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

Deep Learning Enables Dual Screening for Cancer and Cardiovascular Disease

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

Right mediolateral oblique (MLO) mammograms for different women with the same breast thickness but varying breast density.

Right mediolateral oblique (MLO) mammograms for different women with the same breast thickness but varying breast density.

News | Breast Imaging | July 30, 2021
July 30, 2021 — Volpara Health, a global health technology software leader providing an integrated breast care platfo
According to ARRS’ American Journal of Roentgenology (AJR), the resources required to warm iohexol 350 to body temperature before injection for computed tomography (CT) may not be warranted, given the lack of observed practical benefit.

Values represent number of patients, with percentage in parentheses and 95% CI in brackets (not reported for levels of severity of allergic/allergic-like reactions). 95% CIs were calculated using the Clopper-Pearson exact formula. For events with zero frequency, one-sided 97.5% CIs are provided.

News | Contrast Media | July 30, 2021
Images, or a digital twin mitral valve of a patient, created from cardiac ultrasound that were used to perform a virtual surgical procedure to test how the intervention would impact the patient prior to actually performing the procedure. The right image shows color coding for sheer stresses on the valve leaflets before and after the virtual surgery. The left image shows the model quantitation of leaflet coaptation at peak systole prior to the the virtual surgery.

Images, or a digital twin mitral valve of a patient, created from cardiac ultrasound that were used to perform a virtual surgical procedure to test how the intervention would impact the patient prior to actually performing the procedure. The right image shows color coding for sheer stresses on the valve leaflets before and after the virtual surgery. The left image shows the model quantitation of leaflet coaptation at peak systole prior to the the virtual surgery. Read the original article in Plos One.

Feature | Ultrasound Imaging | July 28, 2021
Outside of medicine, computer-generated virtual twins of real machines like cars or airplanes have been used in engin
If you plan to attend HIMSS21 Aug. 9-13 in Las Vegas, be sure to note that due to health and safety updates, masks will now be required for attendees and exhibitors.

Getty Images

News | HIMSS | July 28, 2021
July 28, 2021 —If you plan to attend HIMSS21 Aug.
The FLASH Effect significantly improves the therapeutic ratio for curing cancer

The FLASH Effect significantly improves the therapeutic ratio for curing cancer

News | Radiation Oncology | July 28, 2021
July 28, 2021 — IntraOp Medical Corporation announced that ...
Researchers from the School of Biomedical Engineering & Imaging Sciences at King's College London have automated brain MRI image labeling, needed to teach machine learning image recognition models, by deriving important labels from radiology reports and accurately assigning them to the corresponding MRI examinations

Getty Images

News | Magnetic Resonance Imaging (MRI) | July 28, 2021
July 28, 2021 — Researchers from the School of Biomedical Engineering & Imaging Sciences at...
A new Harvey L. Neiman Health Policy Institute study found that patients paid 12% of the costs of secondary imaging interpretation out-of-pocket. Such secondary interpretations are increasingly performed for complex patients, but patients’ liabilities and paid out-of-pocket costs were not previously known.

Getty Images

News | Radiology Imaging | July 28, 2021
July 28, 2021 — A new Harvey L.