Evidence map›Paper›PMID 30176769›Full record

ArticleTechnology in cancer research & treatment2018

Multiparametric High-Resolution MRI as a Tool for Mapping of Hypoxic Level in Tumors.

Igor Serša, Franci Bajd, Monika Savarin, Tanja Jesenko, Maja Čemažar, Gregor Serša

Open access · goldAbstract read
In one paragraph

Article in Technology in cancer research & treatment, 2018. 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
0.2field-weighted citation impact, top 49% 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, 9 citations in OpenAlex.

  1. Use of Radiomics in Characterizing Tumor Hypoxia.International journal of molecular sciences · 2025
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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 1 country.

Igor Serša1 Jožef Stefan Institute, Ljubljana, Slovenia.
Franci Bajd3 Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia.
Monika Savarin4 Institute of Oncology Ljubljana, Ljubljana, Slovenia.ORCID 0000-0002-8316-8110
Tanja Jesenko4 Institute of Oncology Ljubljana, Ljubljana, Slovenia.
Maja Čemažar4 Institute of Oncology Ljubljana, Ljubljana, Slovenia.ORCID 0000-0002-1418-1928
Gregor Serša4 Institute of Oncology Ljubljana, Ljubljana, Slovenia.ORCID 0000-0002-7641-5670
Institute of Oncology Ljubljana · SIUniversity of Ljubljana · SI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypoxia is a condition, common to most malignant tumors, where oxygen tension in the tissue is below the physiological level. Among consequences of tumor hypoxia is also altered cancer cell metabolism that contributes to cancer therapy resistance. Therefore, precise assessment of tumor hypoxia is important for monitoring the tumor treatment progression. In this study, we propose a simple model for prediction of hypoxic level in tumors based on multiparametric magnetic resonance imaging. The study was performed on B16F1 murine melanoma tumors ex vivo that were first magnetic resonance scanned and then analyzed for hypoxic level using hypoxia-inducable factor 1-alpha antibody staining. Each tumor was analyzed in identical sections and in identical regions of interest for pairs of hypoxic level and magnetic resonance values (apparent diffusion coefficient and T

Indexed as

PrognosisAnimalsDiffusion Magnetic Resonance ImagingHumansHypoxia-Inducible Factor 1, alpha SubunitMelanoma, ExperimentalMiceTumor HypoxiaHif1a protein, mouseHypoxia-Inducible Factor 1, alpha Subunithypoxic levelMR microscopymultiparametric MRIpredictive modelstumors

Identifiers

PMID30176769
PMCPMC6122235
OpenAlexW2890917461

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.