Interobserver and interlaboratory variability of mismatch repair protein expression in ovarian tumors
Bibliographic record
Abstract
e17505 Background: Immunohistochemistry (IHC) for mismatch repair (MMR) protein (MLH1, MSH2, MSH6) expression has been a useful strategy for identifying tumors with MMR deficiency. However, despite its wide use, interpretation of results suffers from poor reproducibility. Methods: To assess inter-observer (IO) and inter-laboratory (IL) variability of MMR protein expression, 41 epithelial ovarian cancer (EOC) samples were arrayed in triplicate for construction of a tissue microarray (TMA). Six slides were made from this donor TMA block, of which 3 were stained at the Moffitt Cancer Center (MCC) and 3 were stained at the University of South Florida (USF), using different lab procedures. IHC for MMR protein expression was performed using the avidin-biotin-complex (ABC) method with appropriate controls. Subsequently, all slides were independently scored for protein expression by two pathologists. The Concordance Correlation Coefficient (CCC) value was computed to evaluate IO and IL concordance, with a value >0.75 indicating excellent concordance. Results: The CCC value for the IO analysis was 0.95 (for MCC-stained slides; 95% C.I.: 0.89–0.98) and 0.85 (for USF-stained slides; 95% C.I.: 0.66, 0.93), indicating excellent concordance. The CCC value for IL analysis was 0.53 (95% C.I.: 0.37–0.66). Conclusions: Our findings demonstrate that variability in IHC protocols may contribute to the interpretation of IHC results. Our data suggest that when pathologists are given the same slide, there is excellent agreement between two observers; however, when the same slides are stained in a separate laboratory using the same method (ABC) but different protocol, there may be considerable disagreement. These findings are of great clinical significance due to the widespread use of IHC as diagnostic, prognostic and therapeutic tools in cancer care. No significant financial relationships to disclose.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".