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Abstract P2-01-02: A whole blood assay to identify breast cancer: Interim analysis of the international identify breast cancer (IDBC) study evidence supporting the Syantra DX breast cancer test

2022· article· en· W4220929390 on OpenAlexaffabout
Nigel Bundred, Kenneth F. Fuh, Nasimeh Asgarian, Shannon Brown, Danielle Simonot, Xiuling Wang, Robert K. Shepherd, May Lynn Quan, Bobbi Jo Docktor, Anthony Maxwell, Cliona Kirwan, Alan B. Hollingsworth, Donald G. Morris, Kristina D. Rinker

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsAlberta Health ServicesUniversity of CalgaryAlberta Cancer Foundation
Fundersnot available
KeywordsBreast cancerMedicineCancerMammographyInternal medicineOncologyBlood testAsymptomaticInterim analysisInterimClinical trial

Abstract

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Abstract Background: Breast cancer is often detected at later stages, indicating a significant need for additional screening methods. Mammography has limitations in breast cancer detection, for example young age, mammographic high density (categories C and D), small tumors and breast cancer classifications such as invasive lobular carcinomas. Syantra DX Breast Cancer is a new whole blood test that detects the presence of breast cancer by evaluating the expression of 12 novel genes through a custom qPCR process with proprietary software that includes machine learning-derived algorithms. Methodology: Whole blood samples (2.5 ml) were collected and analyzed with Syantra DX Breast Cancer as part of the ongoing IDBC prospective international clinical study (NCT04495244). The study is designed to demonstrate test performance in 2,100 participants. Women aged 30 to 75 years with a normal screening mammogram or physical exam (for the controls), or a BI-RADs 3 – 5 score on a screening mammogram were enrolled. A total of 1,107 participants (240 asymptomatic breast cancer, 867 non-cancer) were recruited and evaluated. All blood samples were collected pre-biopsy. For this interim analysis, 383 samples (132 cancer, 251 non-cancer) were used for machine learning-based model development and initial testing using a cross-validation approach. A set of 724 samples, with 695 evaluable samples (blind test set: 96 cancer, 599 non-cancer) were used for independent testing. All samples in the test set were randomized and blinded by the Alberta Cancer Research Biobank. Clinical performance metrics are reported for the blind test set with 99.5% confidence intervals (CI) computed through an exact binomial test. Results: In the blind test set, 59% of breast cancer subjects were Stage 1 and 25% stage 2. For molecular subtype, 75% were hormone receptor positive, 10% were HER2 positive, and 5% were triple negative. For subjects with invasive breast cancer, the average tumor size was 29 mm (CI: 19 – 38 mm). For the entire test set, Syantra DX Breast Cancer demonstrated an inferred accuracy of 92.2% (CI: 88.9% – 94.6%) with a specificity of 94.3% (CI: 91.0% – 96.4%) and sensitivity of 79.2% (CI: 65.5% – 88.4%) for cancer detection (Table 1). Higher performance was observed in the group of study women under 50 with an inferred specificity of 99.0% and a sensitivity of 91.7% (Table 1). Evaluation of performance in women with extremely dense breast tissue (category D; n=52) revealed an inferred specificity of 95.3% (CI: 77.4% – 99.2%) and sensitivity of 88.9% (CI: 42.6% – 98.9%). This analysis also showed that small tumors less than 10 mm (n=19) were detected by the test, with a sensitivity of 68.4%. Conclusions: Interim data from the IDBC study demonstrated the clinical utility of the Syantra DX Breast Cancer test for use in early screening. Syantra DX Breast Cancer is the first blood test to show strong performance for women under 50, as well for those with very high breast density, and therefore provides a promising screening option to supplement current imaging approaches. Table 1. Performance Metrics of the Syantra DX Breast Cancer TestAgeNumber of participants (n)AccuracySpecificitySensitivity< 50Normal: 19298.5% (CI: 93.8% – 99.7%)99.0% (CI: 94.2% – 99.8%)91.7% (CI: 51.1% – 99.1%)Cancer: 12≥ 50Normal: 40789.6% (CI: 85.1% – 92.9%)92.1% (CI: 87.5% – 95.1%)77.4% (CI: 62.5% – 87.5%)Cancer: 84Entire cohortNormal: 59992.2% (CI: 88.9% – 94.6%)94.3% (CI: 91.0% – 96.4%)79.2% (CI: 65.5% – 88.4%)Cancer: 96 Citation Format: Nigel Bundred, Kenneth Fuh, Nasimeh Asgarian, Shannon Brown, Danielle Simonot, Xiuling Wang, Robert Shepherd, May Lynn Quan, Bobbi Jo Docktor, Anthony Maxwell, Cliona Kirwan, Alan Hollingsworth (retired), Donald Morris, Kristina Rinker. A whole blood assay to identify breast cancer: Interim analysis of the international identify breast cancer (IDBC) study evidence supporting the Syantra DX breast cancer test [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-01-02.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.072
GPT teacher head0.448
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2022
Admission routes2
Has abstractyes

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