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
Marvin Kinz, MS
Department of Radiation Oncology, Dana-Farber Cancer Institute/Brigham and Women’s Hospital, 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
Tumor–Vessel Motion Correlation In MR-Guided Lung Radiotherapy
To evaluate the correlation between lung tumor and pulmonary vessel motion under MR-guided radiotherapy and to inform surrogate tracking feature selection when direct tumor tracking is challenging.
Proffered Program · Therapy Physics