Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study.

ABSTRACT:

Purpose

This study was designed to determine the diagnostic performance of fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics-based machine learning (ML) in the classification of cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC).

Methods

Pretreatment ¹⁸F-FDG PET/CT data were retrospectively collected from patients who were diagnosed with locally advanced cervical cancer at two centers. Radiomics features were extracted and selected by the Pearson correlation coefficient and least absolute shrinkage and selection operator regression analysis. Six ML algorithms were then applied to establish models, and the best-performing classifier was selected based on accuracy, sensitivity, speci

SUBMITTER: Liu H 

PROVIDER: S-EPMC11442173 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature