Evidence map›Paper›PMID 39553684›Full record

ArticleHeliyon2024

Towards reliable hyperspectral imaging biomarkers of CT26 murine tumor model.

Tadej Tomanic, Jost Stergar, Tim Bozic, Bostjan Markelc, Simona Kranjc Brezar, Gregor Sersa, Matija Milanic

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Tadej TomanicFaculty of Mathematics and Physics, University of Ljubljana, 1000 Ljubljana, Slovenia.
Jost StergarFaculty of Mathematics and Physics, University of Ljubljana, 1000 Ljubljana, Slovenia.
Tim BozicDepartment of Experimental Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia.
Bostjan MarkelcDepartment of Experimental Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia.
Simona Kranjc BrezarDepartment of Experimental Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia.
Gregor SersaDepartment of Experimental Oncology, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia.
Matija MilanicFaculty of Mathematics and Physics, University of Ljubljana, 1000 Ljubljana, Slovenia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The non-invasive monitoring of tumor growth can offer invaluable diagnostic insights and enhance our understanding of tumors and their microenvironment. Integrating hyperspectral imaging (HSI) with three-dimensional optical profilometry (3D OP) makes contactless and non-invasive tumor diagnosis possible by utilizing the inherent tissue contrast provided by visible (VIS) and near-infrared (NIR) light. Consequently, valuable information regarding tumors and healthy tissues can be extracted from the acquired hyperspectral images. Until now, very few methods have been used to monitor tumor models

Indexed as

BiomarkersHyperspectral imagingMachine learningMurine modelsTumors

Identifiers

PMID39553684
PMCPMC11567117

What Socratic holds

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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 Socratic graph.