A novel nomogram and risk classification system predicting the Ewing sarcoma: a population-based study.

Area of Science:

• Oncology
• Biostatistics
• Cancer Research

Background:

• Ewing sarcoma (ES) is a rare bone and soft tissue cancer.
• Existing prognostic models for ES are lacking.
• Accurate survival prediction is crucial for treatment planning.

Purpose of the Study:

• To develop and validate a nomogram for predicting overall survival (OS) in ES patients.
• To establish a risk classification system for stratifying ES patients.
• To provide a tool for individualized prognosis and treatment optimization.

Main Methods:

• Utilized clinicopathological data from 935 ES patients in the SEER database (2010-2018).
• Developed a nomogram using Cox proportional hazard analyses (training/validation sets).
• Evaluated model performance using C-index, ROC, calibration curves, IDI, and NRI.

Main Results:

• A 6-variable nomogram was established with a C-index of 0.788 for OS.
• The nomogram demonstrated good predictive performance and accuracy.
• A risk classification system effectively stratified patients into low, intermediate, and high-risk groups.

Conclusions:

• The developed nomogram and risk system offer a more accurate and convenient tool than traditional staging for ES prognosis.
• This tool can assist clinicians in optimizing treatment strategies for individual ES patients.
• Further validation may enhance its clinical utility in managing Ewing sarcoma.