FOXP3-stained image analysis for follicular lymphoma: optimal adaptive thresholding with maximal nucleus coverage

Abstract

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 undersegmented 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-of-interest 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.* caglar.senaras@osumc.edu; phone: (614) 688-9753. HHS Public Access Description of PurposeAlthough there are several studies which focus on classification of follicular lymphoma (FL) [1][2][3], the impact of the number and distribution of T-lymphocytes on the outcome remains controversial. Some authors describe an association of increased T lymphocytes (TR) with improved survival [4], [5] and [6]; while others show a significantly negative impact with poorer survival and higher rates of progression to higher grade tumors [7]. These wide differences in outcomes may be related to the fact that the majority of these studies used tissue microarrays (TMAs) instead of whole sections of tumors that by design do not take into consideration FL heterogeneity. Moreover, enumeration of FOXP3 positive nuclei in these studies was based on manual methods that are notorious for poor reproducibility and high reader bias even among expert pathologists [8]. These discordant results create the need for an objective, accurate and highly reproducible computer based method capable of enumerating FOXP3 positive nuclei in high-resolution images of whole ...

Follicular lymphoma: first - line selection criteria of treatment

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What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

“…In [171], a new method for detecting and counting nuclei from FOXP3 stained FL images is proposed. The method defines a new process of adaptive thresholding, which is called optimal adaptive thresholding (OAT).…”

“…In the process of image segmentation in LHIA, the threshold-based and kmeans clustering methods are widely used. In [171], optimal adaptive thresholding is used to minimize under-and over-segmented nuclei. The sensitivity of 100% and maximum precisions of 100% show the method is useful in segmenting histopathology images.…”