Harnessing unsupervised machine learning with [18F]FDG PET/CT to develop a composite model for predicting overall survival in cervical cancer patients undergoing concurrent chemoradiotherapy

Harnessing unsupervised machine learning with [18F]FDG PET/CT to develop a composite model for predicting overall survival in cervical cancer patients undergoing concurrent chemoradiotherapy

Background and purpose:This study sought to develop an advanced composite model to enhance the prognostic accuracy for cervical cancer patients undergoing concurrent chemoradiotherapy (CCRT). The model integrated imaging features from [18F]FDG PET/CT scans with inflammatory markers using a novel uns...

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Bibliographic Details
Main Authors: Jinyu Shi, Lian Wang, Min Zhou, Shushan Ge, Bin Zhang, Jiangqin Han, Jihui Li, Shengming Deng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Oncology
Subjects:
PET/CT
cervical cancer
unsupervised machine learning
concurrent chemoradiotherapy (CCRT)
prognostic prediction
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1486654/full
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