Masoud Zarepisheh

Fast and reliable optimization remains a major bottleneck in automated and adaptive radiotherapy treatment planning. Most commercial and research planning systems rely on gradient-based optimization methods, which can converge slowly near the optimal solution...

To identify blood-bearing organs whose mean blood dose is significantly associated with OS in non-small cell lung cancer (NSCLC), and to evaluate the feasibility of sparing these organs during treatment planning.

Despite extensive research on automated treatment planning, manual trial-and-error optimization remains common in clinical practice. Knowledge-based and AI-driven approaches show promise but often lack robustness to evolving clinical protocols due to the need...

Radiotherapy (RT) planning for Head and Neck Cancer (HNC) is resource-intensive and prone to variability. This study proposes and validates a fully automated pipeline synergizing deep learning-based 3D dose prediction with a knowledge-based planning (KBP) tem...

We present PortPy (Planning and Optimization for Radiation Therapy in Python), an open-source platform designed to accelerate research, benchmarking, and clinical translation of treatment planning optimization algorithms. PortPy provides curated benchmark dat...