Automated Analysis of 1p/19q Status by FISH in Oligodendroglial Tumors: Rationale and Proposal of an Algorithm
Bibliographic record
Abstract
OBJECTIVE: To propose a new algorithm facilitating automated analysis of 1p and 19q status by FISH technique in oligodendroglial tumors with software packages available in the majority of institutions using this technique. METHODS: We documented all green/red (G/R) probe signal combinations in a retrospective series of 53 oligodendroglial tumors according to literature guidelines (Algorithm 1) and selected only the most significant combinations for a new algorithm (Algorithm 2). This second algorithm was then validated on a prospective internal series of 45 oligodendroglial tumors and on an external series of 36 gliomas. RESULTS: Algorithm 2 utilizes 24 G/R combinations which represent less than 40% of combinations observed with Algorithm 1. The new algorithm excludes some common G/R combinations (1/1, 3/2) and redefines the place of others (defining 1/2 as compatible with normal and 3/3, 4/4 and 5/5 as compatible with imbalanced chromosomal status). The new algorithm uses the combination + ratio method of signal probe analysis to give the best concordance between manual and automated analysis on samples of 100 tumor cells (91% concordance for 1p and 89% concordance for 19q) and full concordance on samples of 200 tumor cells. This highlights the value of automated analysis as a means to identify cases in which a larger number of tumor cells should be studied by manual analysis. Validation of this algorithm on a second series from another institution showed a satisfactory concordance (89%, κ = 0.8). CONCLUSION: Our algorithm can be easily implemented on all existing FISH analysis software platforms and should facilitate multicentric evaluation and standardization of 1p/19q assessment in gliomas with reduction of the professional and technical time required.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".