Analytic and clinical validity of thyroid nodule mutational profiling using droplet digital polymerase chain reaction
Bibliographic record
Abstract
BACKGROUND: Recent guidelines for the management of thyroid nodules incorporate mutation testing as an adjunct for surgical decision-making, however current tests are costly with limited accuracy. Droplet digital PCR (ddPCR) is an ultrasensitive method of nucleic acid detection that is particularly useful for identifying gene mutations. This study aimed to assess the analytic and clinical validity of RAS and BRAF ddPCR mutational testing as a diagnostic tool for thyroid fine needle aspirate biopsy (FNAB). METHODS: Patients with thyroid nodules meeting indication for FNAB were prospectively enrolled from March 2015 to September 2017. In addition to clinical protocol, an additional FNAB was obtained for ddPCR. Optimized ddPCR probes were used to detect mutations including HRASG12 V, HRASQ61K, HRASQ61R, NRASQ61R, NRASQ61K and BRAFV600E. The diagnostic performance of BRAF and RAS mutations was assessed individually or in combination with Bethesda classification against final surgical pathology. RESULTS: A total of 208 patients underwent FNAB and mutational testing with the following Bethesda cytologic classification: 26.9% non-diagnostic, 55.2% benign, 5.3% FLUS/AUS, 2.9% FN/SPN, 2.4% SFM and 7.2% malignant. Adequate RNA was obtained from 91.3% (190) FNABs from which mutations were identified in 21.1% of HRAS, 11.5% of NRAS and 7.4% of BRAF. Malignant cytology or BRAFV600E was 100% specific for malignancy. Combining cytology with ddPCR BRAF600E mutations testing increased the sensitivity of Bethesda classification from 41.7 to 75%. Combined BRAFV600E and Bethesda results had a positive predictive value (PPV) of 100% and negative predictive value (NPV) of 89.7% for thyroid malignancy in our cohort. CONCLUSIONS: DdPCR offers a novel and ultrasensitive method of detecting RAS and BRAF mutations from thyroid FNABs. BRAFV600E mutation testing by ddPCR may serve as a useful adjunct to increase sensitivity and specificity of thyroid FNAB.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".