Evidence map›Paper›PMID 28579665›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2017

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

C Senaras, M Pennell, W Chen, B Sahiner, A Shana'ah, A Louissaint, R P Hasserjian, G Lozanski, M N Gurcan

Open access · greenAbstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

C SenarasDepartment of Biomedical Informatics, The Ohio State University.
M PennellDivision of Biostatistics, College of Public Health, The Ohio State University.
W ChenOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, S.
B SahinerOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, S.
A Shana'ahDepartment of Pathology, The Ohio State University.
A LouissaintDepartment of Pathology Massachusetts General Hospital.
R P HasserjianDepartment of Pathology Massachusetts General Hospital.
G LozanskiDepartment of Pathology, The Ohio State University.
M N GurcanDepartment of Biomedical Informatics, The Ohio State University.
The Ohio State University · USMassachusetts General Hospital · USUnited States Food and Drug Administration · US

Funding

Pathology Image Informatics Platform for visualization, analysis and managementU24CA199374 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI GURCAN, METIN NAFI, MADABHUSHI, ANANT · 2015 to 2019
$2.9M
Computer-based assessment of tumor microenvironment (TME) in Follicular LymphomaR01CA134451 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI GURCAN, METIN NAFI, LOZANSKI, GERARD · 2009 to 2017
$2.3M
NCI NIH HHS R01 CA134451NCI NIH HHS U24 CA199374
6 · The paper itself

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 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-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.

Indexed as

cell nuclei detectionFollicular LymphomaFOXP3histopathologyoptimal adaptive thresholding

Identifiers

PMID28579665
PMCPMC5452684
OpenAlexW2591616513

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.