The effectiveness of anticancer drugs in treating a solid tumour is dependent on delivery of
the drug to virtually all cancer cells in the tumour. The distribution of drug in tumour tissue …

Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …

Background In this study, we evaluated the ability of radiomic textural analysis of
intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast …

Machine‐learning techniques have led to remarkable advances in data extraction and
analysis of medical imaging. Applications of machine learning to breast MRI continue to …

Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic
biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis …

Like the traditional machine learning, the multi-label learning is faced with the curse of
dimensionality. Some feature selection algorithms have been proposed for multi-label …

Purpose To characterize intratumoral spatial heterogeneity at perfusion magnetic resonance
(MR) imaging and investigate intratumoral heterogeneity as a predictor of recurrence-free …

Background Previous studies have suggested that texture analysis is a promising tool in the
diagnosis, characterization, and assessment of treatment response in various cancer types …

Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and
human epidermal growth factor 2–overexpressing breast cancers, as well as locally …

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown high
sensitivity to diagnose breast cancer. However, few computer-aided algorithms focus on …