Magnetic resonance imaging-based artificial intelligence model predicts neoadjuvant therapy response in triple-negative breast cancer
This proof-of-concept study supports the potential for AI-based tools to aid clinical decision-making and reduce the risks associated with ineffective therapies. Future research with larger datasets and additional imaging modalities is needed to improve model generalizability and clinical applicability.
Authors
- Ayşenur Oktay (ORCID: https://orcid.org/0000-0003-0209-5878)
- Raşit Eren Büyüktoka (ORCID: https://orcid.org/0000-0002-6989-6613)
- Ali Murat Koç (ORCID: https://orcid.org/0000-0001-6824-4990)
- Özge Aslan (ORCID: https://orcid.org/0000-0001-6935-1472)
- Aslı Dilara Büyüktoka (ORCID: https://orcid.org/0000-0002-0278-3382)
- Demet Kocatepe Çavdar (ORCID: https://orcid.org/0000-0002-3278-3770)
- Zehra Hilal Adıbelli (ORCID: https://orcid.org/0000-0001-9265-8114)
- Yalçın İşler (ORCID: https://orcid.org/0000-0002-2150-4756)
- Özlem Özdemir (ORCID: https://orcid.org/0000-0003-2520-5953)
- Murat Sürücü (ORCID: https://orcid.org/0000-0002-9889-9952)
- S. Değer (ORCID: https://orcid.org/0009-0008-4288-1850)
Institutions
- Izmir University (TR)
- Izmir Kâtip Çelebi University (TR)
- Ege University (TR)
- Alanya University (TR)
- Sağlık Bilimleri Üniversitesi (TR)
- Burdur Mehmet Akif Ersoy Üniversitesi (TR)
Publication Details
- Journal
- Diagnostic and Interventional Radiology
- Published
- 2025-10-27
- DOI
- https://doi.org/10.4274/dir.2025.253376
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00