Evidence map›Paper›PMID 31780698›Full record

ArticleScientific reports2019

Histopathology Feature Mining and Association with Hyperspectral Imaging for the Detection of Squamous Neoplasia.

Guolan Lu, Dongsheng Wang, Xulei Qin, Susan Muller, James V Little, Xu Wang, Amy Y Chen, Georgia Chen, Baowei Fei

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
1.7field-weighted citation impact, top 15% 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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.

  1. Pooled it
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  10. Proceedings of SPIE--the International Society for Optical Engineering · 2020
    Article
  11. An automatic whole-slide hyperspectral imaging microscope.Proceedings of SPIE--the International Society for Optical Engineering
    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 2 institutions in 1 country.

Guolan LuDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
Dongsheng WangDepartment of Hematology and Medical Oncology, Emory University, Atlanta, GA, USA.
Xulei QinDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Susan MullerDepartment of Otolaryngology, Emory University School of Medicine, Atlanta, GA, USA.
James V LittleDepartment of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA.
Xu WangDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Amy Y ChenDepartment of Otolaryngology, Emory University School of Medicine, Atlanta, GA, USA.
Georgia ChenDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Baowei FeiDepartment of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA. bfei@utdallas.edu.ORCID http://orcid.org/0000-0002-9123-9484
Emory University · USGeorgia Institute of Technology · US

Funding

Image-guided Intravascular Robotic System for Mitral Valve Repair and ImplantsR01HL140325 · NHLBI · GEORGIA INSTITUTE OF TECHNOLOGY · PI DESAI, JAYDEV P., FEI, BAOWEI · 2018 to 2021
$3.0M
ACADEMIC-INDUSTRIAL PARTNERSHIP FOR TRANSLATION OF PET/TRUS GUIDED INTERVENTIONR01CA204254 · NCI · UNIVERSITY OF TEXAS DALLAS · PI FEI, BAOWEI · 2017 to 2021
$2.0M
MOLECULAR IMAGING DIRECTED, 3D ULTRASOUND-GUIDED, BIOPSY SYSTEMR01CA156775 · NCI · UNIVERSITY OF TEXAS DALLAS · PI FEI, BAOWEI · 2011 to 2016
$1.7M
NCI NIH HHS R01 CA156775NCI NIH HHS R01 CA204254NHLBI NIH HHS R01 HL140325
6 · The paper itself

Abstract

Hyperspectral imaging (HSI) is a noninvasive optical modality that holds promise for early detection of tongue lesions. Spectral signatures generated by HSI contain important diagnostic information that can be used to predict the disease status of the examined biological tissue. However, the underlying pathophysiology for the spectral difference between normal and neoplastic tissue is not well understood. Here, we propose to leverage digital pathology and predictive modeling to select the most discriminative features from digitized histological images to differentiate tongue neoplasia from normal tissue, and then correlate these discriminative pathological features with corresponding spectral signatures of the neoplasia. We demonstrated the association between the histological features quantifying the architectural features of neoplasia on a microscopic scale, with the spectral signature of the corresponding tissue measured by HSI on a macroscopic level. This study may provide insight into the pathophysiology underlying the hyperspectral dataset.

Indexed as

AnimalsCarcinoma, Squamous CellDiagnosis, Computer-AssistedFemaleHumansMiceMice, Inbred CBAMouth NeoplasmsOptical Imaging

Identifiers

PMID31780698
PMCPMC6882850
OpenAlexW2989601473

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.