Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence
- DOI
- 10.1016/j.jpi.2025.100513
- Published
- 2025-11
- Container
- Journal of Pathology Informatics
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.jpi.2025.100513,
title = {Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence},
author = {Frederik Aidt and Elad Arbel and Itay Remer and Oded Ben-David and Amir Ben-Dor and Daniela Rabkin and Kirsten Hoff and Karin Salomon and Sarit Aviel-Ronen and Gitte Nielsen and Jens Mollerup and Lars Jacobsen and Anya Tsalenko},
year = {2025},
journal = {Journal of Pathology Informatics},
doi = {10.1016/j.jpi.2025.100513},
url = {https://doi.org/10.1016/j.jpi.2025.100513}
}RIS
TY - JOUR TI - Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence AU - Frederik Aidt AU - Elad Arbel AU - Itay Remer AU - Oded Ben-David AU - Amir Ben-Dor AU - Daniela Rabkin AU - Kirsten Hoff AU - Karin Salomon AU - Sarit Aviel-Ronen AU - Gitte Nielsen AU - Jens Mollerup AU - Lars Jacobsen AU - Anya Tsalenko PY - 2025 JO - Journal of Pathology Informatics DO - 10.1016/j.jpi.2025.100513 UR - https://doi.org/10.1016/j.jpi.2025.100513 ER -
APA
Aidt, F., Arbel, E., Remer, I., Ben-David, O., Ben-Dor, A., Rabkin, D., Hoff, K., Salomon, K., Aviel-Ronen, S., Nielsen, G., Mollerup, J., Jacobsen, L., & Tsalenko, A. (2025). Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence. Journal of Pathology Informatics. https://doi.org/10.1016/j.jpi.2025.100513
Source records
- crossref · retrieved 2026-09-26T08:44:40.607Z