Evidence map›Paper›PMID 39335767›Full record

ReviewDiagnostics (Basel, Switzerland)2024

Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.

Zaka Ur Rehman, Mohammad Faizal Ahmad Fauzi, Wan Siti Halimatul Munirah Wan Ahmad, Fazly Salleh Abas, Phaik Leng Cheah, Seow Fan Chiew, Lai-Meng Looi

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2024. 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
–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

4 citing papers in PubMed.

  1. A Unified Deep Learning Framework for Instance Segmentation Across Diverse Cytological Stains.Cytopathology : official journal of the British Society for Clinical Cytology · 2026
    Article
  2. Article
  3. Review
  4. Review
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

7 authors.

Zaka Ur RehmanFaculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia.
Mohammad Faizal Ahmad FauziFaculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia.ORCID 0000-0001-5382-6269
Wan Siti Halimatul Munirah Wan AhmadFaculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia.ORCID 0000-0001-6364-6341
Fazly Salleh AbasFaculty of Engineering and Technology, Multimedia University, Bukit Beruang, Melaka 75450, Malaysia.ORCID 0000-0002-4308-131X
Phaik Leng CheahDepartment of Pathology, University Malaya-Medical Center, Kuala Lumpur 50603, Malaysia.
Seow Fan ChiewDepartment of Pathology, University Malaya-Medical Center, Kuala Lumpur 50603, Malaysia.
Lai-Meng LooiDepartment of Pathology, University Malaya-Medical Center, Kuala Lumpur 50603, Malaysia.ORCID 0000-0001-8325-0117

Funding

Fundamental Research Grant Scheme (FRGS), Malaysia FRGS/1/2020/ICT02/MMU/02/10
6 · The paper itself

Abstract

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20-25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on

Indexed as

deep learningfluorescent in situ hybridization (FISH)human epidermal growth factor receptor 2 (HER2)pathologiessilver-enhanced in situ hybridization (SISH)

Identifiers

PMID39335767
PMCPMC11430898

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.