Recommendations for reporting tissue and circulating tumour (ct)DNA next-generation sequencing results in non-small cell lung cancer
Bibliographic record
Abstract
Abstract Non-small cell lung cancer is a heterogeneous disease and molecular characterisation plays an important role in its clinical management. Next-generation sequencing-based panel testing enables many molecular alterations to be interrogated simultaneously, allowing for comprehensive identification of actionable oncogenic drivers (and co-mutations) and appropriate matching of patients with targeted therapies. Despite consensus in international guidelines on the importance of broad molecular profiling, adoption of next-generation sequencing varies globally. One of the barriers to its successful implementation is a lack of accepted standards and guidelines specifically for the reporting and clinical annotation of next-generation sequencing results. Based on roundtable discussions between pathologists and oncologists, we provide best practice recommendations for the reporting of next-generation sequencing results in non-small cell lung cancer to facilitate its use and enable easy interpretation for physicians. These are intended to complement existing guidelines related to the use of next-generation sequencing (solid and liquid). Here, we discuss next-generation sequencing workflows, the structure of next-generation sequencing reports, and our recommendations for best practice thereof. The aim of these recommendations and considerations is ultimately to ensure that reports are fully interpretable, and that the most appropriate treatment options are selected based on robust molecular profiles in well-defined reports.
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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 | 0.149 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.016 |
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".