The central objective of this partnership between Emory and Eigen is to translate our PET/ultrasound fusion targeted biopsy 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. There are unmet clinical needs 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. PET with new molecular imaging tracers has shown promising results for the detection of prostate cancer. For example, 68Ga-PSMA PET can detect lesions characteristic for prostate cancer at low prostate specific antigen level. In our preliminary study, 18F-FACBC PET showed higher focal uptake in tumor foci than in normal prostate; and the standard uptake value of FACBC significantly correlated with Gleason score. Hence, PET molecular information is useful to identify and target the suspicious high-risk cancer lesions for biopsy. For this purpose, we built a PET/ultrasound fusion targeted biopsy system that is able to obtain 3D ultrasound data and fuse them with PET/CT images. As a result, a suspicious PET lesion is superimposed over the ultrasound data; and the fused image is then used to direct biopsy needles to targets. The PET/ultrasound targeted biopsy technology can be used to identify those AS patients who have high-risk cancers but are missed by standard TRUS-guided biopsy. We hypothesize that PET/ultrasound fusion targeted biopsy can detect more clinically significant cancers than the standard transrectal ultrasound (TRUS) guided biopsy in AS patients. This partnership will focus on the technology development and translation. Advanced learning-based segmentation and deformable registration methods will be developed and integrated into Eigen's Artemis and ProFuse systems. The PET/ultrasound targeted biopsy system will be tested with two new PET tracers in AS patients. The approach will be applicable to any other PET probe. With this device, histology will be correlated with molecular image characteristics, which may correlate to low vs high risk, at a high degree of certainty. The new PET/ultrasound fusion method can be readily disseminated to our 55 existing sites. 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.
Through this academic-industrial partnership between Emory and Eigen, we will combine highly sensitive PET molecular imaging with real-time ultrasound for the management of prostate cancer patients on active surveillance. We will integrate our PET/ultrasound fusion targeted biopsy technology into a commercially supported platform and will provide an innovative imaging tool for clinicians to select and monitor patients with low-risk prostate cancer for active surveillance instead of unnecessary treatment and thus help save billions of dollars in treatment costs.
|Lu, Guolan; Little, James V; Wang, Xu et al. (2017) Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging. Clin Cancer Res 23:5426-5436|
|Li, Rufeng; Wang, Yibei; Xu, Hong et al. (2017) Micro-Droplet Detection Method for Measuring the Concentration of Alkaline Phosphatase-Labeled Nanoparticles in Fluorescence Microscopy. Sensors (Basel) 17:|
|Ma, Ling; Guo, Rongrong; Tian, Zhiqiang et al. (2017) A random walk-based segmentation framework for 3D ultrasound images of the prostate. Med Phys 44:5128-5142|
|Sarno, Antonio; Dance, David R; van Engen, Ruben E et al. (2017) A Monte Carlo model for mean glandular dose evaluation in spot compression mammography. Med Phys 44:3848-3860|
|Ma, Ling; Liu, Xiabi; Fei, Baowei (2017) Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases. Phys Med Biol 62:612-632|
|Ma, Ling; Guo, Rongrong; Zhang, Guoyi et al. (2017) A combined learning algorithm for prostate segmentation on 3D CT images. Med Phys 44:5768-5781|
|Fei, Baowei; Lu, Guolan; Wang, Xu et al. (2017) Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients. J Biomed Opt 22:1-7|
|Tian, Zhiqiang; Liu, Lizhi; Zhang, Zhenfeng et al. (2017) A supervoxel-based segmentation method for prostate MR images. Med Phys 44:558-569|
|Fei, Baowei; Nieh, Peter T; Master, Viraj A et al. (2017) Molecular imaging and fusion targeted biopsy of the prostate. Clin Transl Imaging 5:29-43|
|Lu, Guolan; Wang, Dongsheng; Qin, Xulei et al. (2017) Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis. J Biophotonics :|
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