Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma

Area of Science:

• Oncology
• Surgical Oncology
• Biostatistics

Background:

• Osteosarcoma is a challenging bone cancer, particularly in advanced stages (III and IV).
• Surgical resection of the primary tumor is a cornerstone of treatment, but not all patients experience survival benefits.
• Identifying patients who will benefit from surgery is crucial for optimizing treatment strategies and resource allocation.

Purpose of the Study:

• To develop and validate a predictive nomogram model.
• To identify a subset of patients with stage III and IV osteosarcoma who benefit from primary tumor resection surgery.
• To improve patient selection for surgical intervention.

Main Methods:

• Utilized data from 412 patients in the Surveillance, Epidemiology, and End Results (SEER) database.
• Employed 1:1 propensity score matching (PSM) to balance confounders between surgical and non-surgical groups.
• Constructed a multivariate Cox model and nomograms, validated using ROC curves, calibration curves, and decision curve analysis (DCA).

Main Results:

• A nomogram was developed using age, primary site, and chemotherapy as independent predictors of cancer-specific survival (CSS).
• The nomogram demonstrated good predictive accuracy and clinical utility, as confirmed by ROC, calibration, and DCA.
• Kaplan-Meier analysis confirmed the nomogram's ability to differentiate between surgical beneficiaries and non-beneficiaries.

Conclusions:

• A practical and validated nomogram model can effectively predict surgical benefit in patients with stage III/IV osteosarcoma.
• This tool aids clinicians in selecting appropriate candidates for surgery, potentially improving patient outcomes.
• Further refinement and prospective validation of the nomogram are warranted.