# Human-like automated radiotherapy treatment planning via imitation learning

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $574,629

## Abstract

PROJECT SUMMARY
Radiation therapy is one of the major approaches for cancer treatment. Treatment planning, the process of
designing the optimal treatment plan for each patient, is one of the most critical steps. If a treatment is poorly
designed, a satisfactory outcome cannot be achieved, regardless of the quality of other treatment steps.
Treatment planning in modern radiotherapy is formulated as a mathematical optimization problem defined by a
set of hyperparameters. While there exists several quantifiable metrics to quantify plan quality and guide the
planning process, these are simplified representations that cannot fully describe the physician’s intent. In addition,
these metrics only measure plan quality from a population-based perspective, and cannot guide treatment
planning to achieve the patient-specific best treatment plans. Hence, the best physician-preferred solution often
sits in a gray area, only achievable by an extensive trial-and-error hyperparameter tuning process and
interactions between the planner and physician. Consequently, planning time can take up to a week for complex
cases and plan quality may be poor, if the planner is inexperienced and/or under heavy time constraints. These
consequences substantially deteriorate treatment outcomes, as having been clearly demonstrated in clinical
studies. Recently, the advancement in artificial intelligence (AI), particularly in imitation learning allows human-
like decision making by observing a human expert’s actions and internally building its own decision-making
system. In response to PAR-18-530, the goal of this project is to develop and translate an AI planner that mimics
human experts’ behavior to generate a high quality plan. The AI planner will not replace human planners. Instead,
the AI plan will be used as a starting point in the current planning process to improve plan quality and planning
efficiency. The human planner’s actions on further plan improvement can feed back to the AI planner through
continuous learning for its continuous evolution. We will pursue this goal using prostate cancer as the test bed
through an academic-industrial partnership, jointing strong research and clinical expertise at UT Southwestern
Medical Center with extensive commercial product development experience at Varian Medical Systems Inc. The
following specific aims are defined. Aim 1: Model and algorithm development. We will collect experts’ behavior
data in routine treatment planning and train the AI planner. Aim 2: System validation and translation. We will
integrate the AI planner into Varian Eclipse treatment planning system and validate the system in a clinically
realistic setting. The innovations include the use of a state-of-the-art AI imitation learning algorithm to solve a
clinically important problem, the novel technological capabilities enabled by the developed system, as well as
coherent translation activities to deliver new capabilities to end users. Deliverability is ensured by ext...

## Key facts

- **NIH application ID:** 10819507
- **Project number:** 5R01CA254377-04
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Xun Jia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $574,629
- **Award type:** 5
- **Project period:** 2021-05-18 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10819507

## Citation

> US National Institutes of Health, RePORTER application 10819507, Human-like automated radiotherapy treatment planning via imitation learning (5R01CA254377-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10819507. Licensed CC0.

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