Objective. Automatic treatment planning of radiation therapy (RT) is desired to ensure plan
quality, improve planning efficiency, and reduce human errors. We have proposed an …

Results VTP spontaneously learns how to operate the optimization engine to generate high-
quality plans by adjusting the TPPs. For HDRBT cases, quality score of plans generated by …

Purpose We previously proposed an intelligent automatic treatment planning framework for
radiotherapy, in which a virtual treatment planner network (VTPN) is built using deep …

Purpose. We have previously proposed an intelligent automatic treatment planning (IATP)
framework that builds a virtual treatment planner network (VTPN) to operate a treatment …

Purpose In the treatment planning process of intensity‐modulated radiation therapy (IMRT),
a human planner operates the treatment planning system (TPS) to adjust treatment planning …

Purpose The purposes of this work are to (a) investigate whether the use of auto‐planning
and multiple iterations improves quality of head and neck (HN) radiotherapy plans;(b) …

Although commercial treatment planning systems (TPSs) can automatically solve the
optimization problem for treatment planning, human planners need to define and adjust the …

Advancement of novel radiation therapy (RT) modalities such as intensity-modulated
radiation therapy (IMRT)[1],[2],[3],[4],[5],[6],[7] and volumetric modulated arc therapy …

Purpose We previously developed a virtual treatment planner (VTP), an artificial intelligence
robot, operating a treatment planning system (TPS). Using deep reinforcement learning …

Background Treatment plans for head and neck (H&N) cancer are highly complex due to
multiple dose prescription levels and numerous organs at risk (OARs) close to the target …