Addition of pathology and biomarker information significantly improves the performance of the Manchester scoring system for <i>BRCA1</i> and <i>BRCA2</i> testing
Bibliographic record
Abstract
BACKGROUND: Selection for genetic testing of BRCA1/BRCA2 is an important area of healthcare. Although testing costs for mutational analysis are falling, costs in North America remain in excess of US$3000 (UK price can be 690 pounds). Guidelines in most countries use a 10-20% threshold of detecting a mutation in BRCA1/2 combined within a family before mutational analysis is considered. A number of computer-based models have been developed. However, use of these models can be time consuming and difficult. The Manchester scoring system was developed in 2003 to simplify the selection process without losing accuracy. METHODS: In order to increase accuracy of prediction, breast pathology of the index case was incorporated into the Manchester scoring system based on 2156 samples from unrelated non-Jewish patients fully tested for BRCA1/2, and the scores were adapted accordingly. Results/ DISCUSSION: Data from breast pathology allowed adjustment of BRCA1 and combined BRCA1/2 scores alone. There was a lack of pathological homogeneity for BRCA2, therefore specific pathological correlates could not be identified. Upward adjustments in BRCA1 mutation prediction scores were made for grade 3 ductal cancers, oestrogen receptor (ER) and triple-negative tumours. Downward adjustments in the score were made for grade 1 tumours, lobular cancer, ductal carcinoma in situ and ER/HER2 positivity. Application of the updated scoring system led to four and nine more mutations in BRCA1 being identified at the 10% and 20% threshold, respectively. Furthermore, 65 and 58 fewer cases met the 10% and 20% threshold, respectively, for testing. Moreover, the adjusted score significantly improved the trade-off between sensitivity and specificity for BRCA1/2 prediction.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".