Dr. Baowei Fei’s research on prostate cancer imaging has been featured by SciTech Europa Quarterly that provides opportunities to leading figures from across Europe to discuss current and future projects, policy change and future priorities within research and development, as well as being a platform for others to voice their opinions and showcase their results to Europe’s research community. For more information, read the PDF file of the article or visit the following websites:
Community Impact Newspaper reported the cancer research work that were conducted at the University of Texas at Dallas. Community Impact Newspaper is vested in the communities and has dedicated reporters in each community who attend city council and school board meetings. Community Impact Newspaper distributes hyperlocal news and information to millions of local residents and business owners each day online and monthly by mail. For more information, read the article from the following website:
Dr. Baowei Fei and his group have recently used a unique imaging technology and artificial intelligence (AI) to predict the presence of cancer cells in tissue samples. This hyperspectral imaging technique is effective in satellite imagery and orbiting telescopes. It could be used to quickly identify cancer cells in the operating room as well. Analyzing 293 tissue samples from 102 head and neck cancer surgery patients, Fei and colleagues found that hyperspectral imaging and AI could be used to predict cancer cell presence with 80-90% accuracy. Dr. Fei recently received a $1.6 million grant from the Cancer Prevention & Research Institute of Texas (CPRIT) to continue improving this smart surgical microscope. Once this approach is fully developed, it would need to be tested in clinical studies before being used in the live healthcare environment. For more information, visit the following website:
The research work by Dr. Baowei Fei’s Lab has been featured by AAAS and EurekAlert!. In a study published in the Sept. 14 edition of the journal Cancers, Dr. Fei and colleagues showed that hyperspectral imaging and artificial intelligence could predict the presence of cancer cells with 80% to 90% accuracy in 293 tissue specimens from 102 head and neck cancer surgery patients. Dr. Fei recently received a $1.6 million grant from the Cancer Prevention & Research Institute of Texas (CPRIT) to further develop the technology, called a smart surgical microscope. For more information, visit the following website:
Dr. Baowei Fei, the Cecil H. and Ida Green Chair in Systems Biology Science at UT Dallas, is developing a smart surgical microscope that uses hyperspectral imaging and artificial intelligence to detect cancer cells during surgery. He recently received a $1.6 million grant from the Cancer Prevention & Research Institute of Texas (CPRIT) to further develop the technology. Hyperspectral imaging, originally used in satellite imagery, orbiting telescopes and other applications, goes beyond what the human eye can see as cells are examined under ultraviolet and near-infrared lights at micrometer resolution. By analyzing how cells reflect and absorb light across the electromagnetic spectrum, experts can get a spectral image of cells that is as unique as a fingerprint. For more information, visit the following website:
Lung cancer is the leading cause of cancer death and one of the most common cancers among both men and women in the United States. Recent advances in high-resolution imaging set the stage for radiomics to become an active emerging field in cancer research. However, the promise of radiomics is limited by a lack of image standardization tools, because computed tomography (CT) images are often acquired using scanners from different vendors with customized acquisition parameters, posing a fundamental challenge to radiomic studies across sites. To overcome this challenge, especially for large-scale, multi-site radiomic studies, advanced algorithms are required to integrate, standardize, and normalize CT images from multiple sources. We propose to develop STAN-CT, a deep learning software package that can automatically standardize and normalize a large volume of diagnostic images to facilitate cross-site large-scale image feature extraction for lung cancer characterization and stratification. STAN-CT will enable a wide range of radiomic researches to identify diagnostic image features that strongly associated with lung cancer prognosis.
Dr. Baowei Fei’s research on prostate cancer biopsy has been featured by Scientia Global that is a series of outreach research publications. Scientia Global connects people: scientists and educators, policy-makers and researchers, and the public and private sectors, and helps researchers communicate their findings beyond their specialty and into the wider world. Scientia offers the research community significant visibility and accessibility to those both inside and outside the community to take an interest in science and research. Dr. Fei is pioneering a technique that merges positron emission tomography (PET) with ultrasound imaging to detect prostate cancer more accurately than before. For more information, read the PDF file of the article or visit the following websites: