# Computer aided diagnosis of cancer metastases in the brain

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $268,500

## Abstract

The overarching goal of this project is to improve the accuracy in diagnosing cancer metastases in the brain
through the development of a novel computer-aided diagnosis (CAD) technique. In today’s cancer treatment, it
is often not the primary cancer but the metastasized cancer that causes fatality. Many cancer, including lung,
kidney, ovarian, and breast cancer, and melanoma, have a tendency metastasizing to the brain and the
number of brain metastases is as high as 170,000 a year in the US alone. Therefore, accurate diagnosis of
brain metastases is of utmost importance in saving lives and improving patient’s well-being. Magnetic
resonance imaging (MRI) is the most widely used modality to scan brain for potential metastases but
diagnosing metastases is a very challenging task that has a considerable rate of false-negatives. The first
difficulty in diagnosing metastases is that, at early stage, metastases are asymptomatic. The second difficulty
is that metastases manifest as weak signal intensity changes on MRI and their appearance is often highly
similar to normal brain structures, such as small blood vessels, meaning that one must visualize in his/her mind
whether an observed object is a metastasis or a blood vessel. Missing a metastasis has a severe consequence
as the patient will not be called for further treatment. The benefit of accurate diagnosis of metastases, on the
other hand, can have a significant benefit to the patient as treatment like stereotactic radiosurgery (SRS) can
completely eliminate the metastasized tumor in many cases and extend patient’s life span by three to four
years in most cases.
 CAD can play a key role in improving the accuracy in diagnosing brain metastases by identifying abnormal
signal intensity changes and mark them for radiologists to examine. In this process, CAD will function as an aid
tool to complement human’s expertise in interpreting brain MRI. However, despite the importance of finding
and treating brain metastases, there currently is lacking a CAD approach to this problem. Many existing
computational techniques on brain MRI were tailored to MRI data acquired in a research setting that often
involves many other MRI techniques such as DWI, DTI, and functional MRI. But in clinics only anatomic MRI
like T1- and T2-weighted MRI are used to scan a patient, therefore, a CAD approach must be tailored to the
clinical setting to assist radiologists in reading the brain MRI. In this project we propose a CAD design that is
based on novel computational techniques and integrated with routine clinical MRI acquisition. The CAD design
features minimum user intervention and parameter selection, high robustness, and user-friendliness. We will
also take advantage of the availability of graphics processing unit (GPU) in implementation to speed up the
computations. We expect the proposed CAD approach will improve the accuracy of diagnosing brain
metastases, and in turn, save lives and benefit patients’ well-being.

## Key facts

- **NIH application ID:** 10163013
- **Project number:** 3R01LM012434-05S1
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Geoffrey Young
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $268,500
- **Award type:** 3
- **Project period:** 2016-09-06 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10163013

## Citation

> US National Institutes of Health, RePORTER application 10163013, Computer aided diagnosis of cancer metastases in the brain (3R01LM012434-05S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10163013. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*