Pre-analytic steps for molecular testing on thyroid fine-needle aspirations: The goal of good results
Bibliographic record
Abstract
Fine-needle aspiration cytology (FNAC) represents a valid alternative to biopsy in a variety of clinical settings mainly based on its simplicity and less invasive clinical approach. In some cases, morphology evaluation alone is not sufficient to manage the patients, so that the application of ancillary techniques can contribute to diagnosis, prognosis and prediction of tumor behavior. These techniques include polymerase chain reaction (PCR), fluorescence in situ hybridization (FISH), in situ PCR, direct Sequencing, microarrays and proteomic methodologies. Although several recent experiences underline the superior value of deoxyribonucleic acid (DNA) quality mainly for advanced genomic high throughput platforms, very scant literature studied the role of the pre-analytical or analytical phases. Despite the high specificity of molecular techniques as a support for diagnosis, there is a need for an increased standardization of pre-analytical/analytical steps such as providing appropriate clinical history, proper collection of laboratory specimens and proper preparation of samples, adequate fixative/reagent concentrations and technical equipments. All these requirements are crucial according to the results from 42 American laboratories, which reported 0.33% of significant molecular errors with 60% of them in the pre-analytical phase. The most common error is to forget that cytological preparation requires specific molecular variables, which are different from histological specimens. Cytological samples offer the advantage of a well preserved DNA, readily extractable and reasonably stable (from 6 months to 5 years) avoiding pitfalls due to formalin-fixation. Freshly prepared, unstained direct, alcohol-fixed papanicolaou, air-dried diff-quick smears are all suitable for DNA extraction and preservation. In the specific field of thyroid FNAC, molecular analysis has been supported by the growing evidence that papillary thyroid carcinoma (PTC), the most common thyroid cancer, frequently is a diploid lesion and can display non-overlapping mutations of the v-Raf murine sarcoma viral oncogene homolog B1 (BRAF) in 46% to 70%, cases, ret proto-oncogene (RET) in 3 to 85% and Rat Sarcoma oncogene (RAS) in 0-21% cases. Recently, several cytological papers demonstrated that the combination of morphology and molecular analysis can increase the diagnostic accuracy allowing more precise prediction of malignancy regardless of the diagnostic categories. In conclusion, the correct use of the pre-analytical-analytical steps might lead to optimal results on cytology and empower the prognostic value of molecular techniques as strong indicators of cancer for their high specificity and positive predictive value.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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".