Preoperative prediction of non-invasive follicular thyroid neoplasm with papillary-like nuclear features: A Canadian single-Centre experience
Bibliographic record
Abstract
BACKGROUND: An international group of experts recommended reclassifying non-invasive follicular variant of papillary thyroid cancers (FVPTC) as 'non-invasive follicular thyroid neoplasm with papillary-like nuclear features' (NIFTP) in April 2016. The purpose of this study was to establish preoperative clinical, laboratory, ultrasonographic, and cytological variables, which can differentiate NIFTP from FVPTC. METHODS: We conducted a retrospective chart review of consecutive patients from a single institution evaluated between January 2012 and December 2017. 203 adult patients underwent lobectomy or total thyroidectomy for a FVPTC during that period. Each patient's medical chart was reviewed and information on pre-operative variables was recorded. An expert pathologist reviewed all surgical specimens and reclassified a subset of FVPTC as NIFTP according to the specific criteria. RESULTS: Overall, 44 patients were included in the NIFTP group and 159 in the non-NIFTP group. Mean age was 50.1 years in the NIFTP group and 50.7 in the non-NIFTP group. Most patients were female (86.4% (38/44) in the NIFTP group vs 79.8% (127/159) in the non-NIFTP group). More patients underwent lobectomy in the NIFTP group (50% (22/44) vs 16.4% (26/159) in the non-NIFTP group, p = < 0.0001). Less patients received radioactive iodine in the NIFTP group (31.8% (14/44) vs 52.2% (83/159) in the non-NIFTP group, p = 0.0177). Preoperative thyroglobulin levels were lower in NIFTP patients (Median 25.55 mcg/L +/- 67.8 vs 76.06 mcg/L +/- 119.8 in Non-NIFTP, p = 0.0104). NIFTP nodules were smaller (Mean size 22.97 mm +/- 12.3 vs 25.88 mm +/- 11.2 for non-NIFTP, p = 0.0448) and more often solid than non-NIFTP (93.2% (41/44) vs 74.8% (119/159) for non-NIFTP, p = 0.0067). 2017 ACR TIRADS nodule category of 1-4 on ultrasound had a negative predictive value and a sensitivity of 100% for NIFTP. ROC Curve Analysis demonstrated that a preoperative thyroglobulin level of 31.3 mcg/L had a sensitivity of 75% and a specificity of 62.5% to differentiate NIFTP from non-NIFTP cancers. CONCLUSION: Lower preoperative thyroglobulin levels, smaller nodule size, solid texture and 2017 ACR TIRADS Category of 1-4 are more strongly associated with NIFTP than FVPTC and can favour less invasive surgical options such as lobectomy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".