Diagnostic value and lymph node metastasis prediction of a custom‑made panel (thyroline) in thyroid cancer
Bibliographic record
Abstract
Differentiation of benign and malignant thyroid nodules is crucial for clinical management. Here, we explored the efficacy of next‑generation sequencing (NGS) in predicting the classification of benign and malignant thyroid nodules and lymph node metastasis status, and simultaneously compared the results with ultrasound (US). Thyroline was designed to detect 15 target gene mutations and 2 fusions in 98 formalin‑fixed, paraffin‑embedded (FFPE) tissues, including those from 82 thyroid cancer (TC) patients and 16 patients with benign nodules. BRAF mutations were found in 57.69% of the papillary thyroid cancer (PTC) cases, while RET mutations were detected among all the medullary thyroid cancer (MTC) cases. Multiple mutations were positive but none showed dominance in anaplastic thyroid cancer (ATC) and follicular thyroid cancer (FTC). The sensitivity and specificity of NGS prediction in differentiation of benign and malignant thyroid nodules were 79.27 and 93.75%, respectively, and the positive predictive value (PPV) and negative predictive value (NPV) were 98.48 and 46.88%, respectively. The sens-itivity and specificity of US were 76.83 and 6.25%, respectively, and the PPV and NPV were 80.77 and 5.00%, respectively. The area under curve (AUC) of NGS and US were 0.865 and 0.415, respectively. A total of 27 patients had ≥1 metastases to lymph nodes, 19 of which carried mutations, including BRAF, RET, NRAS, PIK3CA, TP53, CTNNB1 and PTEN. However, there was no correlation between the variant allele frequency of specific gene mutations and the number of metastatic lymph nodes. In conclusion, the prediction value of NGS was higher than the US‑based Thyroid Imaging Reporting and Data System (TI‑RADS). NGS is valuable for the accurate differentiation of benign and malignant thyroid nodules, and pathological subtypes in FFPE samples. The findings of the present study may pave the way for the application of NGS in analyzing fine‑needle aspiration (FNA) biopsy samples.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".