Evidence map›Paper›PMID 34558235›Full record

ArticleJournal of biomedical optics2021

Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging.

Andrew C Stier, Will Goth, Aislinn Hurley, Treshayla Brown, Xu Feng, Yao Zhang, Fabiana C P S Lopes, Katherine R Sebastian, Pengyu Ren, Matthew C Fox and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical optics, 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
–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

8 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

13 authors.

Andrew C StierThe University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, Texas, United States.
Will GothThe University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.
Aislinn HurleyThe University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.
Treshayla BrownThe University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.
Xu FengThe University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.
Yao ZhangThe University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.
Fabiana C P S LopesThe University of Texas at Austin, Dell Medical School, Department of Internal Medicine, Austin, Tex, United States.
Katherine R SebastianThe University of Texas at Austin, Dell Medical School, Department of Internal Medicine, Austin, Tex, United States.
Pengyu RenThe University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.
Matthew C FoxThe University of Texas at Austin, Dell Medical School, Department of Internal Medicine, Austin, Tex, United States.
Jason S ReichenbergThe University of Texas at Austin, Dell Medical School, Department of Internal Medicine, Austin, Tex, United States.
Mia K MarkeyThe University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.
James W TunnellThe University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

significanceSub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications.

aimOur study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes. APPROACH: A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue.

resultsThe model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes.

conclusionThese results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Indexed as

NeoplasmsOptical ImagingHumansMachine LearningPhantoms, ImagingSkinartificial neural networklight scatteringmachine learningoptical propertiestissue optics

Identifiers

PMID34558235
PMCPMC8459901

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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.