Abstract
Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositories of FFPE specimens support the construction of tissue microarrays (TMAs) for high throughput studies. Due to the ubiquitous availability of IHC techniques in clinical laboratories, validated IHC biomarkers may be translated readily into clinical use. However, the method of pathologist semi-quantification is costly, inherently subjective, and produces ordinal rather than continuous variable data. Computer-aided analysis of digitized whole slide images may overcome these limitations. Using TMAs representing 215 ovarian serous carcinoma specimens stained for S100A1, we assessed the degree to which data obtained using computer-aided methods correlated with data obtained by pathologist visual scoring. To evaluate computer-aided image classification, IHC staining within pathologist annotated and software-classified areas of carcinoma were compared for each case. Two metrics for IHC staining were used: the percentage of carcinoma with S100A1 staining (%Pos), and the product of the staining intensity (optical density [OD] of staining) multiplied by the percentage of carcinoma with S100A1 staining (OD*%Pos). A comparison of the IHC staining data obtained from manual annotations and software-derived annotations showed strong agreement, indicating that software efficiently classifies carcinomatous areas within IHC slide images. Comparisons of IHC intensity data derived using pixel analysis software versus pathologist visual scoring demonstrated high Spearman correlations of 0.88 for %Pos (p < 0.0001) and 0.90 for OD*%Pos (p < 0.0001). This study demonstrated that computer-aided methods to classify image areas of interest (e.g., carcinomatous areas of tissue specimens) and quantify IHC staining intensity within those areas can produce highly similar data to visual evaluation by a pathologist.
| Original language | English (US) |
|---|---|
| Article number | 42 |
| Journal | Diagnostic Pathology |
| Volume | 7 |
| Issue number | 1 |
| DOIs | |
| State | Published - Apr 19 2012 |
Bibliographical note
Funding Information:This work was supported by NIH grants R01-CA131013 (G Metzger) and R01-CA106878 (A Skubitz), and Minnesota Medical Foundation grants 3824-9202-08 (S Schmechel) and 3850-9295-08 (A Johnson). These studies utilized BioNet histology and digital imaging core facilities which are supported by NIH grants P30-CA77598 (D Yee), P50-CA101955 (D Buchsbaum) and KL2-RR033182 (B Blazar), and by the University of Minnesota Academic Health Center. Computations were performed using computer resources provided by Dr. Timothy Schacker who is supported by NIH grants P01-AI074340 and R01-AI093319.
Keywords
- Annotation
- Color deconvolution
- Digital pathology
- Immunohistochemistry
- Intensity
- Quantification
- Software
