A nomogram model of recurrence survival after radical resection of stage T2-T4 lung adenocarcinoma
Objective To analyze the feature recognition of recurrence survival in patients with resectable stage T2-T4 lung adenocarcinoma after radical surgery and construct a nomogram model.Methods The patients who underwent radical resection of lung cancer in Quanzhou First Hospital from January 2019 to December 2022 were analyzed retrospectively.A total of 400 consecutive patients were included.Random serial numbers were generated by Zstats statistical software,and the patients were divided into a training set(n=280)and a validation set(n=120)according to 7∶3.Clinical,pathological,surgical,and follow-up information were recorded,and relapse-free survival(RFS)was recorded as of May 31,2024.Univariate and multivariate Cox proportional regression models were used to analyze the factors affecting RFS in patients with lung adenocarcinoma.Results The median follow-up time of the training set was 14.27 months,and the recurrence rate was 40.36%.The follow-up time of the verification center was 14.10 months,and the recurrence rate was 50.83%.The demographic and clinical characteristics of the training set and the validation set were basically balanced(P>0.05).Univariate Cox regression and LASSO Cox analysis showed that C-reactive protein-albumin ratio(CAR),body mass index(BMI),differentiation degree,tumor size,lymphovascular invasion(LVI),platelet-to-lymphocyte ratio(PLR),and carcinoembryonic antigen(CEA)were the factors influencing postoperative RFS in patients with lung adenocarcinoma.Multivariate Cox regression analysis showed that poorly differentiated/medium-poorly differentiated,LVI and CAR≥1.09×10-3 were independent risk factors for postoperative RFS in lung adenocarcinoma patients.Based on the above results,3-year and 5-year RFS nomogram prediction models were constructed.In the training set,the C-index of the RFS prediction model was 0.783(95%CI:0.744-0.822);in the validation set,each patient in the cohort was scored using the RFS nomogram model,and the C-index was 0.717(95%CI:0.651-0.784).In both the training set and the validation set,the area under the time-dependent subject operating characteristic curve of the predicted RFS within 3 and 5 years was>0.700.Calibration curves in the training set and validation set nomogram showed a high degree of agreement between predicted and observed survival rates.Kaplan-Meier method showed that the RFS of low-risk lung adenocarcinoma patients was significantly longer than that of high-risk patients in both the training set and the validation set.Conclusion In this study,a nomogram model including CAR,differentiation degree and LVI was constructed to predict RFS of lung adenocarcinoma patients after radical surgery.This nomogram model can accurately predict the recurrence survival of patients,and is helpful for appropriate postoperative management of high-risk patients.
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