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

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2015
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  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
Classification of follicular lymphoma: the effect of computer aid on pathologists grading
Published in
BMC Medical Informatics and Decision Making, December 2015
DOI 10.1186/s12911-015-0235-6
Pubmed ID
Authors

Mohammad Faizal Ahmad Fauzi, Michael Pennell, Berkman Sahiner, Weijie Chen, Arwa Shana’ah, Jessica Hemminger, Alejandro Gru, Habibe Kurt, Michael Losos, Amy Joehlin-Price, Christina Kavran, Stephen M. Smith, Nicholas Nowacki, Sharmeen Mansor, Gerard Lozanski, Metin N. Gurcan

Abstract

Follicular 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. In 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. FLAGS 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. The 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.

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The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 2 17%
Researcher 2 17%
Student > Ph. D. Student 2 17%
Other 1 8%
Student > Bachelor 1 8%
Other 3 25%
Unknown 1 8%
Readers by discipline Count As %
Medicine and Dentistry 6 50%
Biochemistry, Genetics and Molecular Biology 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Nursing and Health Professions 1 8%
Engineering 1 8%
Other 0 0%
Unknown 2 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 September 2021.
All research outputs
#6,755,994
of 25,377,790 outputs
Outputs from BMC Medical Informatics and Decision Making
#585
of 2,140 outputs
Outputs of similar age
#97,644
of 399,627 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#11
of 41 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,140 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 399,627 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.