RESUMO
Young breast cancer (YBC) patients often face a poor prognosis, hence it's necessary to construct a model that can accurately predict their long-term survival in early stage. To realize this goal, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) databases between January 2010 and December 2020, and meanwhile, enrolled an independent external cohort from Tianjin Medical University Cancer Institute and Hospital. The study aimed to develop and validate a prediction model constructed using the Random Survival Forest (RSF) machine learning algorithm. By applying the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, we pinpointed key prognostic factors for YBC patients, which were used to create a prediction model capable of forecasting the 3-year, 5-year, 7-year, and 10-year survival rates of YBC patients. The RSF model constructed in the study demonstrated exceptional performance, achieving C-index values of 0.920 in the training set, 0.789 in the internal validation set, and 0.701 in the external validation set, outperforming the Cox regression model. The model's calibration was confirmed by Brier scores at various time points, showcasing its excellent accuracy in prediction. Decision curve analysis (DCA) underscored the model's importance in clinical application, and the Shapley Additive Explanations (SHAP) plots highlighted the importance of key variables. The RSF model also proved valuable in risk stratification, which has effectively categorized patients based on their survival risks. In summary, this study has constructed a well-performed prediction model for the evaluation of prognostic factors influencing the long-term survival of early-stage YBC patients, which is significant in risk stratification when physicians handle YBC patients in clinical settings.
RESUMO
Purpose: To evaluate the longitudinal changes in subfoveal choroidal thickness (SFCT) in children with different refractive status. Methods: A total of 2290 children 3 to 14 years old who attended the first year of kindergarten (G0), first year of primary school (G1), fourth year of primary school (G4), or first year of junior high school (G7) in Guangzhou, China, were recruited and followed up for 2 years. All participants received cycloplegic autorefraction, axial length measurement and SFCT measurement using a CIRRUS HD-OCT device. Children were divided into groups of persistent non-myopia (PNM), persistent myopia (PM), or newly developed myopia (NDM). Children in the PNM and PM groups were further divided into subgroups of stable refraction (absolute mean annual spherical equivalent refraction [SER] change < 0.5 D) and refractive progression (absolute mean annual SER change ≥ 0.5 D). Results: The mean ± SD ages for the G1 to G7 cohorts were 3.89 ± 0.30, 6.79 ± 0.47, 9.71 ± 0.34, and 12.54 ± 0.38, years, respectively. SFCT consistently decreased in the NDM group across the G1 to G7 cohorts (all P < 0.001) and exhibited variability across different age cohorts in the PNM and PM groups. Further subgroup analysis revealed significant thickening of SFCT in the PNM-stable group among the G0, G1, and G7 cohorts (all P < 0.05), whereas it remained stable among all cohorts in the PM-stable group (all P > 0.05). Conversely, SFCT exhibited thinning in the G4 and G7 cohorts in the PM-progressive group (both P < 0.01) and for the entire cohort of children in the PNM-progressive group (P = 0.012). Conclusions: SFCT increased in nonmyopic children with stable refraction, remained stable in myopic children maintained stable refraction, and decreased in those with refractive progression, whether they were myopic or not.