Introduction: Advanced quantitative information such as radiomics features derived from magnetic resonance (MR) image may be useful for outcome prediction, prognostic models or response biomarkers in Glioblastoma (GBM). The main aim of this study was to evaluate MRI radiomics features for recurrence prediction in glioblastoma multiform.   Materials and Methods:</str...

BACKGROUND Napkin-ring sign (NRS) is an independent prognostic imaging marker of major adverse cardiac events. However, identification of NRS is challenging because of its qualitative nature. Radiomics is the process of extracting thousands of quantitative parameters from medical images to create big-data data sets that can identify distinct patterns in radiological images. Therefore, we sought...

Introduction: Despite growing interest in the use of magnetic resonance imaging (MRI) in the external radiotherapy design process (RT), Computer Tomography (CT) remains a gold standard and is regarded as a basic imaging modality in radiotherapy. MRI shows the high contrast in soft tissues without any radiation exposure to patients. As a result, MRI is used in functional tissue ...

Standard imaging cannot assess the pathology details of intrahepatic cholangiocarcinoma (ICC). We investigated whether CT-based radiomics may improve prediction tumor characteristics. All consecutive patients undergoing liver resection for ICC (2009-2019) in six high-volume centers were evaluated inclusion. On preoperative CT, we segmented (Tumor-VOI, i.e., volume-of-interest) and a 5-mm parenc...

Abstract AIMS Radiomics converts routinely obtained medical imaging into mineable, high-dimensionality data. We assessed its utility for incorporation a multivariate model to predict the likelihood of response brain metastases (BMs) stereotactic radiotherapy (SRT). METHOD Patients treated with SRT over ten-year period BMs at three tertiary centres were retrospectively analyzed. planning MRI sca...

As a vital task in cancer therapy, accurately predicting the treatment outcome is valuable for tailoring and adapting a treatment planning. To this end, multi-sources of information (radiomics, clinical characteristics, genomic expressions, etc) gathered before and during treatment are potentially profitable. In this paper, we propose such a prediction system primarily using radiomic features (...