Follicular Lymphoma (FL) is the second most common subtype of lymphoma in the Western World. It is a low-grade lymphoma arising from Germinal Centre (GC) B cells. The neoplasm predominantly consists of back-to-back arrangement of nodules or follicles of transformed GC B cells with the replacement of lymph node architecture and loss of normal cortex and medullary differentiation, which is preserved in non-neoplastic or reactive lymph node. There is a growing interest in studying different cell subsets inside and on the periphery of the follicles to direct curative therapies and minimize treatment-related complications. To facilitate this analysis, we develop an automated method for follicle detection from images of CD8 stained histopathological slides. The proposed method is trained on eight whole digital slides. The method is inspired by U-net to segment follicles from the whole slide images. The results on an independent dataset resulted in an average Dice similarity coefficient of 85.6% when compared to an expert pathologist’s annotations. We expect that the method will play a considerable role for comparing the ratios of different subsets of cells inside and at the periphery of the follicles.
Follicular Lymphoma (FL) is the second most common subtype of lymphoma in the Western World. In 2009, about 15,000 new cases of FL were diagnosed in the U.S. and approximately 120,000 patients were affected. Both the clinical course and prognosis of FL are variable, and at present, oncologists do not have evidence-based systems to assess risk and make individualized treatment choices. Our goal is to develop a clinically relevant, pathology-based prognostic model in FL utilizing a computer-assisted image analysis (CaIA) system to incorporate grade, tumor microenvironment, and immunohistochemical markers, thereby improving upon the existing prognostic models. Therefore, we developed an approach to estimate the outcome of the follicular lymphoma patients by analyzing the tumor microenvironment as represented by quantification of CD4, CD8, FoxP3 and Ki67 stains intra- and inter-follicular regions. In our experiments, we analyzed 15 patients, and we were able to correctly estimate the output for the 87.5% of the patient with no evidence of disease after the therapy/operation.
Immunohistochemical detection of FOXP3 antigen is a usable marker for detection of regulatory T lymphocytes (TR) in
formalin fixed and paraffin embedded sections of different types of tumor tissue. TR plays a major role in homeostasis
of normal immune systems where they prevent auto reactivity of the immune system towards the host. This beneficial
effect of TR is frequently “hijacked” by malignant cells where tumor-infiltrating regulatory T cells are recruited by the
malignant nuclei to inhibit the beneficial immune response of the host against the tumor cells. In the majority of human
solid tumors, an increased number of tumor-infiltrating FOXP3 positive TR is associated with worse outcome. However,
in follicular lymphoma (FL) the impact of the number and distribution of TR on the outcome still remains controversial.
In this study, we present a novel method to detect and enumerate nuclei from FOXP3 stained images of FL biopsies. The
proposed method defines a new adaptive thresholding procedure, namely the optimal adaptive thresholding (OAT)
method, which aims to minimize under-segmented and over-segmented nuclei for coarse segmentation. Next, we
integrate a parameter free elliptical arc and line segment detector (ELSD) as additional information to refine
segmentation results and to split most of the merged nuclei. Finally, we utilize a state-of-the-art super-pixel method,
Simple Linear Iterative Clustering (SLIC) to split the rest of the merged nuclei. Our dataset consists of 13 region-ofinterest
images containing 769 negative and 88 positive nuclei. Three expert pathologists evaluated the method and
reported sensitivity values in detecting negative and positive nuclei ranging from 83-100% and 90-95%, and precision
values of 98-100% and 99-100%, respectively. The proposed solution can be used to investigate the impact of FOXP3
positive nuclei on the outcome and prognosis in FL.