Pretreatment systemic immune-inflammation index predicts survival for non-metastatic nasopharyngeal carcinoma:two independent institutional studies

Objective:This study aimed to evaluate the prognostic value of the pretreatment systemic immune-inflammation index(SII)in non-metastatic nasopharyngeal carcinoma(NPC).Methods:We retrospectively analyzed the data of 839 patients with non-metastatic NPC recruited from two independent institutions.The training-set cohort and the external validation-set cohort was comprised of 459 and 380 patients from each institution,respectively.The optimal cut-offvalue of SII was determined,and a prognostic risk stratification model was developed based on the training cohort and further assessed in the validation cohort.The propensity score matching(PSM)method was applied to minimize the confounding effects of unbalanced covariables.Results:The optimal cut-offvalue of the SII in the training cohort was 686,which was confirmed using the vali-dation cohort.Multivariate analysis showed that both before and after PSM,SII values>686 were independently associated with worse progression-free survival(PFS)ratio in both cohorts(before PSM,P=0.008 and P=0.008;after PSM,P=0.008 and P=0.007,respectively).Based on the analysis of independent prognostic factors of SII and N stage,we developed a categorical risk stratification model,which achieved significant discrimination among risk indexes associated with PFS and distant metastasis-free survival(DMFS)in the training cohort.There was no significant difference in PFS between RT alone and combined therapies within the low-and intermediate-risk groups(5-year PFS,77.5%vs.75.3%,P=0.275).Patients in the high-risk group who received concurrent chemoradiotherapy experienced superior PFS compared with those who received other therapies(5-year PFS,64.9%vs.40.3%,P=0.003).Conclusion:Pretreatment SII predicts PFS of patients with non-metastatic NPC.Prognostic risk stratification incorporating SII is instructive for selecting individualized treatment.

Predictive function of tumor burden-incorporated machine-learning algorithms for overall survival and their value in guiding management decisions in patients with locally advanced nasopharyngeal carcinoma

Objective:Accurate prognostic predictions and personalized decision-making on induction chemotherapy(IC)for individuals with locally advanced nasopharyngeal carcinoma(LA-NPC)remain challenging.This research examined the predictive function of tumor burden-incorporated machine-learning algorithms for overall survival(OS)and their value in guiding treatment in patients with LA-NPC.Methods:Individuals with LA-NPC were reviewed retrospectively.Tumor burden signature-based OS prediction models were established using a nomogram and two machine-learning methods,the interpretable eXtreme Gradi-ent Boosting(XGBoost)risk prediction model,and DeepHit time-to-event neural network.The models’prediction performances were compared using the concordance index(C-index)and the area under the curve(AUC).The patients were divided into two cohorts based on the risk predictions of the most successful model.The efficacy of IC combined with concurrent chemoradiotherapy was compared to that of chemoradiotherapy alone.Results:The 1221 eligible individuals,assigned to the training(n=813)or validation(n=408)set,showed significant respective differences in the C-indices of the XGBoost,DeepHit,and nomogram models(0.849 and 0.768,0.811 and 0.767,0.730 and 0.705).The training and validation sets had larger AUCs in the XGBoost and DeepHit models than the nomogram model in predicting OS(0.881 and 0.760,0.845 and 0.776,and 0.764 and 0.729,P<0.001).IC presented survival benefits in the XGBoost-derived high-risk but not low-risk group.Conclusion:This research used machine-learning algorithms to create and verify a comprehensive model inte-grating tumor burden with clinical variables to predict OS and determine which patients will most likely gain from IC.This model could be valuable for delivering patient counseling and conducting clinical evaluations.