Cancer patterns in nasopharyngeal carcinoma multiplex families over 15 years

Kinetics of EBV antibody-based NPC risk scores in Taiwan NPC multiplex families

Nasopharyngeal carcinoma (NPC) risk prediction models based on Epstein-Barr virus (EBV)-antibody testing have shown potential for screening of NPC; however, the long-term stability is unclear. Here, we investigated the kinetics of two EBV-antibody NPC risk scores within the Taiwan NPC Multiplex Family Study. Among 545 participants with multiple blood samples, we evaluated the stability of a 2-marker enzyme-linked immunosorbent assay score and 13-marker multiplex serology score using the intra-class correlation coefficient (ICC) by fitting a linear mixed model that accounted for the clustering effect of multiple measurements per subject and age. We also estimated the clustering of positive tests using Fleiss's kappa statistic. Over an average 20-year follow-up, the 2-marker score showed high stability over time, whereas the 13-marker score was more variable (p < .05). Case-control status is associated with the kinetics of the antibody response, with higher ICCs among cases. Positive tests were more likely to cluster within the same individual for the 2-marker score than the 13-marker score (p < .05). The 2-marker score had an increase in specificity from ~90% for single measurement to ~96% with repeat testing. The 13-marker score had a specificity of ~73% for a single measurement that increased to ~92% with repeat testing. Among individuals who developed NPC, none experienced score reversion. Our findings suggest that repeated testing could improve the specificity of NPC screening in high-risk NPC multiplex families. Further studies are required to determine the impact on sensitivity, establish optimal screening intervals, and generalize these findings to general population settings in high-risk regions.

Prognostic modeling for nasopharyngeal carcinoma (NC) undergoing concurrent chemoradiotherapy using clinical and enhanced MRI-Delta radiomics data: A preliminary study

BACKGROUND

Nasopharyngeal carcinoma (NC) is one of the prevalent malignancies of the head and neck region with poor prognosis.

OBJECTIVE

The aim of this study is to establish a predictive model for assessing NC prognosis based on clinical and MR radiomics data, subsequently to develop a nomogram for practical application.

METHODS

Retrospective analysis was conducted on clinical and imaging data collected between May 2010 and August 2018, involving 211 patients diagnosed with histologically confirmed NC who received concurrent chemoradiotherapy or radical surgery in Xiangyang No. 1 People's Hospital. According to 5-10 years of follow-up results, the patients were divided into two groups: the study group (n= 76), which experienced recurrence, metastasis, or death, and the control group (n= 135), characterized by normal survival. Training and testing subsets were established at a 7:3 ratio, with a predefined time cutoff. In the training set, three prediction models were established: a clinical data model, an imaging model, and a combined model using the integrated variation in clinical characteristics along with MR radiomics parameters (Delta-Radscore) observed before and after concurrent chemoradiotherapy. Model performance was compared using Delong's test, and net clinical benefit was assessed via decision curve analysis (DCA). Then, external validation was conducted on the test set, and finally a nomogram predicting NC prognosis was created.

RESULTS

Univariate analysis identified that the risk factors impacting the prognosis of NC included gender, pathological type, neutrophil to lymphocyte ratio (NLR), degree of tumor differentiation, MR enhancement pattern, and Delta-Radscore (P< 0.05). The combined model established based on the abovementioned factors exhibited significantly higher predictive performance [AUC: 0.874, 95% CI (0.810-0.923)] than that of the clinical data model [AUC: 0.650, 95% CI (0.568-0.727)] and imaging model [AUC: 0.824, 95% CI (0.753-0.882)]. DCA also demonstrated superior clinical net benefit in the combined model, a finding further verified by results from the test set. The developed nomogram, based on the combined model, exhibited promising performance in clinical applications.

CONCLUSION

The Delta-Radscore derived from MR radiomics data before and after concurrent chemoradiotherapy helps enhance the performance of the NC prognostic model. The combined model and resultant nomogram provide valuable support for clinical decision-making in NC treatment, ultimately contributing to an improved survival rate.