Evidence map›Paper›PMID 26715518›Full record

ArticleBMC medical informatics and decision making2015

Classification of follicular lymphoma: the effect of computer aid on pathologists grading.

Mohammad Faizal Ahmad Fauzi, Michael Pennell, Berkman Sahiner, Weijie Chen, Arwa Shana'ah, Jessica Hemminger, Alejandro Gru, Habibe Kurt, Michael Losos, Amy Joehlin-Price and 6 more

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In one paragraph

Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Review
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  11. Predicting cancer outcomes from histology and genomics using convolutional networks.Proceedings of the National Academy of Sciences of the United States of America · 2018
    Article
  12. Review
  13. Article
  14. Article
  15. FOXP3-stained image analysis for follicular lymphoma: Optimal adaptive thresholding with maximal nucleus coverage.Proceedings of SPIE--the International Society for Optical Engineering · 2017
    Article
  16. Review
  17. 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

16 authors.

Mohammad Faizal Ahmad FauziFaculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia.
Michael PennellDivision of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA.
Berkman SahinerCenter for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA.
Weijie ChenCenter for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA.
Arwa Shana'ahDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Jessica HemmingerDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Alejandro GruDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Habibe KurtDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Michael LososDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Amy Joehlin-PriceDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Christina KavranDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Stephen M SmithDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Nicholas NowackiDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Sharmeen MansorDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Gerard LozanskiDepartment of Pathology, The Ohio State University, Columbus, OH, USA.
Metin N GurcanDepartment of Biomedical Informatics, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA. metin.gurcan@osumc.edu.

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
Task-Specific Compression for Biomedical Big DataU01CA198945 · NCI · UNIVERSITY OF ARIZONA · PI BILGIN, ALI · 2015 to 2017
$1.6M
NCI NIH HHS R01 CA134451NCI NIH HHS R01CA134451NCI NIH HHS U01 CA198945NCI NIH HHS U24 CA199374NCI NIH HHS U24CA199374
6 · The paper itself

Abstract

backgroundFollicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias.

methodsIn this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured.

resultsFLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates "acceptable" diagnostic performance.

conclusionsThe results of this study show that FLAGS can be useful in increasing the pathologists' accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists' grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability.

Indexed as

Diagnosis, Computer-AssistedHumansImage Processing, Computer-AssistedLymphoma, FollicularNeoplasm Grading

Identifiers

PMID26715518
PMCPMC4696238

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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.