Evidence map›Paper›PMID 39742670›Full record

ArticleAmerican journal of otolaryngology

Non-invasive screening for laryngeal cancer using the oral cavity as a proxy for differentiation of laryngeal cancer versus leukoplakia: A novel application of ESS technology and artificial intelligence supported statistical modeling.

M Sakharkar, G Spokas, L Berry, K Daniels, P Nithagon, E Rodriguez-Diaz, L Tracy, J P Noordzij, I Bigio, G Grillone and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of otolaryngology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

11 authors.

M SakharkarBoston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA. Electronic address: mitalisa@bu.edu.
G SpokasBoston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA; Department of Otolaryngology, Boston Medical Center, 800 Harrison Avenue, Boston, MA 02118, USA. Electronic address: gabriele.spokas@bmc.org.
L BerryBoston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA; Department of Otolaryngology, Boston Medical Center, 800 Harrison Avenue, Boston, MA 02118, USA. Electronic address: lucas.berry@bmc.org.
K DanielsAugusta University/University of Georgia Medical Partnership, Medical College of Georgia, Athens, GA, USA. Electronic address: KADDANIELS@augusta.edu.
P NithagonBoston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA. Electronic address: nithagon@bu.edu.
E Rodriguez-DiazDepartment of Biomedical Engineering, Boston University, Boston, MA, USA. Electronic address: eladior@bu.edu.
L TracyBoston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA; Department of Otolaryngology, Boston Medical Center, 800 Harrison Avenue, Boston, MA 02118, USA.
J P NoordzijBoston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA; Department of Otolaryngology, Boston Medical Center, 800 Harrison Avenue, Boston, MA 02118, USA.
I BigioDepartment of Biomedical Engineering, Boston University, Boston, MA, USA; Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA. Electronic address: bigio@bu.edu.
G GrilloneBoston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA; Department of Otolaryngology, Boston Medical Center, 800 Harrison Avenue, Boston, MA 02118, USA. Electronic address: gregory.grillone@bmc.org.
G P KrisciunasBoston University Chobanian and Avedisian School of Medicine, 72 E. Concord Street, Boston, MA 02118, USA; Department of Otolaryngology, Boston Medical Center, 800 Harrison Avenue, Boston, MA 02118, USA. Electronic address: gintas@bu.edu.

Funding

Validation of Light Scattering Spectroscopy for Intra-operative Margin Guidance during Oral Cancer ResectionR01DE029862 · NIDCR · BOSTON MEDICAL CENTER · PI GRILLONE, GREGORY A · 2020 to 2024
$3.6M
NIDCR NIH HHS R01 DE029862
6 · The paper itself

Abstract

objectiveThis preliminary study tested whether non-invasive, remote Elastic Scattering Spectroscopy (ESS) measurements obtained in the oral cavity can be used as a proxy to accurately differentiate between patients with laryngeal cancer versus laryngeal leukoplakia.

methods20 patients with laryngeal lesions [cancer (n = 10),leukoplakia (n = 10)] were clinically assessed and categorized by otolaryngologists per standard clinical practice. Patient demographics of age, race, sex, and smoking history were collected. A machine-learning artificial intelligence (AI) algorithm was applied to classify patients using ESS spectra of patients with benign laryngeal leukoplakia or laryngeal cancer. Specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), F1, and area-under-the-curve (AUC) were calculated. Additional algorithms stratified spectral data by sub-anatomical site and smoking status to explore diagnostic capability.

resultsOverall, the algorithm had a sensitivity = 74 %, specificity = 40 %, PPV = 51 %, NPV = 64 %, F1 = 0.61 and AUC = 0.65. When stratifying by former and active smokers, algorithm sensitivities increased to 85 % and 77 %. Analysis by sub-anatomic location yielded an AUC = 0.77 for lateral tongue, and when stratified by (former/current) smoking status, demonstrated AUC = 0.94 and 0.83, sensitivities = 98 % and 76 %, and specificities = 85 % and 86 %. Algorithm output from the mucosal lip yielded sensitivity = 89 %, specificity = 88 %, PPV = 83 %, and NPV = 92 % in former smokers.

conclusionThis pilot study demonstrated ESS technology coupled with AI-assisted statistical modeling, could differentiate between patients with laryngeal leukoplakia versus cancer with good precision, especially with smoking status and anatomic subclassification. If ESS can be utilized in the oral cavity as a non-invasive screening tool for laryngeal cancer, it would greatly facilitate early detection in specialized/non-specialized clinics, and under-resourced regions.

Indexed as

Artificial IntelligenceEarly Detection of CancerLaryngeal NeoplasmsLeukoplakiaMouthSpectrum AnalysisAgedAlgorithmsDiagnosis, DifferentialFemaleHumansMachine LearningMaleMiddle AgedModels, StatisticalSensitivity and SpecificityDiagnosticsElastic scattering spectroscopyLaryngeal cancerLeukoplakiaMachine learningNon-invasive

Identifiers

PMID39742670
PMCPMC11825272

What Socratic holds

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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 Socratic graph.