Survival characteristics of Wilms Tumor, a reference developed from a longitudinal cohort study.
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BACKGROUND: Wilms tumor (WT) survival has been affected by the evolution in clinical and biological prognostic factors. Significant differences in survival rates indicate the need for further efforts to reduce these disparities. This study aims to evaluate the clinicopathological data impact on survival among patients after Wilm's diagnosis. METHODS: The study utilized the SEERStat Database to identify Wilms tumor patients, applying SEERStat software version 8.3.9.2 for data extraction. Selection criteria involved specific codes based on the International Classification of Diseases for Oncology (ICDO-3), excluding cases with unknown SEER stage, incomplete survival data, unknown size, or lymph node status. Statistical analyses, including Kaplan-Meier estimates and Cox regression models, were conducted using R software version 3.5. Standardized mortality ratios (SMR) were computed with SEER*Stat software, and relative and conditional survival analyses were performed to evaluate long-term survival outcomes. RESULTS: Of 2273 patients diagnosed with Wilms tumor, (1219 patients, 53.6% were females with an average age group of 3-8 years (50.2%). The overall mean survival after five years of diagnosis was 93.6% (2.6-94.7), and the overall mean survival rate was 92.5% (91.3-93.8) after ten years of diagnosis. Renal cancers were identified as the leading cause of death (77.3%), followed by nonrenal cancers (11%) and noncancer causes (11%). Additionally, robust relative survival rates of 98.10%, 92.80%, and 91.3% at one, five, and ten years, respectively, were observed, with corresponding five-year conditional survival rates indicating an increasing likelihood of survival with each additional year post-diagnosis. Univariate Cox regression identified significant prognostic factors: superior CSS for patients below 3 years (cHR 0.48) and poorer CSS for those older than 15 years (cHR 2.72), distant spread (cHR 10.24), regional spread (cHR 3.09), and unknown stage (cHR 4.97). In the multivariate model, age was not a significant predictor, but distant spread (aHR 9.22), regional spread (aHR 2.84), and unknown stage (aHR 4.98) were associated with worse CSS compared to localized tumors. CONCLUSION: This study delving into WT survival dynamics reveals a multifaceted landscape influenced by clinicopathological variables. This comprehensive understanding emphasizes the imperative for ongoing research and personalized interventions to refine survival rates and address nuanced challenges across age, stage, and tumor spread in WT patients.