The impact of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) on the diagnosis of thyroid nodules
Bibliographic record
Abstract
." Almost 70 years later, the recent evidence-based reclassification of the non-invasive encapsulated follicular variant of papillary thyroid carcinoma (FVPTC) as non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), has demonstrated that this problematic is still ongoing. For about 30 years, it was wrongly assumed that the encapsulated FVPTC behaved and spread like its classical counterpart and, therefore, that it should be treated likewise. As such, the renaming of a malignant entity as a nonmalignant (albeit not benign) neoplasm has contributed to optimizing patient care by deescalating treatment and follow-up for an indolent neoplasm, decreasing medical expense and complications possibly caused by further treatment including radioactive iodine, and reassuring patients with this diagnosis. At the same time, NIFTP has significant implications not only for the practice of thyroid cytopathology but also for surgical pathology and for molecular tests, creating significant new challenges. NIFTP has rigorous histopathologic diagnostic criteria, including papillary-like nuclear features, and submission of the entire tumor capsule and content is required to exclude both invasion and presence of papillary structures. Cytologically, because of the morphological overlap with other follicular neoplasms and with papillary thyroid carcinoma (PTC), most NIFTP are diagnosed into one of the indeterminate Bethesda categories (III-V) and can be adequately triaged for surgery. From a molecular view, NIFTP are most often characterized by RAS-type mutations, similar to other follicular-patterned lesions, and molecular testing can be helpful to suggest NIFTP preoperatively. In this review, we focus on the impact of NIFTP on the diagnosis of thyroid nodules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".