A Prognostic Nomogram for Cervical Cancer after Surgery from SEER Database.

Area of Science:

• Oncology
• Surgical Pathology
• Biostatistics

Background:

• Cervical cancer prognosis prediction after surgery requires refined tools.
• Current staging systems may not fully capture prognostic information.

Purpose of the Study:

• To develop and validate a nomogram for predicting overall survival (OS) in cervical cancer patients.
• To assess the utility of log of odds between positive and negative lymph nodes (LODDS) in prognostic models.

Main Methods:

• Utilized data from 8202 cervical cancer patients from the SEER database (2004-2014).
• Developed a nomogram using demographic and clinicopathological factors, including LODDS, in a training cohort.
• Validated the nomogram's predictive performance in an independent validation cohort.

Main Results:

• Age, race, marital status, tumor grade, FIGO stage, histology, size, and LODDS were significant predictors of OS.
• The developed nomogram demonstrated excellent calibration and a concordance index of 0.749 in the training set.
• The nomogram outperformed the FIGO model in the validation cohort, with concordance indices of 0.786 vs 0.685 for OS.

Conclusions:

• A nomogram incorporating LODDS offers superior prognostic prediction for cervical cancer patients undergoing surgery.
• This novel nomogram may enhance clinical decision-making and patient management strategies.
• The proposed model provides a more accurate alternative to the existing FIGO staging system.