Evidence map›Paper›PMID 38325706›Full record

ReviewModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2024

Virtual Staining of Nonfixed Tissue Histology.

Nir Pillar, Yuzhu Li, Yijie Zhang, Aydogan Ozcan

Abstract readReview
In one paragraph

Review in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 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

4 authors.

Nir PillarElectrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California.
Yuzhu LiElectrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California.
Yijie ZhangElectrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California.
Aydogan OzcanElectrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California. Electronic address: ozcan@ucla.edu.

Funding

TRD3: Data Analytics and Intelligent Systems (AI-ML-DL-Visualization)P41EB032840 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Griffith R. Harsh, Laura Marcu · 2022 to 2026
$6.7M
NIBIB NIH HHS P41 EB032840
6 · The paper itself

Abstract

Surgical pathology workflow involves multiple labor-intensive steps, such as tissue removal, fixation, embedding, sectioning, staining, and microscopic examination. This process is time-consuming and costly and requires skilled technicians. In certain clinical scenarios, such as intraoperative consultations, there is a need for faster histologic evaluation to provide real-time surgical guidance. Currently, frozen section techniques involving hematoxylin and eosin (H&E) staining are used for intraoperative pathology consultations. However, these techniques have limitations, including a turnaround time of 20 to 30 minutes, staining artifacts, and potential tissue loss, negatively impacting accurate diagnosis. To address these challenges, researchers are exploring alternative optical imaging modalities for rapid microscopic tissue imaging. These modalities differ in optical characteristics, tissue preparation requirements, imaging equipment, and output image quality and format. Some of these imaging methods have been combined with computational algorithms to generate H&E-like images, which could greatly facilitate their adoption by pathologists. Here, we provide a comprehensive, organ-specific review of the latest advancements in emerging imaging modalities applied to nonfixed human tissue. We focused on studies that generated H&E-like images evaluated by pathologists. By presenting up-to-date research progress and clinical utility, this review serves as a valuable resource for scholars and clinicians, covering some of the major technical developments in this rapidly evolving field. It also offers insights into the potential benefits and drawbacks of alternative imaging modalities and their implications for improving patient care.

Indexed as

Pathology, SurgicalStaining and LabelingHumansOptical Imagingdeep learningfrozen sectionnovel imaging modalitiessurgical pathologyvirtual staining

Identifiers

PMID38325706
PMCPMC11918264

What OpenQuestion holds

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Registered trials

None linked

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.