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Multiparametric MRI-based Deep Learning and Radiomics for Evaluating Lymph Node Metastasis in Early-Stage Cervical

Yu Hao Bao1, Mei Ling Xiao1,2, Yong Ai Li1

  • 1Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai 201508, China.

Radiology. Imaging Cancer
|April 3, 2026
PubMed
Summary

This study developed radiomics and deep learning-radiomics models to predict lymph node metastasis in early-stage cervical cancer. Both models showed robust diagnostic performance, aiding in preoperative assessment.

Keywords:
CervixDecision AnalysisDeep LearningDiagnosisGenital/ReproductiveLymphatic MetastasisMR ImagingMR–Diffusion Weighted ImagingMagnetic Resonance ImagingMetastasesRadiomicsSegmentationUterine Cervical Neoplasms

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Lymph node metastasis (LNM) is a critical prognostic factor in early-stage cervical cancer.
  • Accurate preoperative prediction of LNM is essential for guiding treatment decisions.

Purpose of the Study:

  • To develop and evaluate multiparametric MRI-based radiomics and deep learning-radiomics (DLR) fusion models for preoperative LNM prediction in early-stage cervical cancer.
  • To compare the diagnostic performance of the radiomics model (Rad_T+LN) and the DLR model (DLR_T+LN).

Main Methods:

  • A multicenter retrospective study involving 862 patients with early-stage cervical cancer.
  • Development of Rad_T+LN and DLR_T+LN models using preoperative MRI data.
  • Validation of models using training, internal testing, and external testing cohorts with evaluation via ROC curves, calibration curves, and decision curve analysis.

Main Results:

  • Both Rad_T+LN and DLR_T+LN models demonstrated robust diagnostic performance for LNM prediction.
  • Rad_T+LN achieved areas under the ROC curve (AUCs) ranging from 0.77 to 0.81 across cohorts.
  • DLR_T+LN showed comparable performance to Rad_T+LN without significant improvement (P > .05) in internal and external testing.

Conclusions:

  • Multiparametric MRI-based radiomics and DLR models exhibit strong diagnostic capabilities for preoperative LNM prediction in early-stage cervical cancer.
  • These models show good calibration and clinical applicability, supporting their potential use in patient management.