Abstract

Prostate cancer affects 1 in 6 men in the USA. Every man over the age of 45 is at risk for prostate cancer. Systematic transrectal ultrasound (TRUS)-guided biopsy is the standard method for a definitive diagnosis of prostate cancer. More than 1.2 million prostate biopsies are performed annually and the medical cost is more than two billion dollars each year. However, this technique has a significant sampling error and is characterized by low sensitivity (39-52%). This blind biopsy approach can miss 30% of prostate cancers. As a negative biopsy does not preclude the possibility of a missed cancer, both the physicians and patients face challenges in making treatment decisions. Due to the increasing number of younger men with potentially early and curable prostate cancer, this problem must be addressed in order to improve cancer detection rate. At our NIH/NCI-supported Emory Molecular and Translational Imaging Center, positron emission tomography (PET) with a new molecular imaging tracer FACBC has shown very promising results for prostate cancer detection in human patients. We hypothesize that FACBC PET molecular images can be incorporated into ultrasound-guided biopsy for improved cancer detection. The proposed research is to develop a molecular image-directed, 3D ultrasound-guided system for targeted biopsy of the prostate.
Specific Aim 1 : To modify a real-time, mechanically assisted, 3D ultrasound-guided device. Compared to conventional 2D image guidance, 3D images of the prostate will be used to guide the biopsy.
Specific Aim 2 : To develop fast deformable and statistical appearance model based segmentation methods for 3D ultrasound images of the prostate. Statistical shape models will be developed from our database and will be used to guide automatic segmentation of the prostate.
Specific Aim 3 : To combine FACBC molecular images with 3D ultrasound for targeted biopsy. New deformable image registration methods based joint saliency map and fuzzy point correspondence will be developed in order to solve major limitations of mutual information based image registration.
Specific Aim 4 : To test the accuracy of the integrated biopsy system in phantoms and animals. The complete biopsy system will also be tested in a small number of human patients. Our FACBC patient studies are supported by the NIH-sponsored imaging center program (P50CA128301) and by the Phase II Clinical Trial (NCT00562315). The mechanically assisted device has received the FDA 510K approval. The proposed study will promote the academic-industrial collaboration (Emory University, Robarts Research Institute of the University of Western Ontario, and Eigen Inc., Grass Valley, CA). If completely developed, the multimodality molecular image-guided system will be able to be used not only for biopsy but also for brachytherapy, radiofrequency thermal ablation, cryotherapy, and photodynamic therapy.

Public Health Relevance

The research could improve prostate cancer detection by using novel molecular imaging technology and by using a new three-dimensional image-guided biopsy device. The molecular image-guided system can be used not only for improved biopsy of diseases but also for minimally invasive therapy of cancers.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
4R01CA156775-06
Application #
8984870
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Tandon, Pushpa
Project Start
2011-01-01
Project End
2017-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
6
Fiscal Year
2016
Total Cost
Indirect Cost

  Institution

Name
Emory University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322

  Publications

Dormer, James D; Ma, Ling; Halicek, Martin et al. (2018) Heart Chamber Segmentation from CT Using Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng 10578:
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:
Halicek, Martin; Little, James V; Wang, Xu et al. (2018) Optical Biopsy of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng 10469:
Dormer, James D; Guo, Rongrong; Shen, Ming et al. (2018) Ultrasound Segmentation of Rat Hearts Using Convolution Neural Networks. Proc SPIE Int Soc Opt Eng 10580:
To, Minh Nguyen Nhat; Vu, Dang Quoc; Turkbey, Baris et al. (2018) Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. Int J Comput Assist Radiol Surg 13:1687-1696
Lu, Guolan; Wang, Dongsheng; Qin, Xulei et al. (2018) Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis. J Biophotonics 11:
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

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