Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …

Background Accurately predicting the survival rate of breast cancer patients is a major issue
for cancer researchers. Machine learning (ML) has attracted much attention with the hope …

Breast cancer is a significant global health burden, with increasing morbidity and mortality
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …

Simple Summary Nowadays patients affected by locally advanced breast cancer and
particular subtypes of early breast cancer may benefit from neoadjuvant chemotherapy …

Simple Summary The two most common cross-sectional imaging modalities, computed
tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in …

Background There is an increasing need to develop a more accurate prediction model for
pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast …

Significant advances in imaging analysis and the development of high-throughput methods
that can extract and correlate multiple imaging parameters with different clinical outcomes …

Previous studies have shown that the performance of a classifier on imbalanced data
heavily relies on informative objects lying in borderline or overlapping areas. In this study …

Medical imaging techniques, such as mammography, ultrasound and magnetic resonance
imaging, plays an integral role in the detection and characterization of breast cancer …

Purpose The aim of this meta-analysis was to determine the diagnostic accuracy of machine
learning (ML) models with MRI in predicting pathological response to neoadjuvant …