Development Of A Prognostic Model For Limited-stage Small Cell Lung Cancer Based On Prognostic Ability Of Lung Immune Prognostic Index And Clinical Features

Part I Prognostic ability of lung immune prognostic index in Limited-Stage Small Cell Lung Cancer Objective: Lung immune prognostic index(LIPI)is a prognostic marker of extensive-stage small cell lung cancer(ES-SCLC)patients who received immunotherapy or chemotherapy.However,its ability in limited-stage SCLC(LS-SCLC)should be evaluated extensively.Methods:We retrospectively enrolled 497 patients diagnosed as LS-SCLC between 2015 and2018,and clinical data included pretreatment lactate dehydrogenase(LDH),white blood cell count,and absolute neutrophil count levels were collected.According to the LIPI scores,the patients were stratified into low-risk(0 points)and high-risk(1-2 points).The correlations between LIPI and overall survival(OS)or progression-free survival(PFS)were analyzed by the COX regression.Additionally,the propensity score matching(PSM)and inverse probability of treatment weight(IPTW)methods were used to reduce the selection and confounding bias.Results:250 and 247 patients were in the LIPI high-risk group and low-risk groups;their median OS was 14.67 months(95% CI: 12.30-16.85)and 20.53 months(95% CI:17.67-23.39),respectively.In the statistical analysis,high-risk LIPI was significantly against worse OS(HR=1.377,95%CI:1.114-1.702)and poor PFS(HR=1.338,95%CI:1.1-1.626),and the result was similar after matching and compensating with the PSM or IPTW method.Conclusion:LIPI stratification was a significant factor against OS or PFS of LS-SCLC patients.Part II Construction of a prognosis model for limited stage small cell lung cancer incorporating pulmonary immune prognostic indexObjective: To construct and validate a novel prognostic model for limited-stage small cell lung cancer(LS-SCLC)patients based on multiple clinical parameters combined with the LIPI score.Methods: 497 LS-SCLC patients were randomly divided into training and validation groups.The predictive ability of various parameters on overall survival(OS)was determined by single and multivariate COX regression analysis in the training group.The significant prognostic factors were incorporated into a nomogram that included the LIPI score to predict the 1-year,2-year,and 3-year survival rates of LS-SCLC patients after treatment and was validated in the validation group.The performance of the model was evaluated.Using several methods,including 5-fold cross-validation,calibration curve,time-dependent receiver operating characteristic curve(time-ROC),integrated Brier score(IBS),and decision curve analysis(DCA).The survival curve was drawn by the Kaplan-Meier method and compared by the Log-rank test.Results: The training and validation groups consisted of 378 and 119 patients,respectively.The significant prognostic factors in the single and multivariate analysis,including gender,TNM stage,chemotherapy cycle,RT treatment,PCI treatment,and LIPI group,were incorporated into the nomogram to predict the 1-year,2-year,and 3-year survival rates of LS-SCLC patients.The model’s performance was verified in both the training and validation groups.The 5-fold cross-validation showed that the model had good generalization ability,and the calibration curve demonstrated that the predicted survival rates were consistent with the actual observed values.The time-ROC analysis showed that the model had a stable discriminatory ability,with a mean AUC value of 0.76.The IBS score was consistently less than 0.25,indicating that the model can accurately predict prognosis with small error values.The DCA curve showed that the threshold probability range was broad for all time points,with a high clinical net benefit.Furthermore,a risk classification system was developed based on the nomogram.The prognosis of the low-risk group was better than that of the high-risk group in the training group and the validation group.Conclusion: Gender,TNM stage,chemotherapy cycle,RT treatment,PCI treatment,and LIPI group are significant factors affecting the OS of LS-SCLC patients.The newly established LS-SCLC OS prognostic model has the potential to help healthcare workers optimize treatment plans and improve patient prognosis.