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

Comert R. G., Durak G., Yilmaz R. S., Aktas H. E., Tuz Z., Pan H., ...More

Bioengineering (Basel, Switzerland), vol.12, no.9, 2025 (SCI-Expanded, Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 9
  • Publication Date: 2025
  • Doi Number: 10.3390/bioengineering12090973
  • Journal Name: Bioengineering (Basel, Switzerland)
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, INSPEC, Directory of Open Access Journals
  • Keywords: Artificial Intelligence, machine learning, metaplastic breast cancer, radiomics, triple-negative breast cancer
  • Istanbul University Affiliated: Yes

Abstract

This study aims to develop and validate a multisequence MRI-based radiomics approach for distinguishing metaplastic breast cancer (MBC) from non-metaplastic triple-negative breast cancer (TNBC) at the initial diagnosis, which could facilitate optimal treatment selection. In this retrospective study, we analyzed 105 patients (27 MBC, 78 non-metaplastic TNBC) who underwent standardized breast magnetic resonance imaging (MRI), which included T1-weighted contrast-enhanced (T1W-CE) and short-tau inversion recovery (STIR) sequences. Two radiologists performed ground truth lesion segmentation, verified by a senior radiologist. We extracted 214 radiomic features (using PyRadiomics) and used least absolute shrinkage and selection operator (LASSO) regression for feature selection. Seven machine learning classifiers were thoroughly evaluated using five-fold cross-validation, with performance assessed through ROC analysis and accuracy metrics. The combined T1W-CE and STIR analysis demonstrated superior diagnostic performance for distinguishing MBC from non-metaplastic TNBC (AUC = 0.845; accuracy = 81%) compared with either sequence alone (T1W only AUC = 0.805; accuracy = 80%; STIR only AUC:0.768; accuracy = 77%). Multisequence MRI radiomics can reliably distinguish between MBC and TNBC at the time of initial diagnosis. This could potentially facilitate the selection of more appropriate treatments and help avoid ineffective chemotherapy for MBC patients.