Evidence map›Paper›PMID 34241603›Full record

ArticleThe Analyst2021

Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM.

Barnaby G Ellis, Conor A Whitley, Safaa Al Jedani, Caroline I Smith, Philip J Gunning, Paul Harrison, Paul Unsworth, Peter Gardner, Richard J Shaw, Steve D Barrett and 3 more

Open access · hybridAbstract read
In one paragraph

Article in The Analyst, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

8 citing papers in PubMed, 11 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

13 authors at 2 institutions in 4 countries.

Barnaby G EllisDepartment of Physics, University of Liverpool, L69 7ZE, UK. peterw@liverpool.ac.uk.
Conor A WhitleyDepartment of Physics, University of Liverpool, L69 7ZE, UK. peterw@liverpool.ac.uk.
Safaa Al JedaniDepartment of Physics, University of Liverpool, L69 7ZE, UK. peterw@liverpool.ac.uk.
Caroline I SmithDepartment of Physics, University of Liverpool, L69 7ZE, UK. peterw@liverpool.ac.uk.ORCID http://orcid.org/0000-0001-6878-0697
Philip J GunningDepartment of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, UK.ORCID http://orcid.org/0000-0003-1942-0171
Paul HarrisonDepartment of Physics, University of Liverpool, L69 7ZE, UK. peterw@liverpool.ac.uk.
Paul UnsworthDepartment of Physics, University of Liverpool, L69 7ZE, UK. peterw@liverpool.ac.uk.
Peter GardnerManchester Institute of Biotechnology, 131 Princess Street, University of Manchester, Manchester, M1 7DN, UK.ORCID http://orcid.org/0000-0003-4034-3764
Richard J ShawDepartment of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, UK and Regional Maxillofacial Unit, Aintree University Hospital, Liverpool, L9 7AL, UK.
Steve D BarrettDepartment of Physics, University of Liverpool, L69 7ZE, UK. peterw@liverpool.ac.uk.ORCID http://orcid.org/0000-0003-2960-3334
Asterios TriantafyllouDepartment of Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, L69 3GA, UK.ORCID http://orcid.org/0000-0001-8752-5595
Janet M RiskDepartment of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, UK.ORCID http://orcid.org/0000-0002-8770-7783
Peter WeightmanDepartment of Physics, University of Liverpool, L69 7ZE, UK. peterw@liverpool.ac.uk.ORCID http://orcid.org/0000-0002-0907-3930
University of Liverpool · GBUniversity of Manchester · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the ratio of Fourier transform infrared (FTIR) absorption intensities at 1252 cm-1 and 1285 cm-1. The metric yields discriminating sensitivities, specificities and precision of 98.8 ± 0.1%, 99.89 ± 0.01% and 99.78 ± 0.02% respectively, and an area under receiver operator characteristic (AUC) of 0.9935 ± 0.0006. The delineation of the OSCC and lymphoid tissue revealed by the image formed from the metric is in better agreement with an immunohistochemistry (IHC) stained image than are either of the FTIR images obtained at the individual wavenumbers. Scanning near-field optical microscopy (SNOM) images of the tissue obtained at a number of key wavenumbers, with high spatial resolution, show variations in the chemical structure of the tissue with a feature size down to ∼4 μm. The image formed from the ratio of the SNOM images obtained at 1252 cm-1 and 1285 cm-1 shows more contrast than the SNOM images obtained at these or a number of other individual wavenumbers. The discrimination between the two tissue types is dominated by the contribution from the 1252 cm-1 signal, which is representative of nucleic acids, and this shows the OSCC tissue to be accompanied by two wide arcs of tissue which are particularly low in nucleic acids. Haematoxylin and eosin (H&E) staining shows the tumour core in this specimen to be ∼40 μm wide and the SNOM topography shows that the core centre is raised by ∼1 μm compared to the surrounding tissue. Line profiles of the SNOM signal intensity taken through the highly keratinised core show that the increase in height correlates with an increase in the protein signal. SNOM line profiles show that the nucleic acids signal decreases at the centre of the tumour core between two peaks of higher intensity. All these nucleic acid features are ∼25 μm wide, roughly the width of two cancer cells.

Indexed as

Carcinoma, Squamous CellMouth NeoplasmsAlgorithmsHumansMicroscopySpectroscopy, Fourier Transform Infrared

Identifiers

PMID34241603
PMCPMC8311263
OpenAlexW3180853865

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.