Prevalence of Differentiated High-Grade Thyroid Carcinoma Among Well-Differentiated Tumors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The current edition of the World Health Organization (WHO) classification of endocrine tumors introduced grading for follicular cell-derived thyroid cancer. Tumors with necrosis and/or high mitotic count but not fulfilling the Turin criteria for poorly differentiated carcinoma will be reclassified as differentiated high-grade thyroid carcinoma (DHGTC). However, the impact of this reclassification has not been evaluated. In this study, we performed a systematic review and meta-analysis to estimate the prevalence of this new entry across thyroid tumor subtypes. Methods: In this systematic review and meta-analysis, studies reporting data on necrosis and/or mitoses in well-differentiated thyroid carcinoma (WDTC) were used to estimate the prevalence of DHGTC. Heterogeneity and potential publication bias were also evaluated. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed, and quality assessment was performed using a modification of the Newcastle–Ottawa scale. The study has been registered in the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD42022378716). Results: In clinically unselected patients, the prevalence of DHGTC in WDTC was 0.072 [95% confidence interval, CI, = 0.045–0.113]. The proportion of high-grade tumors greatly varied across growth patterns and subtypes. Overall, the prevalence of DHGTC was higher in follicular thyroid carcinoma (FTC; 0.146 [CI = 0.101–0.205]) than in papillary thyroid carcinoma (PTC; 0.059 [CI = 0.036–0.097]). Diffuse sclerosing, follicular, and classic subtype PTC had the lowest rates of high-grade features (i.e., 0.018 [CI = 0.004–0.084]; 0.036 [CI = 0.010–0.124]; and 0.042 [CI = 0.027–0.066], respectively), while a greater proportion of solid trabecular and histologically aggressive PTC could be reclassified as DHGTC (i.e., 0.154 [CI = 0.067–0.314] and 0.168 [CI = 0.108–0.252], respectively). Similar proportions were obtained for minimally and widely invasive FTC (i.e., 0.136 [CI = 0.058–0.287] and 0.152 [CI = 0.086–0.254], respectively). Finally, in a cohort of patients with poor prognosis (i.e., fatal cases, metastatic and radioiodine resistant tumors, cases with biochemical recurrence), the proportion of DHGTC was 0.287 [CI = 0.155–0.469]. Conclusions: Following the current WHO indications, some tumors will be reclassified as DHGTC. The proportion of tumors with high-grade features is relevant in FTC, solid trabecular, and histologically aggressive PTC subtypes. A remarkable enrichment in DHGTC among patients with poor prognosis confirms the negative impact of high-grade features on outcome.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.005 | 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 | 0.001 |
| 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".