Identifying image features that are robust with respect to segmentation variability is a tough
challenge in radiomics. So far, this problem has mainly been tackled in test–retest analyses …

Radiomics and artificial intelligence carry the promise of increased precision in oncologic
imaging assessments due to the ability of harnessing thousands of occult digital imaging …

Objectives To investigate sources of variation in a multicenter rectal cancer MRI dataset
focusing on hardware and image acquisition, segmentation methodology, and radiomics …

In this study, we tested and compared radiomics and deep learning-based approaches on
the public LUNG1 dataset, for the prediction of 2-year overall survival (OS) in non-small cell …

Background Radiomics has recently received considerable research attention for providing
potential prognostic biomarkers for locally advanced rectal cancer (LARC). We aimed to …

Objective Analysis of textural features of pulmonary nodules in chest CT, also known as
radiomics, has several potential clinical applications, such as diagnosis, prognostication …

Stability analysis remains a fundamental step in developing a successful imaging biomarker
to personalize oncological strategies. This study proposes an in silico contour generation …

In recent years, the application of radiomics to lung cancer show encouraging results in the
prediction of histological outcomes, survival times, staging of the disease and so on. In this …