This proposal will address PQC-4: """"""""What in vivo imaging methods can be developed to portray the """"""""cytotype"""""""" of a tumor defined as the identity, quantity, and location of each of the different cell types that make up a tumor and its microenvironment? An ideal system to address this question will have the following characteristics: 1) images and data should be obtained from human patients;2) the relationship between imaging and cytotypes should have clinical relevance;3) there should be a large amount and a balance in data obtained from within cancerous and non-cancerous volumes;4) the image data should be of high quality and ideally multiparametric;and 5) registration of histology to radiographic images must be feasible. Such criteria are met in prostate cancer patients who are being monitored by active surveillance (AS). The University of Miami (UM) has a large AS population, and patients with prostate cancer are regularly and routinely imaged with multiparametric MRI (MP- MRI) that includes diffusion (DWI), dynamic contrast enhancement (DCE) and T2 weighted (T2w) imaging sequences as standard of care (SOC). These images are fused to a transrectal ultrasound (TRUS) guidance instrument for biopsy localization. The singular goal of the current work is to develop predictive models that define this interrelationshi based on profound image analyses (""""""""radiomics"""""""") in combination with quantitative histology and immunohistochemistry from spatially co-registered volumes;thus defining the """"""""cytotypes"""""""" giving rise to MR image data. Researchers at the Moffitt Cancer Center have pioneered the application of radiomics and predictive (classifier like) modeling to cancer. Thus, this work will proceed with two interrelated aims.
In Aim 1, MR images, histology, gene expression and clinical data will be generated at UM via the MAST Trial: MRI- Guided Biopsy Selection for Active Surveillance versus Treatment.
In Aim 2, informatics data analysis, databasing and classifier modeling will be undertaken at Moffitt. Analysis of MR images will use a """"""""radiomics"""""""" approach, wherein 432 size, shape and texture features are extracted from image-identified habitats. These will be matched up to registered histology images analyzed with quantitative pathology wherein 32 features are extracted from each cell to form clusters of similar morphotypes, as well as IHC for known and putative progression markers. From these quantitative markers, training and test set classifier models will be developed to relate the MR-defined habitats to their underlying mixtures of cytotypes. Because this will be a large and invaluable data base, it is our explicit intention to share the complete dataset, with the research community through material transfer agreements, which will allow alternative data mining schema.

Public Health Relevance

Virtually every cancer patient is imaged with CT, PET or MRI. Importantly, such imaging reveals that tumors are complex and heterogeneous, often containing multiple habitats within them. It is the intention of this research to deeply analyze these image so we can infer what is the cellular and molecular structure in each of these habitats. This will be performed in a large cohort of men with prostate cancer who are under active surveillance. In other words, they have cancer, but it is not known if the cancer is life-threatening or not. Men under active surveillance receive regular MRI scans and regular biopsies. The MRI and biopsy data will be quantitatively compared in order for us to understand the cellular and molecular basis for these different habitats in cancers.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-GRB-P (A1))
Program Officer
Nordstrom, Robert J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
H. Lee Moffitt Cancer Center & Research Institute
United States
Zip Code
Gillies, Robert J; Kinahan, Paul E; Hricak, Hedvig (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278:563-77
Parra, N Andres; Orman, Amber; Padgett, Kyle et al. (2016) Dynamic contrast-enhanced MRI for automatic detection of foci of residual or recurrent disease after prostatectomy. Strahlenther Onkol :
Stoyanova, Radka; Pollack, Alan; Takhar, Mandeep et al. (2016) Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget :
Kryvenko, Oleksandr N; Lyapichev, Kirill; Chinea, Felix M et al. (2016) Radical Prostatectomy Findings in White Hispanic/Latino Men With NCCN Very Low-risk Prostate Cancer Detected by Template Biopsy. Am J Surg Pathol 40:1125-32