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Dynamic Contrast-Enhanced MRI Kinetic Curve-Driven Parametric Radiomics for Predicting Breast Cancer Molecular

Ting Wang1,2, Jing Gong1,2, Simin Wang1,2

  • 1Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.

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This summary is machine-generated.

A new radiomics model using MRI time-intensity curve kinetics accurately predicts breast cancer molecular subtypes. This non-invasive approach aids in classifying subtypes like HER2+ and triple-negative breast cancer.

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Accurate breast cancer molecular subtyping is crucial for treatment selection.
  • Current methods often require invasive procedures or tissue biopsies.
  • Dynamic contrast-enhanced MRI (DCE-MRI) offers a non-invasive imaging modality.

Purpose of the Study:

  • To develop and validate a non-invasive parametric radiomics model for predicting breast cancer molecular subtypes.
  • To utilize time-intensity curve (TIC) kinetics from DCE-MRI for subtype classification.
  • To compare the performance of different radiomics models and a combined approach.

Main Methods:

  • Retrospective analysis of DCE-MRI data from 935 breast cancer patients.
  • Conversion of DCE-MRI images into parametric maps based on wash-in rate (WIR) and TIC area.
  • Extraction and analysis of radiomics features using statistical methods and machine learning (categorical boosting).
  • Development of a TIC-Combined model integrating TIC-WIR and TIC-Area models.

Main Results:

  • The TIC-Combined model demonstrated superior predictive performance in both internal and external validation sets (micro-average AUCs of 0.79 and 0.77, respectively).
  • High subtype-specific classification AUCs were achieved, particularly for triple-negative (0.81) and HER2+ (0.76) breast cancer.
  • The model exhibited good calibration and high interpretability, linking kinetic features to molecular phenotypes.

Conclusions:

  • Interpretable parametric radiomics derived from DCE-MRI TIC kinetics can accurately classify breast cancer molecular subtypes non-invasively.
  • This approach offers a promising tool for personalized treatment strategies in breast cancer management.
  • The study highlights the potential of radiomics in bridging imaging findings with molecular characteristics.