Pathologist Interobserver Variability of Histologic Features in Childhood Brain Tumors: Results from the CCG-945 Study
Bibliographic record
Abstract
In the Children's Cancer Group-945 trial, study design allowed estimation of overall interpathologist observational agreement for 6 histologic features frequently used in brain tumor diagnoses. We evaluated agreement between pairs of 5 experienced neuropathologists, who had knowledge of the general diagnoses prior to slide readings. We performed this study in an attempt to further improve pathologist interinstitutional agreement. The features mitosis, necrosis, and giant cells had "fair" overall kappa estimates of reproducibility of around 0.5, while endothelial proliferation had only a "poor" overall kappa of 0.35. The Rogot reproducibility index averaged 0.5 for pleomorphism and hyperchromia. The upper bounds for the 10 pair summary agreement estimates were at best 0.65 ("good") for all 6 features. These relatively low-reproducibility estimates for the very small number of histologic features being assessed in tumors institutionally diagnosed as high-grade gliomas indicate that neuropathologists either used different operational definitions or interpreted them differently. We found that we could rank the histologic features from best to worst agreement among study pathologists as necrosis, giant cells, mitosis, endothelial proliferation, hyperchromic nuclei, and pleomorphic cells. We suggest that neuropathologists involved in multi-institutional studies of putative therapies not discard these traditional histologic features, but rather develop standardized operational definitions and measure their variability before beginning the studies. Only after such histologic feature variability studies are conducted will we have the data to identify specific histologic features of value to clinicians and researchers. Agreement and strict adherence to improved nonsubjective diagnostic criteria would improve histologic feature reliability and, consequently, their usefulness in studies.
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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 | 0.043 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".