News | Computed Tomography (CT) | June 14, 2021

Rensselaer algorithm can identify risk of cardiovascular disease using lung cancer scan

Rensselaer algorithm can identify risk of cardiovascular disease using lung cancer scan #CT

June 14, 2021 — Heart disease and cancer are the leading causes of death in the United States, and it’s increasingly understood that they share common risk factors, including tobacco use, diet, blood pressure, and obesity. Thus, a diagnostic tool that could screen for cardiovascular disease while a patient is already being screened for cancer, has the potential to expedite a diagnosis, accelerate treatment, and improve patient outcomes. 

In research published today in Nature Communications, a team of engineers from Rensselaer Polytechnic Institute and clinicians from Massachusetts General Hospital developed a deep learning algorithm that can help assess a patient’s risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer. This approach paves the way for more efficient, more cost-effective, and lower radiation diagnoses, without requiring patients to undergo a second CT scan. 

“In this paper, we demonstrate very good performance of a deep learning algorithm in identifying patients with cardiovascular diseases and predicting their mortality risks, which shows promise in converting lung cancer screening low-dose CT into a dual screening tool,” said Pingkun Yan, an assistant professor of biomedical engineering and member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer.

Numerous hurdles had to be overcome in order to make this dual screening possible. Low-dose CT images tend to have lower image quality and higher noise, making the features within an image harder to see. Using a large dataset from the National Lung Screening Trial (NLST), Yan and his team used data from more than 30,000 low-dose CT images to develop, train, and validate a deep learning algorithm capable of filtering out unwanted artifacts and noise, and extracting features needed for diagnosis. Researchers validated the algorithm using an additional 2,085 NLST images.

The Rensselaer team also partnered with Massachusetts General Hospital, where researchers were able to test this deep learning approach against state-of-the-art scans and the expertise of the hospital’s radiologists. The Rensselaer-developed algorithm, Yan said, not only proved to be highly effective in analyzing the risk of cardiovascular disease in high-risk patients using low-dose CT scans, but it also proved to be equally effective as radiologists in analyzing those images. In addition, the algorithm closely mimicked the performance of dedicated cardiac CT scans when it was tested on an independent dataset collected from 335 patients at Massachusetts General Hospital.

“This innovative research is a prime example of the ways in which bioimaging and artificial intelligence can be combined to improve and deliver patient care with greater precision and safety,” said Deepak Vashishth, the director of CBIS.

Yan was joined in this work by Ge Wang, an endowed chair professor of biomedical engineering at Rensselaer and fellow member of CBIS. The Rensselaer team was joined by Dr. Mannudeep K. Kalra, an attending radiologist at Massachusetts General Hospital and professor of radiology with Harvard Medical School. This research was funded by the National Institutes of Health National Heart, Lung, and Blood Institute.  

For more information: www.rpi.edu

Related lung and heart disease content:

Lung Cancer Screening Predicts Risk of Death from Heart Disease

Low-dose CT for Lung Cancer Screening: Benefit Outweighs Potential Harm

AI Analysis Can Improve Lung Cancer Detection on Chest Radiographs

Experts Recommend Shared Patient - Doctor Decision-making Prior to Lung Cancer Screening

Related Content

News | Breast Imaging

August 19, 2022 — The Christ Hospital Health Network, known for providing the best, most compassionate care for its ...

Time August 19, 2022
arrow
News | Radiation Therapy

August 19, 2022 — Recently published research out of VCU Massey Cancer Center demonstrated that cancer patients who live ...

Time August 19, 2022
arrow
News | X-Ray

August 17, 2022 — The Institute of Human Virology in Nigeria (IHVN) has announced their partnership with Fujifilm, the ...

Time August 17, 2022
arrow
News | Prostate Cancer

August 16, 2022 — A new study published by University of Kentucky Markey Cancer Center researchers suggests that the ...

Time August 16, 2022
arrow
News | Computed Tomography (CT)

August 15, 2022 — According to ARRS’ American Journal of Roentgenology (AJR), the combination of deep-learning ...

Time August 15, 2022
arrow
News | Breast Imaging

August 15, 2022 — Brainlab announced the first group of breast cancer patients treated in United States with the ...

Time August 15, 2022
arrow
Feature | Radiology Business | By Melinda Taschetta-Millane

Did you know that Imaging Technology News (ITN) maintains more than 40 comparison charts of product specifications from ...

Time August 12, 2022
arrow
News | ASTRO

August 12, 2022 — The American Society for Radiation Oncology (ASTRO) today expressed its support for President Joseph R ...

Time August 12, 2022
arrow
News | Radiology Education

August 11, 2022 — After a three-year break, this spring featured the return of the anticipated Canon Roadshow Events ...

Time August 11, 2022
arrow
News | Computed Tomography (CT)

August 11, 2022 — For the first time, researchers successfully captured CT images of an entire woolly mammoth tusk ...

Time August 11, 2022
arrow
Subscribe Now