Purpose: Systemic immune-inflammation index (SII) has been demonstrated to be closely associated with the poor prognosis of nasopharyngeal carcinoma (NPC). However, the role of SII during treatment of NPC has not been reported. This study aimed to determine the prognostic value of SII during treatment for NPC patients. Methods: A total of 759 patients diagnosed with NPC were included in this retrospective study (393 in training cohort and 366 in validation cohort). The correlation between variables was analyzed by the chi-squared test, the Fisher’s exact test or the likelihood test. Kaplan–Meier method and log-rank test were used to analyze progression-free survival (PFS) and overall survival (OS). The independent prognostic factors were determined by multivariate analysis of Cox proportional hazards regression model. The uncontrolled risk was analyzed by Logistic regression. Receiver operating characteristic (ROC) curves were used to assess prognostic value. Results: The optimal cut-off point for the SII during treatment was 937.32. High SII during treatment group had higher uncontrolled risk than low SII during treatment group (p = 0.008). In multivariate Cox proportional hazard models analysis, SII during treatment was an independent prognostic factor for 5-year PFS (p < 0.001) and 5-year OS (p < 0.001). All results were found in the training cohort and confirmed in the validation cohort. Conclusions: The SII during treatment is a promising indicator of predicting the survival in NPC patients, especially the risk of uncontrolled occurrence. By monitoring the SII during treatment, it is possible to better evaluate the treatment effect and formulate personalized treatment.

Coordinate Measuring Machines that provide ultraprecise results are widely used in precision manufacturing. However, the traditional point-by-point measuring approaches are time-consuming, limiting their applications in modern manufacturing, especially for large-scale surface measurements and online measurements. In this paper, we propose an efficient machining error evaluation approach for complex surfaces based on the neural process. Without sacrificing accuracy, the proposed method improves the efficiency of measurement by sparse sampling and reconstruction. Using the neural process, the reconstruction algorithm models the machining error accurately and robustly. Besides, by estimating the reconstruction uncertainty, we further improve the sampling efficiency by introducing adaptive sampling. Intensive experiments show that the proposed method can achieve accurate reconstruction with high efficiency compared to existing approaches.