Reducing cytogenetic testing in the era of next generation sequencing: Are we choosing wisely?
Bibliographic record
Abstract
INTRODUCTION: In most laboratories, next generation sequencing (NGS) has been added without consideration for redundancy compared to conventional cytogenetics (CG). We tested a streamlined approach to genomic testing in patients with suspected myeloid and plasma cell neoplasms using next generation sequencing ("NGS first") as the primary testing modality and limiting cytogenetics (CG) to samples with morphologic abnormalities in the marrow aspirate. METHODS: Based on morphologic interpretation of bone marrow aspirate and flow cytometry, samples were triaged into four groups: (a) Samples with dysplasia or excess blasts had both NGS and karyotyping; (b) Samples without excess blasts or dysplasia had NGS only; (c) Repeat samples with previous NGS and/or CG studies were not retested; (d) Samples for suspected myeloma with less than 5% plasma cell had CG testing cancelled. RESULTS: Seven hundred eleven adult bone marrow (BM) samples met the study criteria. The NGS first algorithm eliminated CG testing in 229/303 (75.6%) of patients, primarily by reducing repeat testing. Potential cost avoided was approximately $124 000 per annum. Hematologists overruled the triage comment in only 11/303 (3.6%) cases requesting CG testing for a specific indication. CONCLUSIONS: Utilizing NGS as the primary genomic testing modality NGS was feasible and well accepted, reducing over three quarters of all CG requests and improving the financial case for adoption of NGS. Key factors for the success of this study were collaboration of clinical and genomic diagnostic teams in developing the algorithm, rapid turnaround time for BM interpretation for triage, and communication between laboratories.
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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 | 0.031 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".