Randomized Evaluation of Machine Learning Assisted Radiation Treatment Planning vs Standard Radiation Treatment Planning

A Randomized Evaluation of Machine Learning Assisted Radiation Treatment Planning Versus Standard Radiation Treatment Planning

The purpose of this study is to determine the magnitude of clinical benefit achieved through machine learning assisted radiation treatment planning (MLAP) on post-treatment clinical outcomes such as acute toxicity and patient reported outcomes.

This is a randomized phase III trial to evaluate the effectiveness of MLAP compared to Standard of Care. Patients will undergo a randomization procedure with a variable allocation ratio where the first third of patients are randomized 3:1 in favor of SOC, the next third randomized 1:1, and the last third randomized 1:3 in favor of MLAP. This is done to give dosimetrists time to optimize the MLAP workflow. Since the treatment team may learn to improve their treatment planning process in the SOC arm through interactions with RapidPlan, as a sensitivity analysis, the primary and secondary goals will be re-analyzed including time of registration as a moderator of the treatment effect.

Inclusion Criteria:

• Age ≥ 18 years at time of registration
• Receiving curative intent intensity modulated radiotherapy for head and neck primary cancer at the discretion of the treating medical doctor (MD)
• Provide written informed consent or allow legally authorized representative to consent on behalf of a participant
• Willing to return to enrolling institution for study follow-up visit

Exclusion Criteria:

• Incarcerated
• Unable to provide informed consent
• Prior Head and Neck (H&N) radiation therapy
• Planned radiation dosing LK<30 Gy or >76 Gy