Ki-67 Labelling Index as a Predictor of Invasive Features in Thyroid Cancer: Retrospective Analysis and Implications
Bibliographic record
Abstract
Background: Ki-67 immunostaining is commonly used in neuroendocrine tumors to estimate the proliferative index and for grading. This study investigates its association with the invasiveness of follicular-derived thyroid carcinomas (TCs). Methods: A retrospective analysis of patients with TC at three McGill University teaching hospitals between January 2018 and November 2023 was conducted. The inclusion criteria included patients with malignant thyroid tumors and accessible Ki-67 LI data from final pathology specimens. The data collected included patient demographics, Ki-67 LI values, and different invasiveness attributes, such as molecular mutations, the histological subtype, lymphovascular invasion (LVI), extrathyroidal extension (ETE), and positive lymph nodes (LNs). Results: In total, 212 patients met the inclusion criteria, of which 80.7% were females and 19.3% were males. The Ki-67 LI ranged from 1% to 30%, with the majority of the cases within the range of 1–15%. A significant association was observed between higher Ki-67 LI and high-risk histological subtypes of thyroid carcinoma (p < 0.001). Similarly, Ki-67 LI was significantly associated with LVI and positive LN metastasis (p < 0.001 and p = 0.036, respectively). However, no significant association was found between the Ki-67 LI and gene mutations or ETE (p = 0.133 and p = 0.190, respectively). Using percentiles to establish a cutoff, patients with a Ki-67 LI higher than 6.7 showed a higher likelihood of being associated with invasive features. Conclusion: Elevated Ki-67 LI can serve as an indicator of aggressiveness in follicular-derived TC, especially when associated with distinct histological subtypes, LVI and positive LNs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".