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Prognostic visualization model for primary pulmonary sarcoma: a SEER-based study.

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This study identifies key factors influencing survival in primary pulmonary sarcoma (PPS). A new nomogram predicts overall survival (OS) for patients with this rare cancer, aiding clinical decisions.

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Area of Science:

  • Oncology
  • Thoracic Surgery
  • Biostatistics

Background:

  • Primary pulmonary sarcoma (PPS) is a rare malignancy with a poor prognosis.
  • Limited clinical studies exist, highlighting a need for better understanding of prognostic factors.

Purpose of the Study:

  • To investigate prognostic factors for primary pulmonary sarcoma (PPS).
  • To develop a predictive nomogram for overall survival (OS) in PPS patients.

Main Methods:

  • Retrospective analysis of 169 PPS patients from the SEER database (2010-2019).
  • Univariate and multivariate COX regression analyses were employed.
  • A predictive nomogram was constructed and validated using ROC curves, calibration curves, and DCA plots.

Main Results:

  • Significant prognostic factors identified include age, pathological grade, liver metastasis, surgical intervention, and chemotherapy.
  • The developed nomogram demonstrated good discrimination, accuracy, and clinical utility.
  • Internal and external validation confirmed the model's robustness.

Conclusions:

  • This is the first population-based study to identify prognostic factors for PPS.
  • A novel, validated nomogram can aid in predicting overall survival and guiding clinical management for PPS.
  • The nomogram facilitates personalized treatment strategies and clinical decision-making for primary pulmonary sarcoma.