Risk Stratification of Early-Stage Cervical Cancer with Intermediate-Risk Factors: Model Development and Validation Based on Machine Learning Algorithm.

ABSTRACT:

Background

Adjuvant therapy for patients with cervical cancer (CC) with intermediate-risk factors remains controversial. The objectives of the present study are to assess the prognoses of patients with early-stage CC with pathological intermediate-risk factors and to provide a reference for adjuvant therapy choice.

Materials and methods

This retrospective study included 481 patients with stage IB-IIA CC. Cox proportional hazards regression analysis, machine learning (ML) algorithms, Kaplan-Meier analysis, and the area under the receiver operating characteristic curve (AUC) were used to develop and validate prediction models for disease-free survival (DFS) and overall survival (OS).

Results

A total of 35 (7.3%) patients experienced recurrence, and 20 (4.2%) patients di

SUBMITTER: Chu R 

PROVIDER: S-EPMC8649058 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature