# An artificial intelligence-driven distributed stereotactic radiosurgery strategy for multiple brain metastases management

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2020 · $591,660

## Abstract

PROJECT SUMMARY
Brain metastases (BMs) are a life-threatening neurological disease, but current treatment regimens cannot
manage multiple (>4) BMs (mBMs) without causing strong adverse effects. Stereotactic radiosurgery (SRS),
utilizing potent dose to irradiate BMs and quick dose falloff to spare nearby tissues, has proven to be an effective
treatment regimen for limited-number and small-size BMs. However, SRS could not avoid high toxic dose when
BMs are multiple, clustered, or adjacent to critical organs. To safe and effectively treat mBMs with SRS requires
addressing these urgent needs: 1) to identify the maximum tolerable SRS dose; 2) to study neurocognitive
decline and design strategies to preserve patients’ post-treatment quality of life; and 3) to develop and implement
high-quality streamlined mBMs SRS treatment and follow-up care.
To address mBMs SRS management needs, we aim to develop and implement an artificial intelligence (AI)-
driven treatment planning system (TPS) and conduct a therapeutic intervention clinical trial, both dedicated to
improve mBMs SRS treatment quality and efficiency. The AI-driven TPS, namely AimBMs, will have three AI-
based computational modules, including AI-Segtor for automatic segmentation, AI-Predictor for treatment
outcome prediction and AI-Planner for spatiotemporal distributed SRS plan optimization. AimBMs is initially
developed based on retrospective data and facilitate the mBMs distributed SRS prospective phase I/II clinical
trials, while the clinical trial will provide critical clinical knowledge and evidence as feedback to improve AimBMs
performance. The ultimate goal of the project is to translate the AimBMs to routine clinical practice to improve
mBMs SRS treatment quality, patients’ post-treatment QoL, and clinical facility workflow.
In response to PAR-18-560, we have formed a multidisciplinary collaboration between radiation oncologists and
medical physicists to develop a novel AI-driven distributed SRS technology and conduct a cancer-targeted
therapeutic intervention for managing mBMs. The project’s innovations include: 1) novel SRS treatment planning
technological capability enabled by AI-based auto-segmentation, treatment outcome prediction, and
spatiotemporal plan optimization; 2) novel AI learning capability to improve developed AI tools’ performance
through the coherent clinical trial. The technology development will support the therapeutic intervention clinical
trial, while the clinical trial is designated to improve the developed system performance. This seamlessly
integrated development mode ensures the developed system is clinically practical. Upon completion, our newly
developed AimBMs will lay a solid foundation for mBMs SRS management and benefit a wide population of
patients with BMs. Moreover, the AI-based treatment planning and treatment delivery infrastructure built for
mBMs SRS can be transferred to other tumor sites to generate an even broader clinical impact.

## Key facts

- **NIH application ID:** 9841365
- **Project number:** 5R01CA235723-02
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Xuejun Gu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $591,660
- **Award type:** 5
- **Project period:** 2019-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9841365

## Citation

> US National Institutes of Health, RePORTER application 9841365, An artificial intelligence-driven distributed stereotactic radiosurgery strategy for multiple brain metastases management (5R01CA235723-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9841365. Licensed CC0.

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