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Time-Dependent DCE-MRI Radiomics to Predict Response to Neoadjuvant Therapy in Breast Cancer: A Multicenter Study

Giulia Vatteroni1,2, Riccardo Levi1,2, Paola Nardi1,2

  • 1Department of Biomedical Sciences, Humanitas University, Via R. Montalcini 4, 20072 Pieve Emanuele, Italy.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

This study shows that time-dependent radiomic features from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) can accurately predict neoadjuvant therapy (NAT) response in breast cancer. The model shows strong performance in identifying non-responders, aiding personalized treatment decisions.

Keywords:
MRIbreast cancermachine learningneo-adjuvant therapyradiomicsresponse prediction

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Area of Science:

  • Radiology
  • Oncology
  • Machine Learning

Background:

  • Accurate prediction of neoadjuvant therapy (NAT) response is vital for breast cancer management.
  • Conventional Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) radiomics may miss temporal patterns crucial for understanding tumor heterogeneity.
  • This study explores advanced radiomics for improved NAT response prediction.

Purpose of the Study:

  • To evaluate a machine learning model using time-dependent radiomic features from pre-treatment DCE-MRI for predicting NAT response in breast cancer.
  • To assess the model's ability to differentiate between pathological complete response (pCR), partial response (pPR), and non-response (pNR).

Main Methods:

  • Retrospective collection of breast DCE-MRI data from two centers across multiple vendors.
  • Automatic tumor segmentation using a 3D nnUNet model.
  • Extraction of radiomic features across all DCE phases, with computation of time-dependent features via linear regression.
  • Development of a random forest classifier integrating static and time-dependent features for response prediction.

Main Results:

  • A total of 212 patients were analyzed (173 internal, 39 external).
  • Time-dependent texture features correlated with intratumoral heterogeneity were significantly linked to non-response (pNR).
  • The model achieved high Area Under the Curve (AUC) values, reaching up to 0.95 internally and 0.86 externally for predicting pNR.

Conclusions:

  • Time-dependent radiomic features from pre-treatment DCE-MRI effectively predict NAT response in breast cancer.
  • This approach demonstrates particular strength in identifying patients unlikely to respond to therapy.
  • The findings support the use of advanced imaging biomarkers for risk stratification and personalized breast cancer treatment.