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Lymph Node Metastases Prediction in Cervical Cancer Using Time-Dependent Diffusion MRI and Macromolecular Proton

Nan Meng1,2, Jiayin Pan1, Wei Wei1,2

  • 1Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou 450000, China.

Radiology. Imaging Cancer
|February 27, 2026
PubMed
Summary

Time-dependent diffusion MRI (Td-dMRI) and macromolecular proton fraction (MPF) mapping effectively distinguish cervical cancer with lymph node metastasis (LNM). A combination of cellularity, tumor diameter, and MPF showed the best diagnostic performance for LNM prediction.

Keywords:
Cervical CancerLymph Node MetastasesMacromolecular Proton FractionTime-Dependent Diffusion MRI

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Area of Science:

  • Radiology and Oncological Imaging
  • Biomedical Engineering
  • Medical Physics

Background:

  • Cervical cancer staging requires accurate assessment of lymph node metastasis (LNM).
  • Current imaging techniques have limitations in differentiating LNM in cervical cancer.
  • Novel quantitative MRI techniques may improve diagnostic accuracy.

Purpose of the Study:

  • To evaluate the efficacy of time-dependent diffusion MRI (Td-dMRI) and macromolecular proton fraction (MPF) mapping in distinguishing cervical cancer with and without LNM.
  • To identify quantitative MRI parameters that are independent predictors of LNM.
  • To develop and assess a composite diagnostic tool for LNM detection.

Main Methods:

  • Prospective study involving adults with suspected cervical cancer undergoing Td-dMRI, MPF mapping, and pulsed gradient spin-echo diffusion-weighted imaging (DWIPGSE).
  • Calculation of Td-dMRI parameters (cellularity, diameter, Vin, Dex), MPF, and ADCPGSE.
  • Ridge and logistic regression analyses were used to identify predictors and develop a diagnostic tool, with performance evaluated by ROC analysis.

Main Results:

  • LNM-positive tumors exhibited higher cellularity, Vin, and MPF, and lower diameter, Dex, and ADCPGSE compared to LNM-negative tumors (P < .001 to P = .007).
  • Cellularity, maximum tumor diameter, and MPF were identified as independent predictors of LNM status.
  • The combination of these three parameters achieved the highest diagnostic performance (AUC, 0.95), outperforming individual modalities and parameters.

Conclusions:

  • Td-dMRI and MPF mapping are effective quantitative imaging tools for predicting LNM in cervical cancer.
  • A composite diagnostic tool integrating cellularity, maximum tumor diameter, and MPF demonstrates superior performance for LNM detection.
  • These advanced MRI techniques hold promise for improving preoperative staging of cervical cancer.