The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …

Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and
multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying …

Background Multiparametric MRI radiomics could distinguish human epidermal growth
factor receptor 2 (HER2)‐positive from HER2‐negative breast cancers. However, its value …

Background Radiomics has been applied for assessing lymphovascular invasion (LVI) in
patients with breast cancer. However, associations between features from peritumoral …

Purpose To evaluate intra-and preitumoral radiomics on the contrast-enhanced T1-weighted
(CE-T1) and T2-weighted (T2W) MRI for predicting the LNM, and develop a nomogram for …

Abstract Objective To build T2WI-based multiregional radiomics for predicting tumor deposit
(TD) and prognosis in patients with resectable rectal cancer. Materials and methods A total …

Purpose To investigate the potential of radiomics signatures (RSs) from intratumoral and
peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively …

Purpose To establish and evaluate multiregional T2-weighted imaging (T2WI)-based clinical-
radiomics model for predicting lymph node metastasis (LNM) and prognosis in patients with …

Purpose To create radiomics signatures based on habitat to assess the instant response in
lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA). Methods …

Purpose The aim of this study is to investigate radiomics features extracted from the optimal
peritumoral region and the intratumoral area on the early phase of dynamic contrast …