Riverside Research Institute, Beth Israel Deaconess Medical Center, Rutgers University, and the General Electric Corporation propose to undertake an Academic-Industrial Partnership study to improve prostate- cancer (PCA) imaging markedly and thereby improve prostate-biopsy guidance to detect PCa;enhance monitoring, surveillance, and treatment of PCa;and enable planning and execution of focal PCa therapy. The project is highly significant because it addresses a major health problem in the United States and other developed countries. The project will overcome the current inability of established clinical-imaging method to image PCa reliably by combining the capabilities of advanced ultrasound (US) and magnetic-resonance (MR) techniques in a clinically effective manner. The proposed approach will exploit the sensitivity of US to mechanical properties of tissue on a microscopic scale with the sensitivity of MR to chemical constituents of tissue and its ability to sense blood distribution. Each of these modalities senses different and independent properties of tissue and has shown encouraging potential for improved imaging of PCa when used alone;combining parameters derived from each modality can provide far superior sensitivity and specificity for PCa. We will combine US and MR parameters using advanced classifiers such as artificial neural networks and support-vector machines. These classifiers already have produced ROC-curve areas of 0.91 for advanced US methods, and the MR methods have demonstrated equivalent ROC-curve areas in many studies. We will embody the combined capabilities in specifications for a prototype imaging system that can generate prostate tissue-typing images (TTIs) in real-time for targeting biopsies or planning treatment in the operating room or in an off-line setting. The latest Logiq E9 instrument currently being produced by GE already has a capability for fusing previously obtained MR images with US images in real time, which provides an existing framework for combining US and MR parameters and generating real-time TTIs. Successfully generating reliable prostate TTIs based on combined US and MR parameters will represent a quantum advance in PCa management by enabling significant improvements in the diagnosis and treatment of PCa.

Public Health Relevance

Riverside Research Institute, Beth Israel Deaconess Medical Center, Rutgers University, and the General Electric Corporation propose to undertake an Academic-Industrial Partnership study to improve prostate- cancer (PCA) imaging markedly and thereby improve prostate-biopsy guidance;enhance monitoring, surveillance, and treatment of PCa;and enable planning and execution of focal PCa therapy. The project will combine attributes of advanced ultrasound and magnetic-resonance techniques and embody them in a prototype imaging system capable of generating novel tissue-type images that reliably depict PCa in real-time.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA140772-05
Application #
8677765
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Baker, Houston
Project Start
2010-08-09
Project End
2015-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Riverside Research Institute
Department
Type
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10038
Wan, Tao; Bloch, B Nicolas; Plecha, Donna et al. (2016) A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores. Sci Rep 6:21394
Tiwari, P; Prasanna, P; Wolansky, L et al. (2016) Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study. AJNR Am J Neuroradiol 37:2231-2236
Litjens, Geert J S; Elliott, Robin; Shih, Natalie N C et al. (2016) Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging. Radiology 278:135-45
Xu, Jun; Xiang, Lei; Liu, Qingshan et al. (2016) Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images. IEEE Trans Med Imaging 35:119-30
Singanamalli, Asha; Rusu, Mirabela; Sparks, Rachel E et al. (2016) Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer. J Magn Reson Imaging 43:149-58
Prasanna, Prateek; Tiwari, Pallavi; Madabhushi, Anant (2016) Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor. Sci Rep 6:37241
Ginsburg, Shoshana B; Lee, George; Ali, Sahirzeeshan et al. (2016) Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology. IEEE Trans Med Imaging 35:76-88
Ginsburg, Shoshana B; Viswanath, Satish E; Bloch, B Nicolas et al. (2015) Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors. J Magn Reson Imaging 41:1383-93
Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant (2015) Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer. PLoS One 10:e0117900
Tiwari, Pallavi; Danish, Shabbar F; Jiang, Benjamin et al. (2015) Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings. J Med Imaging (Bellingham) 2:041008

Showing the most recent 10 out of 63 publications