295 Validation of a Cytotechnologist Manual Counting Service for the Ki67 Index in Neuroendocrine Tumors of the Pancreas and Gastrointestinal Tract
Bibliographic record
Abstract
Pathologists routinely assess Ki67 immunohistochemistry to grade gastrointestinal and pancreatic neuroendocrine tumors. Unfortunately, manual counts of the Ki67 index are very time consuming, and “eyeball estimation” has been criticized for being unreliable. Manual Ki67 counts performed by cytotechnologists could potentially save pathologist time and improve accuracy. To assess the concordance between manual Ki67 index counts performed by cytotechnologists vs eyeball estimates and manual Ki67 counts by pathologists. Ki67 immunohistochemical stains were retrieved from archived gastrointestinal or pancreatic neuroendocrine tumor resections. We compared pathologist Ki67 eyeball estimates on glass slides and printed color images with manual counts by three cytotechnologists and gold standard manual Ki67 index counts by three pathologists. Tumor grade agreement between pathologist image eyeball estimate and gold standard pathologist manual count was fair (K value of 0.31 [95% CI 0.03–0.60]). In nine of 20 cases (45%), the mean pathologist eyeball estimate was one grade higher than the mean pathologist manual count. There was almost perfect agreement in classifying tumor grade between the mean cytotechnologist manual count and the mean pathologist manual count (K value of 0.91 [95% CI 0.7–1.0]). In 20 cases, there was only one grade disagreement between the two methods. Eyeball estimation by pathologists required less than one minute, while manual counts by pathologists required a mean of 17 minutes per case. Eyeball estimation of the Ki67 index has a high rate of tumor grade misclassification compared to manual counting. Cytotechnologist manual counts are accurate and save pathologists time.
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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 | 0.032 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".