Usefulness of Ki-67, Mitoses, and Tumor Size for Predicting Metastasis in Carcinoid Tumors of the Lung: A Study of 48 Cases at a Tertiary Care Centre in Canada
Bibliographic record
Abstract
Background. Evaluation of Ki-67 index in lung carcinoid tumors (LCTs) has been of interest in order to identify high risk subsets. Our objectives are (1) to evaluate the usefulness of Ki-67 index, mitoses, and tumor size in predicting metastasis and (2) to compare the Manual Conventional Method (MCM) and the Computer Assisted Image Analysis Method (CIAM) for Ki-67 calculation. Methods. We studied 48 patients with LCTs from two academic centres in Canada. For Ki-67 calculation, digital images of 5000 cells were counted using an image processing software and 2000 cells by MCM. Mitoses/10 HPF was counted. Results. We had 37 typical carcinoids (TCs) and 11 atypical carcinoids (ACs). 7/48 patients developed metastasis. There was a positive relationship between metastasis and carcinoid type (P = 0.039) and metastasis and mitoses (≥2) (P = 0.017). Although not statistically significant, the mean Ki-67 index for ACs was higher than for TCs (0.95% versus 0.72%, CIAM, P = 0.299). Similarly, although not statistically significant, the mean Ki-67 index for metastatic group (MG) was higher than for nonmetastatic group (NMG) (1.01% versus 0.71% by CIAM, P = 0.281). However when Ki-67 index data was categorized at various levels, there is suggestion of a useful cutoff (≥0.50%) to predict metastasis (P = 0.106, CIAM). A significantly higher proportion of patients with mitosis ≥2 and Ki-67 index ≥0.50% had metastasis (P = 0.033) compared to other patients. Similarly patients with tumor size ≥3 cm and Ki-67 ≥0.50% had a greater percentage of metastases than others (P = 0.039). Although there was a strong correlation between two (MCM versus CIAM) counting methods (r = 0.929, P = 0.001), overall the calculated Ki-67 index was slightly higher by MCM (range 0 to 6.4, mean 1.5) compared to CIAM (range 0 to 2.9, mean 0.75). Conclusion. This study confirms that mitoses ≥2 is a powerful predictor of metastasis in LCTs. Although this is a small sample size, there is suggestion that analysis of Ki-67 index along with mitoses and tumor size may be a useful adjunct for predicting metastasis in LCTs.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".