Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is
difficult to quantify what can be seen in an image, and to turn it into valuable predictive …

The goal of this study is to show emerging applications of deep learning technology in
cancer imaging. Deep learning technology is a family of computational methods that allow …

Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases,
such as skin, breast, and lung cancer. AI is an advanced form of technology that uses …

The incorporation of the cloud technology with the Internet of Things (IoT) is significant in
order to obtain better performance for a seamless, continuous, and ubiquitous framework …

Abstract In the recent 5 years (2014–2018), there has been growing interest in the use of
machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic …

Objective To examine the capability of MRI texture analysis to differentiate the primary site of
origin of brain metastases following a radiomics approach. Methods Sixty-seven untreated …

Background To assess the feasibility of texture analysis (TA) based on spectral attenuated
inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the …

Gadolinium-based contrast agents (GBCAs) increase lesion detection and improve disease
characterization for many cerebral pathologies investigated with MRI. These agents …

BACKGROUND AND PURPOSE: The heterogeneity of glioblastoma contributes to the poor
and variant prognosis. The aim of this retrospective study was to assess the glioblastoma …