Macrofollicular Variant of Follicular Thyroid Carcinoma: A Rare Underappreciated Pitfall in the Diagnosis of Thyroid Carcinoma
Bibliographic record
Abstract
Background: Follicular-patterned thyroid nodules predominantly composed of macrofollicular structures without nuclear atypia are generally regarded as benign (i.e., hyperplastic nodules or follicular adenomas). In line with this concept, fine-needle aspiration cytology (FNAC) also assigns a benign connotation to the presence of macrofollicular structures, unless thyrocytes present papillary thyroid carcinoma (PTC)-related nuclear features that raise the possibility of a macrofollicular variant of PTC. However, cases showing macrofollicular architecture, capsular invasion, and no PTC features can also be observed. Methods: We describe the clinical, cytological, histological, and molecular genetic features of four cases of encapsulated follicular neoplasms that presented histologically with a predominant (>70%) macrofollicular architecture, but which also showed clear signs of capsular invasion, and thus were classified as macrofollicular variant of follicular thyroid carcinoma (MV-FTC). Results: Cytologically, macrofollicular structures were identified in all cases, leading to a benign FNAC diagnosis in three of the four cases. Due to increasing nodule size, thyroidectomy was performed in all cases. Histology showed focal and limited capsular invasion, without vascular invasion. Next-generation sequencing (custom 394 gene panel) of each tumor compared with matched normal DNA revealed a total of 7 somatic variants, including dual (likely biallelic) mutations in the DICER1 gene in 2 patients. The clinical outcome was excellent in all cases. Conclusions: Similar to the classical minimally invasive follicular thyroid carcinoma, MV-FTC appears to behave indolently. MV-FTC has a high rate of false-negative FNAC results, but MV-FTC is very rare (<0.05% of all thyroidectomies) and apparently has an indolent behavior. Further studies comprising larger series are necessary to better clarify the biology of this diagnostically challenging rare tumor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".