Evidence map›Paper›PMID 40596143›Full record

ArticleScientific reports2025

Developing transferable and universal IR biomarkers for intraoperative colorectal cancer diagnosis via FTIR spectroscopy.

Ran Li, Heather C Allen

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

2 authors.

Ran LiDepartment of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, OH, 43210, USA.
Heather C AllenDepartment of Chemistry and Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, OH, 43210, USA. allen@chemistry.ohio-state.edu.

Funding

Cancer Margin Detection using Infrared Spectroscopy and Plasmon ResonanceR21CA167403 · NCI · OHIO STATE UNIVERSITY · PI ALLEN, HEATHER CECILE, COE, JAMES V. · 2012 to 2013
$345k
NCI NIH HHS R21 CA167403NIH HHS NIH R21 CA167403
6 · The paper itself

Abstract

Histological staining has long been the gold standard for cancer detection, but it is limited by subjectivity and delayed results. Fourier Transform Infrared Spectroscopy (FTIR) has emerged as a promising technique, offering the advantages of objectivity and real-time analysis. Despite its potential, IR biomarkers developed in different studies are based on varying experimental conditions, including different tissue preparation methods, instrumentation, and patient heterogeneity, which hinder their generalizability and transferability. In this study, tissue spectra from various colorectal cancer cases were systematically collected, and IR biomarkers were developed using the band ratios identified in these spectra. Their ability to differentiate cancer-bearing from non-cancer-bearing tissues was evaluated using a machine learning technique. The results show that the top three biomarkers are b1 (1740/1236), b2 (1740/1162) and b3 (1740/1080). When transferring these biomarkers to a new case (approved under the Ohio State University Institutional Review Board # 2011C0085), b1 effectively differentiates tumor, normal, and margin tissues, while the performance of the b2 and b3 are less satisfactory. This suggests that b1 is more robust and sensitive to key molecular features, whereas b2 and b3 are more likely to be affected by tissue heterogeneity or experimental variations. Combining biomarkers enhances tissue differentiation, but the enhancement plateaus after two to three key biomarkers, as adding more introduces overlapping or redundant information. This study provides a benchmark for future research, with the aim of advancing the clinical translation of noninvasive infrared spectroscopy for intraoperative applications.

Indexed as

Biomarkers, TumorColorectal NeoplasmsAgedFemaleHumansIntraoperative PeriodMachine LearningMaleMiddle AgedSpectroscopy, Fourier Transform InfraredBiomarkers, TumorCancer diagnosisElastic netFeature extractionFourier transform infrared spectroscopyMachine learning

Identifiers

PMID40596143
PMCPMC12215629

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Registered trials

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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.