The overall goals of this proposal are to: 1) Measure the prognostic impact of histologic grade (without and with computer assistance) of follicular lymphoma cases by comparing it with outcome measures; and 2) to develop a computer-assisted image analysis (CaIA) system to quantitatively assess the FL tumor microenvironment (TME); 3) Compare the effectiveness of combined prognostic measure incorporating grade (without and with computer assistance), TME parameters and existing FLIPI score. The proposed research aims to develop a clinically relevant, pathology-based prognostic model in FL utilizing computer image analysis to incorporate grade, tumor microenvironment (TME), and immunohistochemical (IHC) markers. Due to the variable clinical course in FL and increasing treatment options, a prognostic index would allow therapies to be tailored to the patient. Patients with high risk disease may benefit form more intensive therapy, while patients with low risk disease may be appropriate for lower intensity therapy with a more favorable side effect profile. Furthermore, a prognostic index, which includes pathologic features, may ultimately become more relevant in the era of biologically targeted therapies. Our objective is to use advanced image analysis techniques to perform a quantitative and topographical study of the normal and tumor microenvironment and use this study as well as improved and consistent grading options in improving the current prognostic index. Our long-term goal is to translate the improved prognostic index results as better treatment options to FL patients. We plan to pursue the following three specific aims for this project:
Specific Aim 1 : Measure the prognostic impact of histologic grade (without and with computer assistance) of follicular lymphoma cases by comparing it with outcome measures;
Specific Aim 2 : Measure the impact of FL tumor microenvironment by comparing TME parameters with outcome measures;
Specific Aim 3 : Compare the effectiveness of combined prognostic measure incorporating grade (without and with computer assistance), TME parameters and existing FLIPI score. We have formed an experienced team with expertise in FL pathology and oncology, imaging and image analysis, observer studies and biostatistics. Successful completion of this project will exert a sustained and powerful impact on the field by the virtue of its development of a platform for researchers and clinicians to quantitatively and objectively evaluate FL TME, to improve the FL grading, and to incorporate these developments to form a better prognostic index. Microscopic image analysis software, which will be developed for quantification, will be usable for other diseases such as breast cancer, for which TME is also known to be an important predictor of clinical status. The software and data to carry out this project will be made freely available to the research community.

Public Health Relevance

The proposed research is relevant to public health because it will assist in improving the prognosis of follicular lymphoma, which is the second most common subtype of lymphoma in the Western World. Thus, the project is relevant to NIH's mission because it aligns with the NIH's goal to foster innovative research strategies and their applications as a basis for ultimately protecting and improving health.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA134451-06
Application #
8894440
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Ossandon, Miguel
Project Start
2009-05-01
Project End
2018-05-31
Budget Start
2015-06-01
Budget End
2016-05-31
Support Year
6
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Ohio State University
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
832127323
City
Columbus
State
OH
Country
United States
Zip Code
43210
Niazi, Muhammad Khalid Khan; Senaras, Caglar; Pennell, Michael et al. (2018) Relationship between the Ki67 index and its area based approximation in breast cancer. BMC Cancer 18:867
Abas, Fazly S; Shana'ah, Arwa; Christian, Beth et al. (2017) Computer-assisted quantification of CD3+ T cells in follicular lymphoma. Cytometry A 91:609-621
Senaras, C; Pennell, M; Chen, W et al. (2017) FOXP3-stained image analysis for follicular lymphoma: Optimal adaptive thresholding with maximal nucleus coverage. Proc SPIE Int Soc Opt Eng 10140:
Gurcan, Metin N; Tomaszewski, John; Overton, James A et al. (2017) Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem. J Biomed Inform 66:129-135
Gurcan, Metin N (2016) Histopathological Image Analysis: Path to Acceptance through Evaluation. Microsc Microanal 22:1004-1005
Fauzi, Mohammad Faizal Ahmad; Pennell, Michael; Sahiner, Berkman et al. (2015) Classification of follicular lymphoma: the effect of computer aid on pathologists grading. BMC Med Inform Decis Mak 15:115
Smith, Barry; Arabandi, Sivaram; Brochhausen, Mathias et al. (2015) Biomedical imaging ontologies: A survey and proposal for future work. J Pathol Inform 6:37
Bokhari, Shahid H; Çatalyürek, Ümit V; Gurcan, Metin N (2014) Massively Multithreaded Maxflow for Image Segmentation on the Cray XMT-2. Concurr Comput 26:2836-2855
Kornaropoulos, Evgenios N; Niazi, M Khalid Khan; Lozanski, Gerard et al. (2014) Histopathological image analysis for centroblasts classification through dimensionality reduction approaches. Cytometry A 85:242-55
Lozanski, Gerard; Pennell, Michael; Shana'ah, Arwa et al. (2013) Inter-reader variability in follicular lymphoma grading: Conventional and digital reading. J Pathol Inform 4:30

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