Incorporating radiomic MRI models for presurgical response assessment in patients with early breast cancer undergoing neoadjuvant systemic therapy: Collaborative insights from breast oncologists and radiologists.
Authors
Affiliations (5)
Affiliations (5)
- IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20090, Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20090, Italy.
- IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20090, Italy. Electronic address: [email protected].
- IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy.
- Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy.
Abstract
The assessment of neoadjuvant treatment's response is critical for selecting the most suitable therapeutic options for patients with breast cancer to reduce the need for invasive local therapies. Breast magnetic resonance imaging (MRI) is so far one of the most accurate approaches for assessing pathological complete response, although this is limited by the qualitative and subjective nature of radiologists' assessment, often making it insufficient for deciding whether to forgo additional locoregional therapy measures. To increase the accuracy and prediction of radiomic MRI with the aid of machine learning models and deep learning methods, as part of artificial intelligence, have been used to analyse the different subtypes of breast cancer and the specific changes observed before and after therapy. This review discusses recent advancements in radiomic MRI models for presurgical response assessment for patients with early breast cancer receiving preoperative treatments, with a focus on their implications for clinical practice.