# Artificial Intelligence Driven Automatic Treatment Planning of Stereotactic Radiosurgery for the Management of Multiple Brain Metastases

> **NIH NIH R37** · UNIVERSITY OF CHICAGO · 2022 · $365,923

## Abstract

Project Summary/Abstract
Brain metastases (BMs) are a life-threatening disease, occurring in up to 40% of cancer patients. About 40% of
BM patients have multiple (≥4) BMs (mBMs). Whole brain radiotherapy (WBRT), which has long been the
standard of care for mBMs patients, has shown pronounced impairment of neurocognitive functions. Stereotactic
radiosurgery (SRS) has improved tumor control and reduced negative effect on cognition function, compared to
WBRT. However, it has been historically reserved only for patients with <4 BMs. Recently, several clinical trials
reported strong evidence to support SRS for mBMs patients. National Comprehensive Cancer Network
guidelines hence no longer restrict the number of BMs for SRS. However, the larger BM number in mBMs
patients substantially increases the complexity of treatment planning. Conventional manual forward planning to
manually determine plan parameters becomes cumbersome and impractical for mBMs. Modern inverse planning
methods can determine plan parameters by solving an optimization problem that is composed of multiple
objectives designed for various clinical or practical considerations, while the priorities among these objectives
affect the resulting plan quality. The physician’s preferences for a particular patient can hardly be quantified and
precisely conveyed to the planner, especially for mBMs patients due to the varying number, size, and locations
of BMs. Hence, the best physician-preferred plan is often achieved through extensive trial-and-error priority
tuning and several rounds of interactions between the planner and physician. Consequently, planning time can
take up to hours, and plan quality may be suboptimal and can vary significantly, due to the varying levels of
physician and planner’s skills and physician-planner communication and cooperation, leading to deteriorated
clinical outcome. Inspired by the recent colossal advancements of artificial intelligence (AI), particularly deep
reinforcement learning (DRL) and deep inverse reinforcement learning (DIRL), on intelligent decision-making in
computer visions and robotics, we propose to develop an artificial intelligence driven automatic SRS treatment
planning system for effective management of mBMs (Aid-mBMs), learning a human-level intelligence on
treatment planning from human experts. We envision the system to have two deep neural networks: DNN-R that
acts as an AI-physician to predict the physician’s preferences for each individual patient, and DNN-P that acts
as an AI-planner to tune the priorities to achieve a plan of physician’s satisfaction. We will pursue two specific
aims. Aim 1. System prototype development: We will collect human expert planners’ priority-tuning actions and
develop DNN-R and DNN-P via interleaved DIRL-based reward function learning and DRL-based policy learning.
Aim 2. System improvement and end-to-end evaluation: We will perform a prospective study to improve our
system based on human expert’s further fine-tu...

## Key facts

- **NIH application ID:** 10501864
- **Project number:** 1R37CA272755-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Zhen Tian
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $365,923
- **Award type:** 1
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10501864

## Citation

> US National Institutes of Health, RePORTER application 10501864, Artificial Intelligence Driven Automatic Treatment Planning of Stereotactic Radiosurgery for the Management of Multiple Brain Metastases (1R37CA272755-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10501864. Licensed CC0.

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