Constructing a Nomogram for Predicting Pelvic Osteosarcoma: A Retrospective Study Based on the SEER Database and a...

Area of Science:

• Oncology
• Surgical Oncology
• Biostatistics

Background:

• Pelvic osteosarcoma generally has a poorer prognosis than limb osteosarcoma.
• Accurate prognostic prediction is crucial for effective treatment planning in pelvic osteosarcoma.
• Existing prognostic models may not adequately address the specific characteristics of pelvic osteosarcoma.

Purpose of the Study:

• To develop and validate a novel nomogram for predicting the overall survival of patients diagnosed with pelvic osteosarcoma.
• To identify independent prognostic factors influencing survival in pelvic osteosarcoma.
• To improve clinical decision-making and patient management for this rare cancer.

Main Methods:

• Retrospective analysis of clinical data from 62 patients in the SEER database (training cohort) and 31 Chinese patients (validation cohort).
• Kaplan-Meier survival analysis to determine median survival times for various factors.
• Univariate and multivariate Cox regression models to identify independent prognostic factors.
• Construction and validation of a predictive nomogram using ROC curves and calibration plots.

Main Results:

• Key prognostic factors identified include age, race (Asian), tumor size, primary surgery status, lung metastasis, and radiotherapy.
• Multivariate analysis confirmed age, lung metastasis, race (Asian), and tumor size as independent predictors.
• The developed nomogram demonstrated good predictive accuracy in both training (AUCs: 0.81-0.80) and validation (AUCs: 0.67-0.71) cohorts for 1-3 year survival rates.

Conclusions:

• The newly developed nomogram provides a reliable tool for predicting overall survival in patients with pelvic osteosarcoma.
• This predictive model can assist clinicians in making more informed treatment decisions and tailoring patient care.
• Further validation in larger, diverse populations may enhance the nomogram's generalizability.