Diagnostic role of Ki-67 expression in distinguishing thyroid follicular carcinoma from follicular adenoma: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Differentiating follicular thyroid carcinoma (FTC) from follicular adenoma (FA) is challenging due to their histological similarities. This systematic review and meta-analysis aimed to assess the difference in Ki-67 expression between FTC and FA to evaluate its diagnostic utility. Methods: We conducted a comprehensive search of PubMed, Embase, Scopus, and Web of Science databases for studies reporting Ki-67 expression in FTC and FA. The quality of the included studies was assessed using the Newcastle–Ottawa Scale. A random-effects model was applied to calculate the pooled mean difference of the Ki-67 index, with heterogeneity assessed by the Cochran Q and I -squared tests. Meta-regression was used to explore sources of heterogeneity, and publication bias was evaluated using Egger’s test, Begg’s test, and funnel plot. Results: The meta-analysis revealed a pooled mean difference in Ki-67 expression between FTC and FA of 1.13 (0.63–1.63), indicating a significant difference. In addition, the difference in the Ki-67 index between minimally invasive follicular carcinoma and FA was 0.56 (0.12–1.00), which was also statistically significant. The heterogeneity among included studies was due to variations in Ki-67 index calculation methods. The reviewed studies demonstrated low sensitivity but high specificity of Ki-67 for differentiating FTC vs. FA, although diagnostic cut-offs were inconsistent. Conclusions: This study demonstrates a significant difference in Ki-67 expression between FTC and FA, supporting its potential role as a diagnostic marker. Further research is required to establish standardized diagnostic cut-offs and evaluate Ki-67’s sensitivity and specificity in clinical practice.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".