Micro-costing Diagnostics in Oncology: From Single-Gene Testing to Whole Genome Sequencing
Bibliographic record
Abstract
Abstract Purpose Predictive diagnostics play an increasingly important role in personalized medicine for cancer treatment. Whole genome sequencing (WGS) based treatment selection is expected to rapidly increase worldwide. Detailed and comparative cost analyses of diagnostic techniques are an essential element in decision-making. This study aimed to calculate and compare the total cost of currently used diagnostic techniques and of WGS in treatment of non-small cell lung carcinoma (NSCLC), melanoma, colorectal cancer (CRC) and gastrointestinal stromal tumor (GIST) in the Netherlands. Methods The activity-based costing (ABC) method was conducted to calculate the total cost of included diagnostic techniques based on data provided by Dutch pathology laboratories and the Dutch centralized cancer WGS facility. Costs were allocated to four categories: capital costs, maintenance costs, software costs and operational costs. Outcome measures were total cost per cancer patient per included technique, and the total cost per cancer patient per most commonly applied technique (combination) for each cancer type. Results The total cost per cancer patient per technique varied from € 58 (Sanger sequencing, 3 amplicons) to € 4738 (paired tumor-normal WGS). The operational costs accounted for the vast majority over 90 % of the total per cancer patient technique costs. The most important operational cost drivers were consumables followed by personnel (for sample preparation and primary data analysis). Conclusion This study outlined in detail all costing aspects and cost prices of current and new diagnostic modalities used in treatment of NSCLC, melanoma, CRC and GIST in the Netherlands. Detailed cost differences and value comparisons between these diagnostic techniques enable future economic evaluations to support decision-making on implementation of WGS and other diagnostic modalities in routine clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".