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.

Public Health Relevance

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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Baker, Houston
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Emory University
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Shahedi, Maysam; Halicek, Martin; Guo, Rongrong et al. (2018) A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling. Med Phys 45:2527-2541
Guo, Rongrong; Lu, Guolan; Qin, Binjie et al. (2018) Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. Ultrasound Med Biol 44:37-70
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Watson, Shana R; Dormer, James D; Fei, Baowei (2018) Imaging technologies for cardiac fiber and heart failure: a review. Heart Fail Rev 23:273-289
Halicek, Martin; Little, James V; Wang, Xu et al. (2018) Deformable Registration of Histological Cancer Margins to Gross Hyperspectral Images using Demons. Proc SPIE Int Soc Opt Eng 10581:
Tian, Zhiqiang; Liu, Lizhi; Zhang, Zhenfeng et al. (2018) PSNet: prostate segmentation on MRI based on a convolutional neural network. J Med Imaging (Bellingham) 5:021208
Halicek, Martin; Little, James V; Wang, Xu et al. (2018) Tumor Margin Classification of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng 10576:

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