocarcinoma in Chinese population:a large retrospective cohort study based on SEER
Objective To build nomograms with the Surveillance,Epidemiology,and End Results(SEER)database.Then predicting the cancer-specific survival(CSS)of lung adenocarcinoma in Chinese people.Methods A total of 7 940 patients eligible for recruitment between 2000 and 2020 were selected from 17 registries of the SEER database.According to the inclusion and exclusion criteria,3 304 patients were fi-nally enrolled in and randomized(in a 7∶3 ratio)to development sets and validation sets.Nomograms were constructed from variables which screened by univariate or multivariate Cox regression analyses.No-mograms could be evaluated by consistency indices(C-Index),receiver operating characteristic(ROC)curves,calibration curves,decision curve analysis(DCA)and risk stratification Kaplan-Meier survival curve.Results The nomograms were well-structured and well-validated prognostic maps,it constructed from 8 variables:marital status,primary site,clinical stages,T(size of tumour),N(lymph node metas-tasis),surgery,scope of regional lymph node surgery and radiation.The C-Index of the development sets was 0.716(CI:0.702-0.730)and 0.697(CI:0.675-0.719)for the validation sets.At these points in time of 1 year,3 year and 5 year,areas under the ROC curves for the development sets were 0.766,0.808 and 0.858 compare with the validation sets were 0.733,0.789 and 0.816 respectively.The calibration curves indicated ideal consistency between the predicted and observed probabilities,and the decision curve analysis presented a clinically useful model.All individuals were allocated into high-risk groups and low-risk groups based on the median predicted probabilities of the development sets,Kaplan-Meier curve showed there were significant differences in CSS between 2 groups(P＜0.001).Conclusion The research established and validated a prognostic nomogram for CSS in Chinese lung adenocarcinoma patients.Appli-cation of this model in the clinical setting may assist clinicians in evaluating patient prognosis and providing highly individualized therapy.
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