To the editor: We read with great interest the article by Dr. Yusufaly and colleagues (1),
which developed a radiomic model incorporating tumor radiomic features, non-tumor …

REPLY: We appreciate the comments (1) by Zhang and Zhang in response to our
manuscript (2). We thank them for their compliments about our work overall. However, we …

P903 Introduction: A strategy currently explored to improve the performance of predictive
radiomic models consists in aggregating information from multiple locations in the image …

It is now recognized that intratumoral heterogeneity is associated with more aggressive
tumor phenotypes leading to poor patient outcomes (1). Medical imaging plays a central role …

The emerging field of radiomics is generating remarkable interest from oncologists and
cancer researchers alike. In the current issue of the Journal, the commentary by Verma et …

Lambin et al. introduced the term radiomics in 2012 (1) to describe the extraction of
biologically relevant quantitative features from radiological images. They suggested that …

Purpose The objective of this study is to find reproducible and predictive radiomic features
from Computed Tomography (CT) images that are correlated to clinical endpoints and …

Radiomics is the high-throughput extraction of large amounts of image features from
radiographic images, as previously defined in 2012 by Lambin and colleagues in the first …

TO THE EDITOR: We read with great interest the recent article by Wessels et al.(1). The
article purports to propose a clinically relevant, standard method for the blood-based …

TO THE EDITOR: I applaud the authors of the recent article by Siegel et al.,“Subjecting
Radiologic Imaging to the Linear No-Threshold Hypothesis: A Non Sequitur of Non-Trivial …