Evidence map›Paper›PMID 39296407›Full record

ArticleBiomedical optics express2024

Automated analysis of scattering-based light sheet microscopy images of anal squamous intraepithelial lesions.

Yongjun Kim, Jingwei Zhao, Brooke Liang, Momoka Sugimura, Kenneth Marcelino, Rafael Romero, Ameer Nessaee, Carmella Ocaya, Koeun Lim, Denise Roe and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Yongjun KimDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721, USA.
Jingwei ZhaoJames C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA.
Brooke LiangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Momoka SugimuraJames C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA.
Kenneth MarcelinoJames C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA.
Rafael RomeroDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721, USA.
Ameer NessaeeDepartment of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, USA.
Carmella OcayaDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721, USA.
Koeun LimBiotronik Neuro, Lake Oswego, OR 97035, USA.
Denise RoeDepartment of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85721, USA.
Michelle J KhanDepartment of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Eric J YangDepartment of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, USA.
Dongkyun KangDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721, USA.

Funding

VITAMIN AP30CA023074 · NCI · UNIVERSITY OF ARIZONA · PI Teri L Bankhead · 1985 to 2026
$110.2M
Low-Cost, Handheld Light Sheet Microscope for Guiding Anal Cancer DiagnosisR21EB030079 · NIBIB · UNIVERSITY OF ARIZONA · PI KANG, DONGKYUN, YANG, ERIC · 2021 to 2023
$543k
NCI NIH HHS P30 CA023074NIBIB NIH HHS R21 EB030079
6 · The paper itself

Abstract

We developed an algorithm for automatically analyzing scattering-based light sheet microscopy (sLSM) images of anal squamous intraepithelial lesions. We developed a method for automatically segmenting sLSM images for nuclei and calculating seven features: nuclear intensity, intensity slope as a function of depth, nuclear-to-nuclear distance, nuclear-to-cytoplasm ratio, cell density, nuclear area, and proportion of pixels corresponding to nuclei. 187 images from 80 anal biopsies were used for feature analysis and classifier development. The automated nuclear segmentation method provided reliable performance with the precision of 0.97 and recall of 0.91 when compared with the manual segmentation. Among the seven features, six showed statistically significant differences between high-grade squamous intraepithelial lesion (HSIL) and non-HSIL (non-dysplastic or low-grade squamous intraepithelial lesion, LSIL). A classifier using linear support vector machine (SVM) achieved promising performance in diagnosing HSIL versus non-HSIL: sensitivity of 90%, specificity of 70%, and area under the curve (AUC) of 0.89 for per-image diagnosis, and sensitivity of 90%, specificity of 80%, and AUC of 0.92 for per-biopsy diagnosis.

Identifiers

PMID39296407
PMCPMC11407269

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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.