A predictive model for recurrence in patients with borderline ovarian tumor based on neural multi-task logistic regression.

ABSTRACT:

Background

Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event prediction, significantly affecting clinical decisions and practice.

Objective

We developed and validated a novel clinical model based on neural multi-task logistic regression (N-MTLR) for predicting recurrence in patients with BOT who underwent initial surgeries, and compared its prediction performance with that of the Cox regression model.

Methods

This retrospective study included 736 patients diagnosed with BOT from May 2011 to August 2022, with 84 recurrences. The synthetic minority oversampling technique (SMOTE) was used to

SUBMITTER: Ye Q 

PROVIDER: S-EPMC11834230 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature