Comparing Breast Cancer Multiparameter Tests in the OPTIMA Prelim Trial: No Test Is More Equal Than the Others
Bibliographic record
Abstract
BACKGROUND: Previous reports identifying discordance between multiparameter tests at the individual patient level have been largely attributed to methodological shortcomings of multiple in silico studies. Comparisons between tests, when performed using actual diagnostic assays, have been predicted to demonstrate high degrees of concordance. OPTIMA prelim compared predicted risk stratification and subtype classification of different multiparameter tests performed directly on the same population. METHODS: Three hundred thirteen women with early breast cancer were randomized to standard (chemotherapy and endocrine therapy) or test-directed (chemotherapy if Oncotype DX recurrence score >25) treatment. Risk stratification was also determined with Prosigna (PAM50), MammaPrint, MammaTyper, NexCourse Breast (IHC4-AQUA), and conventional IHC4 (IHC4). Subtype classification was provided by Blueprint, MammaTyper, and Prosigna. RESULTS: Oncotype DX predicted a higher proportion of tumors as low risk (82.1%, 95% confidence interval [CI] = 77.8% to 86.4%) than were predicted low/intermediate risk using Prosigna (65.5%, 95% CI = 60.1% to 70.9%), IHC4 (72.0%, 95% CI = 66.5% to 77.5%), MammaPrint (61.4%, 95% CI = 55.9% to 66.9%), or NexCourse Breast (61.6%, 95% CI = 55.8% to 67.4%). Strikingly, the five tests showed only modest agreement when dichotomizing results between high vs low/intermediate risk. Only 119 (39.4%) tumors were classified uniformly as either low/intermediate risk or high risk, and 183 (60.6%) were assigned to different risk categories by different tests, although 94 (31.1%) showed agreement between four of five tests. All three subtype tests assigned 59.5% to 62.4% of tumors to luminal A subtype, but only 121 (40.1%) were classified as luminal A by all three tests and only 58 (19.2%) were uniformly assigned as nonluminal A. Discordant subtyping was observed in 123 (40.7%) tumors. CONCLUSIONS: Existing evidence on the comparative prognostic information provided by different tests suggests that current multiparameter tests provide broadly equivalent risk information for the population of women with estrogen receptor (ER)-positive breast cancers. However, for the individual patient, tests may provide differing risk categorization and subtype information.
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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".