“Radiomics” refers to the extraction and analysis of large amounts of advanced quantitative
imaging features with high throughput from medical images obtained with computed …

Introduction Inter‐observer variability (IOV) in target volume and organ‐at‐risk (OAR)
delineation is a source of potential error in radiation therapy treatment. The aims of this study …

Background and purpose Volume delineation is a well-recognised potential source of error
in radiotherapy. Whilst it is important to quantify the degree of interobserver variability (IOV) …

Advances in technical radiotherapy have resulted in significant sparing of organs at risk
(OARs), reducing radiation-related toxicities for patients with cancer of the head and neck …

Abstract Machine learning technology has a growing impact on radiation oncology with an
increasing presence in research and industry. The prevalence of diverse data including 3D …

Purpose To develop a head and neck normal structures autocontouring tool that could be
used to automatically detect the errors in autocontours from a clinically validated …

In this study, we evaluate the performance of the Segment Anything Model (SAM) model in
clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic …

Background Consistent delineation of patient anatomy becomes increasingly important with
the growing use of highly conformal and adaptive radiotherapy techniques. This study …

Purpose To evaluate the uncertainty of radiomics features from contrast-enhanced breath-
hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic …

Automated atlas-based segmentation (ABS) algorithms present the potential to reduce the
variability in volume delineation. Several vendors offer software that are mainly used for …