ArticleJournal of cancer prevention2023
Immunohistochemistry versus PCR Technology for Molecular Subtyping of Breast Cancer: Multicentered Expereinces from Addis Ababa, Ethiopia.
Article in Journal of cancer prevention, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Breast Cancer Phenotypes in Africa: A Scoping Review and Meta-Analysis.JCO global oncology · 2023Pooled it
- Correlation of Hormone Receptors and HER-2/Neu Status with Histologic Grades of Invasive Ductal Carcinoma of Breast among Women at Myungsung Comprehensive Specialized Hospital, Addis Ababa, Ethiopia.Breast cancer : basic and clinical research · 2026Article
- Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry.NPJ digital medicine · 2025Article
- Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status.International journal of molecular sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of immunohistochemistry (IHC) for molecular characterization of breast cancer (BC) is of paramount importance; however, it is not universally standardized, subject to observer variability and quantifying is a challenge. An alternative molecular technology, such as endpoint reverse transcription (RT)-PCR gene expression analysis, may improve observer variability and diagnostic accuracy. This study was intended to compare IHC with the RT-PCR based technique and assess the potential of RT-PCR for molecular subtyping of BC. In this comparative cross-sectional study, 54 BC tissues were collected from three public hospitals in Addis Ababa and shipped to Gynaecology department at Martin-Luther University (Germany) for laboratory analysis. Only 41 samples were qualified for IHC and RT-PCR investigation of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 protein expression analysis. Kappa statistics was used to assess the concordance between the two techniques. The overall percent agreement between RT-PCR and IHC was 68.3% for ER (positive percent agreement [PPA] 71.1%; negative percent agreement [NPA] 33.3%), 39.0% for PR (PPA 14.3%; NPA 92.3%), and 82.9% for HER2 (PPA 62.5%; NPA 87.9%). Cohen's κ-values of 0.018 (< 0.20), 0.045 (< 0.200), and 0.481 (0.41-0.60) were generated for ER, PR, and HER2, respectively. Concordance for molecular subtypes was only 56.1% (23/41) and 0.20 kappa value. IHC and endpoint RT-PCR techniques have shown to be discordant for 43% samples. Molecular subtyping using endpoint RT-PCR was fairly concordant with IHC. Thus, endpoint RT-PCR may give an objective result, and can be applied for BC subtyping.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.