Evidence map›Paper›PMID 28163974›Full record

ArticleJournal of pathology informatics2016

Enhancements in localized classification for uterine cervical cancer digital histology image assessment.

Peng Guo, Haidar Almubarak, Koyel Banerjee, R Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R Frazier, Randy H Moss and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pathology informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 12% 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

5 citing papers in PubMed, 13 citations in OpenAlex.

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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

11 authors at 4 institutions in 1 country.

Peng GuoDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
Haidar AlmubarakDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
Koyel BanerjeeDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
R Joe StanleyDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
Rodney LongLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, DHHS, Bethesda, MD, USA.
Sameer AntaniLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, DHHS, Bethesda, MD, USA.
George ThomaLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, DHHS, Bethesda, MD, USA.
Rosemary ZunaDepartment of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Shelliane R FrazierSurgical Pathology Department, University of Missouri Hospitals and Clinics, Columbia, MO, USA.
Randy H MossDepartment of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
William V StoeckerStoecker & Associates, Rolla, MO, USA.
Missouri University of Science and Technology · USNational Institutes of Health · USUniversity of Missouri Hospital · USUniversity of Oklahoma Health Sciences Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn previous research, we introduced an automated, localized, fusion-based approach for classifying uterine cervix squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on digitized histology image analysis. As part of the CIN assessment process, acellular and atypical cell concentration features were computed from vertical segment partitions of the epithelium region to quantize the relative distribution of nuclei.

methodsFeature data was extracted from 610 individual segments from 61 images for epithelium classification into categories of Normal, CIN1, CIN2, and CIN3. The classification results were compared against CIN labels obtained from two pathologists who visually assessed abnormality in the digitized histology images. In this study, individual vertical segment CIN classification accuracy improvement is reported using the logistic regression classifier for an expanded data set of 118 histology images.

resultsWe analyzed the effects on classification using the same pathologist labels for training and testing versus using one pathologist labels for training and the other for testing. Based on a leave-one-out approach for classifier training and testing, exact grade CIN accuracies of 81.29% and 88.98% were achieved for individual vertical segment and epithelium whole-image classification, respectively.

conclusionsThe Logistic and Random Tree classifiers outperformed the benchmark SVM and LDA classifiers from previous research. The Logistic Regression classifier yielded an improvement of 10.17% in CIN Exact grade classification results based on CIN labels for training-testing for the individual vertical segments and the whole image from the same single expert over the baseline approach using the reduced features. Overall, the CIN classification rates tended to be higher using the training-testing labels for the same expert than for training labels from one expert and testing labels from the other expert. The Exact class fusion- based CIN discrimination results obtained in this study are similar to the Exact class expert agreement rate.

Indexed as

Cervical cancercervical intraepithelial neoplasiafusion-based classificationimage processing

Identifiers

PMID28163974
PMCPMC5248401
OpenAlexW2566450153

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.