Researchers in China Use Radiomics To Predict Heart Attacks

Heart Attack Illustration

A study in Radiology, a journal of the Radiological Society of North America, reveals that researchers are utilizing radiomics to predict future heart attacks. Radiomics is an approach that helps extract quantitative information from CT images, allowing researchers to uncover disease characteristics that aren’t evident from the images alone.

Researchers are using an approach called radiomics to predict future cardiac events like heart attacks, according to a study published today (February 14, 2023) in Radiology, a journal of the Radiological Society of North America (RSNA). Radiomics allows researchers to extract quantitative, or measurable, data from CT images that can reveal disease characteristics not visible in the images alone.

Coronary artery disease is linked with fatty deposits of plaque that build up inside the artery walls. Large, lipid-rich plaques are vulnerable to rupture. The rupture of these plaques causes most heart attacks. However, predicting which plaques will rupture is challenging.

Researchers in China developed a radiomics model that uses information from coronary CT angiography images to assess plaque vulnerability. They developed the model in 299 patients. They then studied the approach in 708 patients with suspected coronary artery disease.

The model enabled the detection of vulnerable plaques associated with an increased risk for major adverse cardiac events like heart attacks. A high radiomic signature was independently associated with these events over a median three-year follow-up.

Radiomics to Predict Heart Attacks

Representative images from two patients demonstrate the use of a radiomic signature (RS) to discriminate vulnerable plaques defined with intravascular US. (A–C) Images depict a vulnerable plaque with a high RS. (A) Image from coronary CT angiography (CCTA) shows a partially calcified plaque (arrow) with RS value of 0.56 in the proximal left anterior descending artery (LAD). (B) Corresponding axial CCTA image shows the plaque with low-attenuation area (circle, 25 HU). (C) Corresponding intravascular US scan confirms the plaque as an attenuated plaque (*). (D–F) Images show a nonvulnerable plaque with low RS. (D, E) CCTA images show a partially calcified plaque (arrow in D) with RS value of 0.50 in the proximal left anterior descending artery. (F) Corresponding intravascular US scan confirms the plaque as a partially calcified plaque without echo attenuation and echolucent zone. High versus low RS lesion was defined using an optimal cut-off value of 0.53 (range, 0.46–0.58). Credit: Radiological Society of North America

“The results of this study are encouraging and exciting,” said study co-lead author Long Jiang Zhang, M.D., Ph.D., from the Department of Radiology at Jinling Hospital, Medical School of Nanjing University in Nanjing, China. “Radiomics provided a more accurate approach to detect vulnerable plaques compared to conventional coronary CT angiography anatomical parameters.”

The radiomic signature would be easy to add into clinical practice, Dr. Zhang said. In the clinic, it could assess potentially vulnerable plaques and help stratify high-risk patients.

“If the radiomics analysis is embedded into the routine CT angiography workstation, it can automatically identify vulnerable plaques for clinician review,” Dr. Zhang said. “Thus, radiomics may significantly improve the accuracy and precision of high-risk plaque detection in routine clinical practice.”

The researchers intend to build a radiomics model from different scanner types and vendors. They also are planning a larger, multicenter study of 10,000 patients.

“With the support of large observational studies and randomized controlled trials, the radiomics approach may help guide clinical decision-making and improve patient care in the future,” Dr. Zhang said.

“A Coronary CT Angiography Radiomics Model to Identify Vulnerable Plaque and Predict Cardiovascular Events” by Qian Chen, Tao Pan, Yi Ning Wang, U. Joseph Schoepf, Samuel L. Bidwell, Hongyan Qiao, Yun Feng, Cheng Xu, Hui Xu, Guanghui Xie, Xiaofei Gao, Xin-Wei Tao, Mengjie Lu, Peng Peng Xu, Jian Zhong, Yongyue Wei, Xindao Yin, Junjie Zhang and Long Jiang Zhang, 14 February 2023, Radiology.
DOI: 10.1148/radiol.221693

Collaborating with Dr. Zhang were Qian Chen, M.D., Tao Pan, M.B.B.S., Yi Ning Wang, M.D., Ph.D., U. Joseph Schoepf, M.D., Samuel L. Bidwell, B.S., Hongyan Qiao, M.D., Yun Feng, M.D., Cheng Xu, M.D., Hui Xu, M.Sc., Guanghui Xie, M.B.B.S., Xiaofei Gao, M.D., Xin-Wei Tao, M.Sc., Mengjie Lu, Ph.D., Peng Peng Xu, M.D., Jian Zhong, M.B.B.S., Yongyue Wei, Ph.D., Xindao Yin, M.D., Ph.D., and Junjie Zhang, Ph.D.

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