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MRI-Based Model for Personalizing Neoadjuvant Treatment in Breast Cancer.

Wen Li1, Natsuko Onishi1, Jessica E Gibbs1

  • 1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.

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|March 26, 2025
PubMed
Summary
This summary is machine-generated.

Functional tumor volume (FTV) from MRI accurately predicts neoadjuvant chemotherapy (NAC) response in breast cancer. This imaging biomarker aids early surgical decisions for patients with excellent treatment response.

Keywords:
breast cancerdynamic contrast-enhanced MRIfunctional tumor volumeneoadjuvant chemotherapypathologic complete responsetherapy decision

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Functional tumor volume (FTV) from dynamic contrast-enhanced MRI is a key imaging biomarker.
  • FTV predicts treatment response in breast cancer patients receiving neoadjuvant chemotherapy (NAC).
  • FTV models informed early surgical decisions in a large NAC clinical trial.

Purpose of the Study:

  • To construct predictive models using FTV measurements.
  • To analyze performance tradeoffs of probability thresholds for predicting pathology complete response (pCR).
  • To develop individual models within hormone receptor/HER2 (HR/HER2) breast cancer subtypes.

Main Methods:

  • Retrospective analysis of 814 patients from the I-SPY 2 trial (2010-2016).
  • Development of FTV-based predictive models for pCR within HR/HER2 subtypes.
  • Evaluation of probability thresholds to balance positive predictive value (PPV) and sensitivity.

Main Results:

  • The area under the ROC curve (AUC) ranged from 0.68 to 0.74 across subtypes.
  • PPV-sensitivity tradeoffs varied by subtype at different probability thresholds (50%, 70%, 90%).
  • Highest sensitivities were observed in HR-/HER2+ and triple-negative subtypes; lowest in HR+/HER2-.

Conclusions:

  • Imaging biomarkers like FTV can be used in predictive models.
  • Clinically validated probability thresholds enhance model utility.
  • These tools support precision oncology decision-making for breast cancer treatment.