To evaluate the clinical accuracy and utility of a commercial deep learning-based auto-segmentation tool by Siemens for delineating brain metastases (GTV) and organs-at-risk (OAR) on contrast-enhanced MRI.
Author profile
Christian V. Guthier, PhD
Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School
Evaluation of a Deep Learning-Based Auto-Contouring Tool for Brain Metastases and Organs-at-Risk on MRI
Poster Program · Therapy Physics
Safety and Precision In a Same-Day, MRI-Only Simulation with Adaptive VMAT SRS/SRT Workflow: Integrating Synthetic CT and AI-Driven Quality Assurance
The efficacy of stereotactic radiosurgery (SRS) and radiotherapy (SRT) for brain metastases is often compromised by tumor growth and soft tissue changes between simulation and treatment. To eliminate these latencies, we clinically implemented a novel same-day...
Poster Program · Therapy Physics
Dynamic Prediction of Radiation-Induced Arrhythmia Using Landmark Modeling with Longitudinal Heart Rate and Cardiac Substructure Dosimetry
Radiation therapy–associated cardiac arrhythmia is a clinically significant complication of thoracic RT, with important consequences for long-term cardiovascular health and treatment outcomes. Although arrhythmia risk evolves over time and is linked to cardia...
Proffered Program · Therapy Physics
A Playbook for Clinical AI: Contouring, Outcomes, and Workflow Optimization
Therapy Physics
Invited Program · Therapy Physics