Costs of Next-Generation Sequencing Assays in Non-Small Cell Lung Cancer: A Micro-Costing Study
Bibliographic record
Abstract
Background: Next-generation sequencing (NGS) of tumor genomes has changed and improved cancer treatment over the past few decades. It can inform clinicians on the optimal therapeutic approach in many of the solid and hematologic cancers, including non-small lung cancer (NSCLC). Our study aimed to determine the costs of NGS assays for NSCLC diagnostics. Methods: We performed a micro-costing study of four NGS assays (Trusight Tumor 170 Kit (Illumina), Oncomine Focus (Thermo Fisher), QIAseq Targeted DNA Custom Panel and QIASeq Targeted RNAscan Custom Panel (Qiagen), and KAPA HyperPlus/SeqCap EZ (Roche)) at the StemCore Laboratories, the Ottawa Hospital, Canada. We used a time-and-motion approach to measure personnel time and a pre-defined questionnaire to collect resource utilization. The unit costs were based on market prices. The cost data were reported in 2019 Canadian dollars. Results: Based on a case throughput of 500 cases per year, the per-sample cost for TruSight Tumor 170 Kit, QIASeq Targeted DNA Custom Panel and QIASeq Targeted RNAscan Custom Panel, Oncomine Focus, and HyperPlus/SeqCap EZ were CAD 1778, CAD 599, CAD 1100 and CAD 1270, respectively. The key cost drivers were library preparation (34–60%) and sequencing (31–51%), followed by data analysis (6–13%) and administrative support (2–7%). Conclusions: Trusight Tumor 170 Kit was the most expensive NGS assay for NSCLC diagnostics; however, an economic evaluation is required to identify the most cost-effective NGS assay. Our study results could help inform decisions to select a robust platform for NSCLC diagnostics from fine needle aspirates, and future economic evaluations of the NGS platforms to guide treatment selections for NSCLC patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".