Differential Clinicopathological Risk and Prognosis of Major Papillary Thyroid Cancer Variants
Bibliographic record
Abstract
CONTEXT: Individualized management, incorporating papillary thyroid cancer (PTC) variant-specific risk, is conceivably a useful treatment strategy for PTC, which awaits comprehensive data demonstrating differential risks of PTC variants to support. OBJECTIVE: This study sought to establish the differential clinicopathological risk of major PTC variants: conventional PTC (CPTC), follicular-variant PTC (FVPTC), and tall-cell PTC (TCPTC). METHODS: This was a retrospective study of clinicopathological outcomes of 6282 PTC patients (4799 females and 1483 males) from 26 centers and The Cancer Genome Atlas in 14 countries with a median age of 44 years (interquartile range, 33-56 y) and median follow-up time of 37 months (interquartile range, 15-82 mo). RESULTS: The cohort consisted of 4702 (74.8%) patients with CPTC, 1126 (17.9%) with FVPTC, and 239 (3.8%) with TCPTC. The prevalence of high-risk parameters was significantly different among the three variants, including extrathyroidal invasion, lymph node metastasis, stages III/IV, disease recurrence, mortality, and the use (need) of radioiodine treatment (all P < .001), being highest in TCPTC, lowest in FVPTC, and intermediate in CPTC, following an order of TCPTC > CPTC ≫ FVPTC. Recurrence and mortality in TCPTC, CPTC, and FVPTC were 27.3 and 6.7%, 16.1 and 2.5%, and 9.1 and 0.6%, corresponding to events per 1000 person-years (95% confidence interval [CI]) of 92.47 (64.66-132.26) and 24.61 (12.31-49.21), 34.46 (30.71-38.66), and 5.87 (4.37-7.88), and 24.73 (18.34-33.35) and 1.68 (0.54-5.21), respectively. Mortality hazard ratios of CPTC and TCPTC over FVPTC were 3.44 (95% CI, 1.07-11.11) and 14.96 (95% CI, 3.93-56.89), respectively. Kaplan-Meier survival analyses showed the best prognosis in FVPTC, worst in TCPTC, and intermediate in CPTC in disease recurrence-free probability and disease-specific patient survival. This was particularly the case in patients at least 45 years old. CONCLUSION: This large multicenter study demonstrates differential prognostic risks of the three major PTC variants and establishes a unique risk order of TCPTC > CPTC ≫ FVPTC, providing important clinical implications for specific variant-based management of PTC.
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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 | 0.000 | 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".