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Radiomics for Detecting Metaplastic Histology in Triple-Negative Breast Cancer: A Step Towards Personalized Therapy.

Rana Gunoz Comert1, Gorkem Durak2, Ravza Yilmaz1

  • 1Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul 34093, Turkey.

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Summary
This summary is machine-generated.

This study introduces a multisequence MRI radiomics method to differentiate metaplastic breast cancer (MBC) from other triple-negative breast cancers (TNBC). This approach aids in early diagnosis and personalized treatment selection for breast cancer patients.

Keywords:
Artificial Intelligencemachine learningmetaplastic breast cancerradiomicstriple-negative breast cancer

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Metaplastic breast cancer (MBC) is a rare subtype of triple-negative breast cancer (TNBC).
  • Accurate initial diagnosis is crucial for effective treatment planning in TNBC subtypes.
  • Distinguishing MBC from non-metaplastic TNBC can be challenging at initial diagnosis.

Purpose of the Study:

  • To develop and validate a multisequence MRI-based radiomics approach.
  • To differentiate metaplastic breast cancer (MBC) from non-metaplastic triple-negative breast cancer (TNBC) at initial diagnosis.
  • To facilitate optimal treatment selection for breast cancer patients.

Main Methods:

  • Retrospective analysis of 105 patients (27 MBC, 78 non-metaplastic TNBC) with standardized breast MRI (T1W-CE and STIR sequences).
  • Radiomic feature extraction (214 features) using PyRadiomics and feature selection via LASSO regression.
  • Evaluation of seven machine learning classifiers with five-fold cross-validation and ROC analysis.

Main Results:

  • The combined T1W-CE and STIR MRI radiomics approach achieved superior diagnostic performance (AUC = 0.845, accuracy = 81%).
  • Multisequence analysis outperformed individual sequences (T1W-CE only: AUC = 0.805; STIR only: AUC = 0.768).
  • The method reliably distinguished MBC from non-metaplastic TNBC at initial diagnosis.

Conclusions:

  • Multisequence MRI radiomics offers a reliable method for differentiating MBC from TNBC.
  • This approach can potentially guide more appropriate treatment strategies.
  • It may help avoid ineffective chemotherapy in MBC patients.