Innovative Tumor Tissue Dissection Tool for Molecular Oncology Diagnostics
Bibliographic record
Abstract
Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly used material for tumor molecular profiling, therapy selection, and prognostication. Tumor tissue enrichment by tissue dissection is highly recommended to generate quality data reproducibly for use in downstream assays, such as real-time PCR and next-generation sequencing. The aim of this study was to evaluate the performance of the automated tissue dissection tool AVENIO Millisect System compared with a manual dissection method using 18 FFPE tissue specimens. The study assessed performance of these two methods with paraffinized and deparaffinized sections at 5- and 10-μm thickness as well as at low (5% to 10%) and high (>50%) tumor content. In addition, compatibility with various nucleic acid and protein extraction methods was assessed. Overall, dissection by Millisect resulted in statistically significantly higher yields of nucleic acids and protein compared with manual dissection (P = 0.00524). In downstream analysis on a statistically nonpowered sample set, EGFR mutation testing by PCR led to highly concordant results, and next-generation sequencing testing yielded significantly higher allelic frequencies when tissue was dissected by Millisect compared with manual scraping, demonstrating noninferiority of the automated method. In summary, the AVENIO Millisect System may replace manual labor and support automation of FFPE tumor tissue workflows in clinical molecular laboratories with high testing volumes with adequate validation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".