Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation
oncology. However, large curated datasets-often involving imaging data and corresponding …

Abstract Background and Purpose Conventional magnetic resonance imaging (MRI) poses
challenges in quantitative analysis because voxel intensity values lack physical meaning …

Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on
medical images is crucial for radiotherapy. However, interobserver segmentation variability …

Purpose Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a
crowdsourced challenge engaging radiation oncologists across various expertise levels in …

Background/Purpose Sarcopenia is a prognostic factor in patients with head and neck
cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) …

Objective Radiation necrosis (RN) can be difficult to radiographically discern from tumor
progression after stereotactic radiosurgery (SRS). The objective of this study was to …

Introduction Interest in using higher order features of the planned 3D dose distributions (ie,
dosiomics) to predict radiotherapy outcomes is growing. This is driving many retrospective …

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and
novel paradigm describing the role that computers might play in our society to a tool with …

Background and objective: Despite fast evolution cycles in deep learning methodologies for
medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the …

Purpose Radiation therapy (RT) of pediatric brain cancer is known to be associated with
long‐term neurocognitive deficits. Although target and organs‐at‐risk (OARs) are contoured …