ArticleDiagnostic pathology2014
Digital immunohistochemistry wizard: image analysis-assisted stereology tool to produce reference data set for calibration and quality control.
Article in Diagnostic pathology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology.Frontiers in oncology · 2022Article
- How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction.Histochemistry and cell biology · 2021Article
- Analytical validation of the Immunoscore and its associated prognostic value in patients with colon cancer.Journal for immunotherapy of cancer · 2020Article
- Construction and analysis of tissue microarrays in the era of digital pathology: a pilot study targeting CDX1 and CDX2 in a colon cancer cohort of 612 patients.The journal of pathology. Clinical research · 2017Article
- Expression of cell cycle markers is predictive of the response to primary systemic therapy of locally advanced breast cancer.Virchows Archiv : an international journal of pathology · 2016Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDigital image analysis (DIA) enables better reproducibility of immunohistochemistry (IHC) studies. Nevertheless, accuracy of the DIA methods needs to be ensured, demanding production of reference data sets. We have reported on methodology to calibrate DIA for Ki67 IHC in breast cancer tissue based on reference data obtained by stereology grid count. To produce the reference data more efficiently, we propose digital IHC wizard generating initial cell marks to be verified by experts.
methodsDigital images of proliferation marker Ki67 IHC from 158 patients (one tissue microarray spot per patient) with an invasive ductal carcinoma of the breast were used. Manual data (mD) were obtained by marking Ki67-positive and negative tumour cells, using a stereological method for 2D object enumeration. DIA was used as an initial step in stereology grid count to generate the digital data (dD) marks by Aperio Genie and Nuclear algorithms. The dD were collected into XML files from the DIA markup images and overlaid on the original spots along with the stereology grid. The expert correction of the dD marks resulted in corrected data (cD). The percentages of Ki67 positive tumour cells per spot in the mD, dD, and cD sets were compared by single linear regression analysis. Efficiency of cD production was estimated based on manual editing effort.
resultsThe percentage of Ki67-positive tumor cells was in very good agreement in the mD, dD, and cD sets: regression of cD from dD (R2=0.92) reflects the impact of the expert editing the dD as well as accuracy of the DIA used; regression of the cD from the mD (R2=0.94) represents the consistency of the DIA-assisted ground truth (cD) with the manual procedure. Nevertheless, the accuracy of detection of individual tumour cells was much lower: in average, 18 and 219 marks per spot were edited due to the Genie and Nuclear algorithm errors, respectively. The DIA-assisted cD production in our experiment saved approximately 2/3 of manual marking.
conclusionsDigital IHC wizard enabled DIA-assisted stereology to produce reference data in a consistent and efficient way. It can provide quality control measure for appraising accuracy of the DIA steps.
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