Evidence map›Paper›PMID 35773774›Full record

ArticleJournal of biomedical optics2022

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.

Mengkun Chen, Xu Feng, Matthew C Fox, Jason S Reichenberg, Fabiana C P S Lopes, Katherine R Sebastian, Mia K Markey, James W Tunnell

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical optics, 2022. 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
3.4field-weighted citation impact, top 8% of its field
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, 13 citations in OpenAlex.

  1. Review
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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

8 authors at 2 institutions in 1 country.

Mengkun ChenThe 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.
Matthew C FoxThe University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, United States.
Jason S ReichenbergThe University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, United States.
Fabiana C P S LopesThe University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, United States.
Katherine R SebastianThe University of Texas at Austin, Division of Dermatology, Dell Medical School, Austin, Texas, 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.
The University of Texas at Austin · USThe University of Texas MD Anderson Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

significanceRaman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues structures. Reflectance confocal microscopy (RCM) provides morphological and cytological details by which many features of epidermis and hair follicles can be readily identified. Combining RS with deep-learning-aided RCM has the potential to improve the diagnostic accuracy of RS in an automated fashion, without requiring additional input from the clinician.

aimThe aim of this study is to improve the specificity of RS for detecting basal cell carcinoma (BCC) using an artificial neural network trained on RCM images to identify false positive normal skin structures (hair follicles and epidermis). APPROACH: Our approach was to build a two-step classification model. In the first step, a Raman biophysical model that was used in prior work classified BCC tumors from normal tissue structures with high sensitivity. In the second step, 191 RCM images were collected from the same site as the Raman data and served as inputs for two ResNet50 networks. The networks selected the hair structure and epidermis images, respectively, within all images corresponding to the positive predictions of the Raman biophysical model with high specificity. The specificity of the BCC biophysical model was improved by moving the Raman spectra corresponding to these selected images from false positive to true negative.

resultsDeep-learning trained on RCM images removed 52% of false positive predictions from the Raman biophysical model result while maintaining a sensitivity of 100%. The specificity was improved from 84.2% using Raman spectra alone to 92.4% by integrating Raman spectra with RCM images.

conclusionsCombining RS with deep-learning-aided RCM imaging is a promising tool for guiding tumor resection surgery.

Indexed as

Basal Cell CarcinomaDeep LearningSkin NeoplasmsDermoscopyHumansMicroscopy, Confocalbasal cell carcinomadeep learningRaman spectroscopyreflectance confocal microscopy

Identifiers

PMID35773774
PMCPMC9243521
OpenAlexW4283721039

What OpenQuestion holds

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LicenceCC BY
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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.