Tissue Microarrays Are an Effective Quality Assurance Tool for Diagnostic Immunohistochemistry
Bibliographic record
Abstract
There has been considerable variability in the reported results of immunohistochemical staining for some diagnostically relevant antigens. Our objectives in this study were to (1) use a multitumor tissue microarray with tissue from 351 cases received in our department, representing 16 normal tissues and 47 different tumor types, to compare immunohistochemical staining results in our laboratory with published data, using a panel of 22 antibodies; (2) assess interlaboratory variability of immunohistochemical staining for S-100 using this microarray; and (3) test the ability of hierarchical clustering analysis to group tumors by primary site, based on their immunostaining profile. Tissue microarrays consisting of duplicate 0.6-mm cores from blocks identified in the hospital archives were constructed and stained according to our usual protocols. Antibodies directed against the following antigens were used: B72.3, bcl-2, carcinoembryonic antigen, c-kit, pankeratin, CD 68, CD 99, CK 5/6, CK 7, CK 8/18, CK19, CK 20, CK 22, epithelial membrane antigen, estrogen receptor, melan-A, p53, placental alkaline phosphatase, S-100, synaptophysin, thyroid transcription factor-1, and vimentin. Staining results on the array cases were compared with published results, and hierarchical clustering analysis was performed based on the immunohistochemical staining results. Unstained slides of the multitumor tissue microarray were sent to five other diagnostic immunohistochemistry laboratories and stained for S-100 protein. The staining results from the different laboratories were compared. Staining results using our current methods and samples from our laboratory were compatible with those described in the literature for most antigens. Placental alkaline phosphatase staining was not specific with our protocol, showing staining of a broad spectrum of different tumors; this finding initiated a review of our recent requests for placental alkaline phosphatase immunostaining and revealed two instances in which placental alkaline phosphatase positivity was incorrectly interpreted as evidence of a germ cell tumor. S-100 staining was less sensitive but more specific for the diagnosis of melanoma or neural tumor in our laboratory, compared to some published reports. Assessment of interlaboratory variability of S-100 immunostaining showed that there was more frequent staining of carcinomas in some laboratories, resulting in decreased specificity of S-100 staining in distinguishing melanoma from carcinoma. Hierarchical clustering analysis showed a strong trend for tumors to cluster by tissue of origin, but there were significant exceptions. We conclude that multiple-tumor microarrays are an efficient method for assessing the sensitivity and specificity of staining with any antibody used diagnostically. As a tool for quality assurance, they offer the advantage of taking into account local differences in tissue fixation, processing, and staining. They also allow cost-effective assessment of interlaboratory variability in immunohistochemical staining. Results of hierarchical clustering analysis show the potential for panels of immunohistochemical stains to identify the primary site of metastatic carcinomas but also confirm the limitations of currently available antibodies in giving unequivocal tissue-specific staining patterns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".