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Related Experiment Video

Updated: Jun 28, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

AI-assisted Radiomic Model for Cervical Cancer Recurrence Prediction: A Multicenter Retrospective Study with

Shuqing Chen1, Yu Zhang2,3, Dong Chen2

  • 1Department of Radiology, Funan County People's Hospital, Fuyang, Anhui, PR China.

Radiology. Imaging Cancer
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study developed a clinical-radiomic nomogram using MRI features and biomarkers to predict cervical cancer recurrence, offering accurate, noninvasive prognosis for patients. The model identified key prognostic factors and a biological pathway, improving disease-free survival prediction.

Area of Science:

  • Oncology
  • Radiology
  • Biomedical Engineering

Background:

  • Cervical cancer recurrence poses a significant challenge in patient management.
  • Accurate prediction of postoperative disease-free survival (DFS) is crucial for personalized treatment strategies.
  • Existing prognostic models may not fully integrate diverse data types for comprehensive prediction.

Purpose of the Study:

  • To develop and validate a nomogram model for predicting postoperative DFS in cervical cancer patients.
  • To incorporate clinical parameters, hematologic inflammatory biomarkers, and MRI radiomic features into the predictive model.
  • To explore the underlying biologic mechanism of the identified radiomic signature.

Main Methods:

  • A multicenter retrospective study of 804 cervical cancer patients (2016-2023).
Keywords:
Cervical CancerInflammatory MarkersMRIMachine LearningPrognosis and PredictionRadiomicsRecurrence

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Last Updated: Jun 28, 2026

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  • Extraction of 3D radiomic features from pretreatment MRI tumor and peritumoral regions.
  • Development of a nomogram using multivariable Cox regression, integrating clinical data, inflammatory markers, and a machine learning-derived radiomics score (Radscore).
  • Bioinformatic analysis and in vitro experiments for biologic mechanism validation.

    • Main Results:

    • The integrated clinical-radiomic nomogram demonstrated high predictive performance for 1-, 3-, and 5-year DFS in the external test set (AUCs ranging from 0.84 to 0.93).
    • International Federation of Gynecology and Obstetrics stage, squamous cell carcinoma antigen, systemic inflammation response index, and Radscore were independent prognostic factors.
    • A TRIM29-cell cycle regulatory axis was identified as the radiomic signature's biologic mechanism.

    Conclusions:

    • The developed integrated clinical-radiomic nomogram provides accurate, noninvasive prediction of postoperative recurrence in cervical cancer.
    • This tool can aid in risk stratification and personalized management of cervical cancer patients.
    • The study elucidates a potential biological pathway linked to radiomic features, offering insights for future therapeutic targets.