Abstract 791P
Background
The factors affecting prognosis and survival for infantile malignant solid tumors (IMST) have yet to be clearly defined. This study aims to identify key prognostic factors and establish a prognostic nomogram for personalized survival prediction.
Methods
A retrospective analysis was performed on IMST patients from 2000 to 2019, using the Surveillance, Epidemiology, and End Results (SEER) database. Prognostic factors were identified through univariate and multivariate analyses. A nomogram was constructed based on Cox regression outcomes. Validation was carried out using the consistency index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis (DCA).
Results
The study included 2,872 patients diagnosed with IMST, divided into training and validation groups in a 7:3 ratio. The one-year, three-year, and five-year overall survival (OS) were 88.7%, 84.6%, and 83.4%, respectively. Neuroblastoma was the most prevalent, with a five-year OS of 92.5%. Infants with retinoblastoma had the highest five-year OS (96.9%), while those with central nervous system tumors had the lowest (55.6%). Significant predictors of IMST included the International Classification of Childhood Cancer (ICCC), race, primary site, laterality, tumor size, SEER stage, surgery, and chemotherapy. The nomogram incorporating all eight factors was more accurate in predicting OS compared to a model using only ICCC, SEER stage, and clinical factors. The C-indexes for the training and validation cohorts were 0.835 and 0.828, respectively, demonstrating strong discriminative ability. Calibration plots closely aligned with the diagonal, indicating observed OS was nearly identical to predicted OS. DCA suggested the nomogram had greater clinical relevance compared to the summary stage. A web-based application (https://cancernomo.shinyapps.io/DynNomapp/) has been developed to facilitate the nomogram's use.
Conclusions
A novel, well-validated nomogram has been developed to predict personalized OS in IMST. This tool exhibits outstanding predictive accuracy and can help doctors and parents calculate individualized survival rates and formulate treatment plans.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
This work was funded by Startup Fund for scientific research, Fujian Medical University (Grant number: 2021QH1168), Scientific research project of Fujian Children's Hospital(Grant number: ETK2023001), and Natural Science Foundation of Fujian Province (Grant number: 2023J011300).
Disclosure
All authors have declared no conflicts of interest.