Follicular lymphoma (FL) is the second most common non-Hodgkin's lymphoma. Several treatment options exist today, but these are costly and include significant toxicities. No biological or genetic markers are available in clinical practice for reliable risk stratification of follicular lymphomas and the choice of appropriate treatment depends heavily on morphology-based histological grading. In a system adopted by the World Health Organization (WHO), follicular lymphomas are stratified into three grades depending on the average count of centroblasts in ten randomly selected, standard high-power fields (HPFs). Follicular lymphomas with low histological grades show an indolent clinical course with long average survival, but are considered incurable with currently available therapies. In contrast, high-grade follicular lymphomas have an aggressive clinical course and are rapidly fatal if not treated with aggressive chemotherapy. However, in contrast to low-grade follicular lymphoma, high-grade FL may be cured with aggressive chemotherapy. Currently, the inter-reader agreement between pathologists in grading FL is extremely low. In a multi-site study, the agreement among experts for the various grades of follicular lymphoma varied between 61% and 73%. Since only ten HPFs are used by the pathologist for practical reasons, this system may be prone to selection bias in cases that show significant differences in various areas of a section. The primary goal of this project is to develop an effective computer-aided system to assist pathologists in making diagnostic decisions about histological grading of follicular lymphoma. It is important to note that this project aims to provide supplementary information to the pathologist as he or she carries out the classification process;this is not an attempt to automate the classification process. To achieve this objective, ten board-certified hematopathologists (with experience in grading follicular lymphoma) from The Ohio State University, Cleveland Clinic, Vanderbilt University, and private practice will participate in the creation of the database that will contain digitized follicular lymphoma slide images, as well as the associated truth for the development and evaluation of the computer-aided follicular lymphoma grading system. After extensive evaluation of the system with the collected datasets and outcome data, as well as datasets from the Cancer and Leukemia Group B (CALGB) trials, the developed system will be installed at participating pathologists'institutions, and the developed software will be made available to the research community as a shareable resource.

Public Health Relevance

Follicular lymphoma (FL) is the second most common non-Hodgkin's lymphoma. This project aims to provide supplementary information to the pathologist for the grading of the tumor using computerized image analysis techniques. The supplementary information will be useful for better diagnosis, prognosis and treatment of this disease.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA134451-03
Application #
8024533
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Tricoli, James
Project Start
2009-05-01
Project End
2013-02-28
Budget Start
2011-03-01
Budget End
2012-02-29
Support Year
3
Fiscal Year
2011
Total Cost
$272,401
Indirect Cost
Name
Ohio State University
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
832127323
City
Columbus
State
OH
Country
United States
Zip Code
43210
Smith, Barry; Arabandi, Sivaram; Brochhausen, Mathias et al. (2015) Biomedical imaging ontologies: A survey and proposal for future work. J Pathol Inform 6:37
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
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
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
Das, Hiranmoy; Wang, Zhihui; Niazi, M Khalid Khan et al. (2013) Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer. PLoS One 8:e61398
Oger, Myriam; Belhomme, Philippe; Gurcan, Metin N (2012) A general framework for the segmentation of follicular lymphoma virtual slides. Comput Med Imaging Graph 36:442-51
Akakin, Hatice Cinar; Gurcan, Metin N (2012) Content-based microscopic image retrieval system for multi-image queries. IEEE Trans Inf Technol Biomed 16:758-69
Samsi, Siddharth; Krishnamurthy, Ashok K; Gurcan, Metin N (2012) An Efficient Computational Framework for the Analysis of Whole Slide Images: Application to Follicular Lymphoma Immunohistochemistry. J Comput Sci 3:269-279
Ababneh, Sufyan Y; Prescott, Jeff W; Gurcan, Metin N (2011) Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research. Med Image Anal 15:438-48
Kong, Hui; Gurcan, Metin; Belkacem-Boussaid, Kamel (2011) Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imaging 30:1661-77

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