A novel nomogram and prognostic factor for metastatic soft tissue sarcoma survival

Area of Science:

• Oncology
• Medical Statistics

Background:

• Metastatic soft tissue sarcoma (mSTS) treatment has evolved with immune checkpoint inhibitors.
• Predictive models for mSTS survival are crucial for personalized medicine.

Purpose of the Study:

• To develop and validate predictive survival nomograms for mSTS patients.
• To identify independent prognostic factors for overall survival (OS) in mSTS.
• To aid clinical decision-making and risk stratification in mSTS management.

Main Methods:

• Utilized the Surveillance, Epidemiology, and End Results (SEER) database (2016-2022) for 3078 mSTS patients.
• Employed Kaplan-Meier analysis, Cox regression, and logistic regression for factor identification.
• Constructed and validated nomograms using training (n=1846) and validation (n=1232) cohorts, assessing with AUC, calibration curves, and DCA.

Main Results:

• Identified age, race, marital status, pathological grade, histologic subtype, surgery, and chemotherapy as independent prognostic factors.
• Nomograms demonstrated good predictive accuracy (C-index: 0.722 training, 0.716 validation) and excellent calibration.
• Decision curve analysis confirmed favorable clinical utility for risk stratification and treatment guidance.

Conclusions:

• This study presents the first predictive survival nomograms for mSTS patients in the era of immune checkpoint inhibitors.
• The developed nomograms offer valuable insights for follow-up management and clinical decision-making.
• These tools enhance prediction accuracy and assist in identifying high-risk mSTS patients.