Dr. Baowei Fei was awarded a five-year NIH R01 grant

The project is to translate our positron emission tomography (PET)/transrectal ultrasound (TRUS) fusion guided technology into a commercially supported platform for improving the detection of prostate cancer. It has been reported that the long-term prostate cancer specific survival of patients initially managed with active surveillance (AS) or watchful waiting for low-risk prostate cancer ranges from 97% to 100%. However, among all men with indolent prostate cancer, the rate of aggressive treatment is as high as 64.3%. The costs for the treatment are $12 billion each year in the USA. One reason for aggressive treatment is due to the fact that the current standard diagnosis with transrectal ultrasound-guided biopsy can miss up to 30% of cancers. A major concern for active surveillance is the risk of high-grade cancer that may be missed by the current diagnosis. This research is to develop innovative imaging technology that can improve the detection rate and distinguish aggressive cancer, which requires treatment, from the non-aggressive disease, which can be well-managed with active surveillance. The technology will provide clinicians a new imaging tool to select millions of low-risk prostate cancer patients for active surveillance instead of unnecessary treatment, therefore may help save billions of dollars in treatment costs and improve the care of prostate cancer patients.

Workshop and Launch of the Integrative Cancer Imaging Research Program (iCIRP)

The Integrative Cancer Imaging Research Program (iCIRP) is a joint program between Emory University School of Medicine Department of Radiology and Imaging Sciences, Georgia Tech/Emory Coulter Department of Biomedical Engineering, and Winship Cancer Institute of Emory University. This program will build on and synergize unique strengths inherent in Emory units and centers that foster multidisciplinary collaborations within and among the disciplines of imaging science, cancer biology, nanotechnology, biomarker development, computation, and clinical cancer research. The overarching goal of the iCIRP program is to advance cancer detection, diagnosis, prognosis, image-guided therapy, prediction of efficacy, and monitoring of treatment.

Dr. Baowei Fei’s talk on the introduction of the Program: PDF File

For more information about this program, please visit: http://feilab.org/publication_pdf/Fei_iCIRP.pdf

Dr. Baowei Fei served as Chair for NIH Study Section ZRG1 SBIB-F (56)R on Early Phase Clinical Trials in Imaging and Image-guided Interventions

This Funding Opportunity Announcement (FOA) is intended to support clinical trials conducting preliminary evaluation of the safety and efficacy of imaging agents, as well as an assessment of imaging systems, image processing, image-guided therapy, contrast kinetic modeling, 3-D reconstruction and other quantitative tools. As many such preliminary evaluations are early in development, this FOA will provide investigators with support for pilot (Phase I and II) cancer imaging clinical trials, including patient monitoring and laboratory studies. This FOA supports novel uses of known/standard clinical imaging agents and methods as well as the evaluation of new agents, systems, or methods. The imaging and image-guided intervention (IGI) investigations, if proven successful in these early clinical trials, can then be validated in larger studies through competitive R01 mechanisms, or through clinical trials in the Specialized Programs of Research Excellence (SPOREs), Cancer Centers and/or the NCI's National Clinical Trials Network. October 21, 2016

Guolan Lu successfully defended her PhD thesis and joined Stanford University

As an emerging optical modality, hyperspectral imaging (HSI) holds great promise for early cancer detection and image-guided surgery. The major advantage of HSI is that it is a noninvasive technology that doesn't require any contrast agent, and it combines wide-field imaging and spectroscopy to simultaneously attain both spatial and spectral information from an object in a non-contact way. Light delivered to the tissue surface undergoes multiple elastic scattering and absorption interactions, and part of it returns as diffuse reflectance carrying diagnostic information about the underlying tissue structure and composition. The biochemical and morphological properties of the tissue change during disease progression. Therefore hyperspectral images, which contain high-dimensional spectral information at each image point, can be analyzed for visualization, characterization, and quantification of the disease state in biological tissue.

The overall goal of this dissertation was to investigate the potential of label-free HSI technology combined with machine learning methods as a noninvasive diagnostic tool for quantitative detection and delineation of head and neck cancer. More specifically, this dissertation work has two applications: the early detection of cancer, and surgical guidance. To achieve this, we had four different aims. The first two aims evaluated the diagnostic performance of HSI and machine learning algorithms at differentiating cancer from normal tissue in preclinical animal models, including a subcutaneous cancer model (Aim 1), and a chemically-induced tongue carcinogenesis model (Aim 2). The last two aims investigated the detection and delineation of head and neck cancer in a surgical animal model (Aim 3) and fresh surgical specimens of human patients (Aim 4).