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Abstract P2-09-01: Preliminary Findings from the Breast Cancer Combined Visualization And Characterization Tools (bCOMBAT): Low-Dose PEM and Liquid Biopsy Study

2025· article· en· W4411291698 on OpenAlexaboutno aff
Vivianne Freitas, Oleksandr Bubon, Samira Taeb, Frederick Au, Supriya Kulkarni, Sandeep Ghai, Shayna Parker, Michael Waterston, Alla Reznik

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerCancerBiopsyVisualizationLiquid biopsyRadiologyPathologyInternal medicineComputer scienceData mining

Abstract

fetched live from OpenAlex

Abstract Purpose: This study evaluates the effectiveness of low-dose Positron Emission Mammography (PEM) and liquid biopsy in detecting and characterizing breast abnormalities in high-risk patients. Materials and Methods: Approved by the research ethics board, this prospective study involves high-risk women from the Ontario Breast Screening Program (OBSP) at the University Health Network (UHN). Participants scheduled for MRI-guided biopsy of suspicious lesions detected by standard MRI are subjected to low-dose PEM using 74 MBq of fluorine 18-labeled fluorodeoxyglucose (18F-FDG) and liquid biopsy for plasma analysis. Preliminary Findings: Among the first high-risk 24 patients analyzed (median age 41; range 33–66) from a planned cohort of 100, four had biopsy-confirmed cancer, including two cases of ductal carcinoma in-situ (DCIS) and two of invasive cancer. Of these, low-dose PEM imaging identified three malignancies and missed one DCIS, with no false positives reported. Liquid biopsy, measuring tumor content and performing fragmentomic analysis on cell-free DNA, did not detect any cancers. Further analysis using cell-free methylated DNA immunoprecipitation and high throughput sequencing (cfMeDIP) is pending. Conclusion: Early results indicate that low-dose PEM is more sensitive than liquid biopsy for detecting breast cancer in high-risk populations and maintains high specificity relative to MRI. Citation Format: Vivianne Freitas, Oleksandr Bubon, Samira Taeb, Frederick Au, Supriya Kulkarni, Sandeep Ghai, Shayna Parker, Michael Waterston, Alla Reznik. Preliminary Findings from the Breast Cancer Combined Visualization And Characterization Tools (bCOMBAT): Low-Dose PEM and Liquid Biopsy Study [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P2-09-01.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.138
GPT teacher head0.518
Teacher spread0.380 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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