Three‐dimensional nuclear architecture distinguishes thyroid cancer histotypes
Bibliographic record
Abstract
Abstract Molecular markers can serve as diagnostic tools to support pathological analysis in thyroid neoplasms. However, because the same markers can be observed in some benign thyroid lesions, additional approaches are necessary to differentiate thyroid tumor subtypes, prevent overtreatment and tailor specific clinical management. This applies particularly to the recently described variant of thyroid cancer referred to as noninvasive follicular thyroid neoplasm with papillary‐like nuclear features (NIFTP). This variant has an estimated prevalence of 4.4% to 9.1% of all papillary thyroid carcinomas worldwide. We studied 60 thyroid lesions: 20 classical papillary thyroid carcinoma (CPTC), 20 follicular variant of PTC (FVPTC) and 20 NIFTP. We examined morphological and molecular features to identify parameters that can differentiate NIFTP from the other PTC subtypes. When blindly investigating the nuclear architecture of thyroid neoplasms, we observed that NIFTP has significantly longer telomeres than CPTC and FVPTC. Super‐resolved 3D‐structured illumination microscopy demonstrated that NIFTP is heterogeneous and that its nuclei contain more densely packed DNA and smaller interchromatin spaces than CPTC and FVPTC, a pattern that resembles normal thyroid tissue. These data are consistent with the observed indolent biological behavior and favorable prognosis associated with NIFTP, which lacks BRAFV600E mutations. Of note, next‐generation thyroid oncopanel sequencing was unable to distinguish the thyroid cancer histotypes in our study cohort. In summary, our data suggest that 3D nuclear architecture can be a powerful analytical tool to diagnose and guide clinical management of NIFTP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".