57 citations · 141 across the 18 of their papers we have counts for
5 papers · 1 filter
Improving Deep Learning-Based Target Volume Auto-Delineation for Adaptive MR-Guided Radiotherapy in Head and Neck Cancer: Impact of a Volume-Aware Dice Loss
Sogand Beirami, Zahra Esmaeilzadeh, Ahmed Gomaa +9
Background: Manual delineation of target volumes in head and neck cancer (HNC) remains a significant bottleneck in radiotherapy planning, characterized by high inter-observer varia…
Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI
Ahmed Gomaa, Annette Schwarz, Ludwig Singer +23
Background: Differentiating radiation necrosis (RN) from tumor progression after stereotactic radiosurgery (SRS) remains a critical challenge in brain metastases. While histopathol…
Benchmarking GPT-5 in Radiation Oncology: Measurable Gains, but Persistent Need for Expert Oversight
Ugur Dinc, Jibak Sarkar, Philipp Schubert +16
Introduction: Large language models (LLM) have shown great potential in clinical decision support. GPT-5 is a novel LLM system that has been specifically marketed towards oncology…
Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data
Yixing Huang, Fuxin Fan, Ahmed Gomaa +4
Cone-beam computed tomography (CBCT) is widely used in interventional surgeries and radiation oncology. Due to the limited size of flat-panel detectors, anatomical structures might…
Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning
Philipp Sommer, Yixing Huang, Christoph Bert +5
Stereotactic radiotherapy (SRT) is one of the most important treatment for patients with brain metastases (BM). Conventionally, following SRT patients are monitored by serial imagi…