Evidence map›Paper›PMID 39578495›Full record

ArticleScientific reports2024

Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy.

Zheng Tang, Edward Duckworth, Benjamin Mora, Bilal Al-Sarireh, Matthew Mortimer, Debdulal Roy

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Zheng TangDepartment of Computer Science and Mathematics, Swansea University, Swansea, SA2 8PP, UK.
Edward DuckworthConnectomX Ltd, Oxford, OX2 9BG, UK.
Benjamin MoraDepartment of Computer Science and Mathematics, Swansea University, Swansea, SA2 8PP, UK.
Bilal Al-SarirehMorriston Hospital, Heol Maes Eglwys, Morriston, SA6 6NL, UK.
Matthew MortimerMorriston Hospital, Heol Maes Eglwys, Morriston, SA6 6NL, UK.
Debdulal RoyDepartment of Chemistry, Swansea University, Swansea, SA2 8PP, UK. Deb.Roy@swansea.ac.uk.

Funding

European Commission POLightPOLight
6 · The paper itself

Abstract

This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learning pipeline comprising class-specific principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) in both real patient and synthetic data. By conducting separate PCA on cancerous and non-cancerous samples and integrating the projections prior to LDA and SVM classification, we demonstrate significantly improved diagnostic accuracy compared to traditional methods. This methodology not only enhances predictive performance but also offers deeper insights into the influence of molecular spectra on model efficacy. Our findings, validated on real patient data, suggest a promising avenue for developing non-invasive, accurate diagnostic tools for early-stage pancreatic cancer detection.

Indexed as

Early Detection of CancerPancreatic NeoplasmsPrincipal Component AnalysisSupport Vector MachineDiscriminant AnalysisHumansSpectroscopy, Fourier Transform Infrared

Identifiers

PMID39578495
PMCPMC11584613

What OpenQuestion 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 OpenQuestion graph.