Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients

Advanced quantitative MRI radiomics features for recurrence prediction in glioblastoma multiform patients

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...

Radiomics and Deep Learning

Applications and limitations of radiomics.

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe ...

Dissimilarity-based representation for radiomics applications

Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information. Many recent studies have proved that radiomics can offer a lot of useful information that physicians cannot extract from the medical images and can be associated with other information like gene or prot...

Prostate cancer radiomics: A study on IMRT response prediction based on MR image features and machine learning approaches

Introduction: To develop different radiomic models based on radiomic features and machine learning methods to predict early intensity modulated radiation therapy (IMRT) response.   Materials and Methods: Thirty prostate patients were included. All patients underwent pre ad post-IMRT T2 weighted and apparent diffusing coefficient (ADC) magnetic resonance imagi...