A web-based nomogram to predict overall survival for postresection leiomyosarcoma patients with lung metastasis

Area of Science:

• Oncology
• Surgical Oncology
• Biostatistics

Background:

• Leiomyosarcoma (LMS) with lung metastasis presents a significant challenge in predicting patient outcomes.
• Accurate prognostic tools are crucial for tailoring treatment strategies in post-resection LMS patients with lung metastasis.

Purpose of the Study:

• To develop and validate a nomogram for predicting the overall survival of patients with leiomyosarcoma (LMS) who have undergone resection of the primary lesion and have lung metastasis.
• To create a web-based tool for personalized survival prediction in this patient population.

Main Methods:

• Utilized data from the Surveillance, Epidemiology, and End Results (SEER) database (training cohort) and Tianjin Medical University Cancer Hospital & Institute (TJMUCH) (validation cohort).
• Performed univariate and multivariate Cox regression analyses to identify prognostic factors.
• Established and evaluated a nomogram using the area under the curve (AUC) and calibration curves.
• Developed a web-based nomogram for clinical application.

Main Results:

• Identified sex, race, grade, tumor size, chemotherapy, and bone metastasis as significant factors correlated with overall survival in LMS patients with lung metastasis.
• The nomogram demonstrated good predictive performance with C-indices of 0.65 (SEER) and 0.75 (Chinese cohort).
• Achieved AUC values for 1-, 3-, and 5-year survival predictions ranging from 0.646 to 0.689 in the SEER cohort and 0.881 to 0.970 in the Chinese cohort.

Conclusions:

• The developed nomogram is an accurate and personalized tool for predicting overall survival in post-resection LMS patients with lung metastasis.
• The web-based nomogram provides a valuable resource for clinicians to inform treatment decisions and patient counseling.