RESUMEN
OBJECTIVE: The prognostic value of tumor size in neuroblastoma (NB) patients has not been fully evaluated. Our purpose is to elucidate the prognostic significance of tumor size in surgery performed on neuroblastoma patients. METHODS: Neuroblastoma patients diagnosed from 2004 to 2015 were selected from the Surveillance, Epidemiology, and End Results Program (SEER) for the study. Univariate and multivariate Cox proportional hazard regression models were used to identify risk factors and the independent prognostic influences of tumor size on NB patients. Overall survival (OS) was analyzed through univariate Cox regression analysis. To determine the optimal cutoff value of tumor size, we first divided the cohort into three groups (≤5 cm, 5-10 cm, >10 cm). Subsequently, the patients were divided into two groups repeatedly, with tumor size at 1 cm intervals. The cutoff value that maximized prognostic outcome difference was selected. Furthermore, we performed the Kaplan-Meier methods to visually present differences in prognosis between the optimal tumor size cutoff value in different subgroups. RESULTS: A total of 591 NB patients who met the inclusion criteria were selected from the SEER database in this study. Cox analysis showed that age >1 year (HR = 2.42, p < 0.0001), originate from adrenal site (HR = 1.7, p = 0.014), distant stage (HR = 6.4, p < 0.0001), undifferentiated grade (HR = 1.94, p = 0.002), and large tumor size (HR = 1.5, p < 0.0001) independently predicted poor prognosis. For tumor size, there were significant differences in tumor size distribution in different ages, tumor grade, disease stage, and primary site subgroup but not in sex, race, and histology subgroup. Furthermore, both univariate (HR = 4.96, 95% CI 2.31-10.63, p < 0.0001) and multivariable analysis (HR = 2.8, 95% CI 1.29-6.08, p < 0.0001) indicated the optimal cutoff value of tumor size was 4 cm for overall survival of NB patients. Using a 4 cm of tumor size cutoff in subgroups, we found that it can identify poor prognosis patients whatever their age or primary site. Interestingly, tumor size of 4 cm cutoff can only identify unfavorable NB patients with diagnosis at distant-stage disease, or differentiated grade tumor, but not with regional and local or undifferentiated tumor. CONCLUSIONS: Tumor size is first to be recognized as a key prognostic factor of neuroblastoma patients and a cutoff value >4 cm might predict poor prognosis, which should be included in the evaluation of prognostic factors for NB.
Asunto(s)
Neuroblastoma , Estudios de Cohortes , Humanos , Neuroblastoma/patología , Pronóstico , Modelos de Riesgos Proporcionales , Factores de Riesgo , Programa de VERF , Tasa de SupervivenciaRESUMEN
The direct qualitative identification of pure liquids in laboratories and in security checks is generally performed by the detection of the refractive index or the permittivity. However, refractive indices are strongly influenced by temperature, while the permittivities of some organics are difficult to differentiate. On the other hand, the quantitative monitoring of samples with high concentration in plating baths and in chemical production lines are generally performed via a "Sampling-Dilution-Analysis" approach because of significant deviations from the linear range at high concentration, which makes the real-time monitoring of concentrated samples difficult. Here, we propose a self-reference analysis (SRA) method to directly analyze pure liquids and concentrated samples based on temperature difference absorption spectra (TDAS) without the need for dilution. This method was performed by simultaneously scanning the spectra of the reference and the sample, which are both obtained from the same analyte for detection but are at different temperatures. Compared to conventional absorption spectra with a blank reference, the red-shifted peak wavelengths of TDAS enable the detection of many far UV absorptive compounds in the near-ultraviolet region (λ > 190 nm). More importantly, organic compounds with similar structures can be easily distinguished. In addition, TDAS can also be used for the quantitative detection of concentrated analytes. The proposed SRA-TDAS method is a rapid and effective method; this approach does not require dilution and utilizes a self-reference, implying the wide potential applicability in security checks, and the real-time monitoring of concentrated compounds in chemical production lines.