Evidence map›Paper›PMID 31565506›Full record

ArticleBiomedical optics express2019

Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information.

Esther Kho, Behdad Dashtbozorg, Lisanne L de Boer, Koen K Van de Vijver, Henricus J C M Sterenborg, Theo J M Ruers

Open access · goldAbstract read
In one paragraph

Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
6.5field-weighted citation impact, top 3% 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

39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 74 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

6 authors at 2 institutions in 2 countries.

Esther KhoDepartment of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, Netherlands.ORCID https://orcid.org/0000-0001-9783-1588
Behdad DashtbozorgDepartment of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, Netherlands.
Lisanne L de BoerDepartment of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, Netherlands.
Koen K Van de VijverDepartment of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, Netherlands.
Henricus J C M SterenborgDepartment of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, Netherlands.
Theo J M RuersDepartment of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, Netherlands.
The Netherlands Cancer Institute · NLGhent University Hospital · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650 nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome.

Identifiers

PMID31565506
PMCPMC6757478
OpenAlexW2964619993

What OpenQuestion holds

Textmetadata
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.