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Abstract PR-05: Alterations in tissue autofluorescence using spectroscopy in high-risk oral lesions

2010· article· en· W1996980168 on OpenAlexaffabout
Catherine F. Poh, Evan J. Wiens, Pierre Lane, Sylvia S.W. Au, Miriam P. Rosin, Calum MacAulay

Bibliographic record

VenueCancer Prevention Research · 2010
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAutofluorescenceDysplasiaMedicinePathologyCancerExcitation wavelengthOral mucosaInternal medicineFluorescenceOptics

Abstract

fetched live from OpenAlex

Abstract High-risk oral lesions can be sometimes difficult to discriminate from reactive oral lesions, which are commonly seen within community settings. New tools need to be developed to aid in screening and early detection of high-risk lesions. The assessment of alteration in tissue autofluorescence using spectroscopy has demonstrated promising results in distinguishing cancerous from normal tissue at many organs and sites; however, there is limited information on its usage to distinguish reactive lesions (as seen by both white-light and autofluoescence imaging) from cancerous/precancerous lesions. Objectives of this study are: 1) to collect autofluorescence spectra from normal mucosa, mucosa with high-risk histological change and those with chronic inflammation under different excitation wavelengths of light, and 2) to compare the change in tissue autofluorescence in different exciting wavelengths among these oral mucosal lesions. Methods: Patients with high-risk oral lesions and inflammatory conditions were recruited from the Dysplasia Clinics of the BC Oral Cancer Prevention Program. Spectroscopic measurements were taken using a fiber optic probe of a Remiscope (Remicalm, LLC; Houston, TX). Three excitation wavelengths were used: 436 nm, 405 nm, and 355 nm. Percent loss of peak emission intensity (%PEI) was measured by comparing the PEI of the lesional and contralateral normal areas. Differences in %PEI between groups were compared using unpaired t-test. Results: From June to September 2009, 102 spectroscopic measurements were recorded from 17 patients (cancer, 5; dysplasia, 6; inflammation, 6). Among these, loss of tissue autofluorescence under 436 nm, 405 nm, and 355 nm was observed in all cases. When comparing the %PEI between cancer and dysplasia groups, increased loss was seen in the cancer group at all three excitation wavelengths, especially under 355 nm and 405 nm excitations (P = 0.023 and 0.045 respectively). When comparing to the inflammation group, there is almost the same degree of loss observed between cancer and inflammation groups in all 3 excitation wavelengths. Interestingly, there is a significant difference in %PEI observed between dysplasia and inflammation group under 436 nm and 355 nm excitations (P = 0.005 and 0.022 respectively) but no statistical difference under 405 nm excitation. Conclusions: This is the first study to use 3 different excitation wavelengths of light to examine the spectra of oral cancerous, precancerous, and specifically inflammatory oral lesions (as seen by both white-light and autofluoescence imaging). This device has shown its potential to provide an objective, sensitive approach to distinguish precancers from those commonly seen within community settings caused by chronic inflammation. (Supported by grant R01 DE17013 from the National Institute of Dental and Craniofacial Research and grant CCSRI-20336 from Canadian Cancer Society Research Institute. CFP is supported by a Clinician Scientist Award from the Canadian Institutes of Health Research and a Scholar Award from the Michael Smith Foundation for Health Research). Citation Information: Cancer Prev Res 2010;3(1 Suppl):PR-05.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.491
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2010
Admission routes2
Has abstractyes

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