Identification of prognostic factors and construction of nomogram to predict cancer-specific survival for patients...

Area of Science:

• Gynecologic Oncology
• Cancer Epidemiology
• Biostatistics

Background:

• Ovarian granulosa cell tumors (OGCTs) are rare, indolent malignancies with a propensity for late recurrence.
• Managing recurrent OGCTs presents significant clinical challenges.

Purpose of the Study:

• To identify prognostic factors for cancer-specific survival (CSS) in OGCTs patients.
• To develop and validate a predictive nomogram for CSS in OGCTs.

Main Methods:

• Utilized data from 1459 OGCTs patients from the Surveillance, Epidemiology, and End Results (SEER) database.
• Employed univariate/multivariate Cox regression and LASSO regression to identify significant prognostic factors.
• Constructed and validated a nomogram using Cox proportional hazards models, assessing performance with C-index, calibration plots, and decision curve analysis.

Main Results:

• Identified age, pTNM stage, tumor size, primary tumor surgery, residual disease, and chemotherapy as significant CSS predictors.
• The final nomogram excluded regional lymph node surgery and chemotherapy, demonstrating superior discrimination and calibration compared to the AJCC staging system.

Conclusions:

• A novel, validated prognostic nomogram for OGCTs CSS has been developed.
• The nomogram, available as a stationary tool and web calculator, aids clinicians in personalized treatment planning and prognosis assessment.