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Radiomic MRI Model to Predict Response to Chemoradiotherapy in Locally Advanced Cervical Cancer A Retrospective Multicenter Study Using Pelvic MRI Data

Led by Centre Hospitalier Universitaire de Nīmes · Updated on 2026-01-07

120

Participants Needed

1

Research Sites

21 weeks

Total Duration

AI-Summary

What this Trial Is About

Researchers are investigating a predictive model using radiomic data from pelvic MRI scans to understand the response to combined radiotherapy and chemotherapy in women with locally advanced cervical cancer. This approach aims to predict treatment outcomes early, helping to identify patients who may benefit from alternative therapies. The study focuses on patients with cervical cancer stages IB to IVA, who have undergone chemoradiotherapy, addressing the variability in treatment responses and recurrence rates. The study uses clinical and biological data from the EPICOL cohort, which includes patients treated with chemoradiotherapy at specialized cancer centers. Radiomic analysis is applied to MRI images taken before and after treatment to develop a model predicting progression-free survival and overall survival. This observational cohort study is retrospective, open-label, and involves multiple centers, aiming to validate radiomic markers as prognostic tools. Participants will have their clinical history, MRI images, biopsy samples, and follow-up data reviewed, including at least two years of post-treatment monitoring. Researchers will assess correlations between radiomic features and immune markers such as PD-L1 expression and tumor-infiltrating lymphocytes. The primary outcome is progression-free survival assessed at 24 months, with additional analysis of overall survival and biomarker associations. The study contributes to understanding cervical cancer prognosis and potential personalized treatment strategies.

CONDITIONS

Brief Title

A Radiomic MRI Predictive Model for Response to Concomitant Chemoradiotherapy in Locally Advanced Cervical Cancer

Research Team

F

Frédéric FITENI, Professor

A

Anissa MEGZARI

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