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Abstract P3-04-04: Correlation of Mammographic Microcalcifications with Final Surgical Pathology after Neoadjuvant Chemotherapy for Breast Cancer

2023· article· en· W4322771254 on OpenAlexaff
Riordan Azam, Nicole Look Hong, David Lim

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineBreast cancerSurgical pathologyMammographyRadiologyCancerChemotherapyBreast imagingNeoadjuvant therapyOccultDuctal carcinomaBreast MRIOncologyPathologyInternal medicine

Abstract

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Abstract Introduction: Benefits of neoadjuvant chemotherapy (NAC) in breast cancer include de-escalating surgical management, treating occult systemic metastases and the assessment of in-vivo tumor response. Guidelines for post-NAC imaging to assess response lack specificity on appropriateness and utility of individual imaging modalities for surgical planning. Mammographic microcalcifications are a confounder that are not well studied. We examined the correlation between the mammographic extent of microcalcifications present post-NAC, corresponding MRI lesions, and definitive surgical pathology. Methods: In this retrospective cohort study, patients with calcifications on pre-NAC mammograms were collected from a database of consecutive breast cancer patients receiving NAC at an academic center between January 1st 2014 – December 31st 2019. The primary objective was to determine how the maximum diameter of post-NAC calcifications correlates with final surgical pathology, stratified by tumor receptor subtype. The secondary objectives were to assess correlation of the maximum diameter on final pathology with the maximum diameter of (1) post-NAC mammographic mass (if present) and (2) post-NAC MRI, mass (ME) and non-mass enhancement (NME). Parameters on final surgical pathology included the diameters of invasive disease, ductal carcinoma in-situ (DCIS) and the tumor bed (TB). Pearson’s correlation coefficient was used to evaluate statistical significance and considered strong if R2 ≥70%, moderate if R2 = 25-70% and weak if R2 ≤25%. Results: 343 patients received NAC, of which 157 were excluded for lack of calcifications (n=147), lack of imaging reports (n=8) or inflammatory breast cancer (n=2). 186 patients met the inclusion criteria with a mean age of 49.9 years. 34 (18.3%) patients had triple negative breast cancers (TNBCs). Mammographic calcifications correlated poorly with residual invasive disease (R2= 10.8%), overestimating by 57%. TNBCs demonstrated the strongest correlation between microcalcifications and invasive disease (R2=83%), but calcifications overestimated pathology by 41%. Mammographic calcifications correlated moderately with the TB (R2= 50.3%) and poorly with DCIS (R2 = 3.4%). By subtype, both correlations were strong in TNBCs (R2 = 76.3% and 77.9% respectively). Similarly, focal mass on mammography correlated poorly with pathology except in TNBCs, which correlated moderately with all three pathology parameters (Table 1). In patients with calcifications on mammography, MRI ME and NME correlated weakly with invasive disease, except in TNBCs, where correlation was moderate in ME (R2 = 37.7%) and NME (R2 = 28.4%). Conclusion: In breast cancer patients with microcalcifications, current post-NAC imaging modalities appear to overestimate the extent of residual disease. Surgical excision of all residual microcalcifications may maximize oncologic safety but may also represent over-treatment. Ongoing feasibility trials on surgical omission rely on post-NAC imaging to determine trial candidacy. Our data suggests that potentially eligible patients are excluded on the basis of post-NAC imaging overestimating the extent of residual disease. Further studies are needed to determine the most accurate imaging correlate of residual disease after NAC to plan better surgeries, identify candidates for surgical omission and establish optimal post-NAC imaging guidelines. Current imaging modalities appear most accurate for TNBCs and may be more reliable at identifying exceptional responders for surgical omission in this subtype. Table 1. Summary of Correlation Analyses in Overall and Triple Negative Breast Cancer (TNBC) cohorts. * Average % difference in maximal diameter calculated as (pathology – radiology)/radiology Citation Format: Riordan M. Azam, Nicole Look Hong, David Lim. Correlation of Mammographic Microcalcifications with Final Surgical Pathology after Neoadjuvant Chemotherapy for Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-04.

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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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.371
Teacher spread0.330 · 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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Citations0
Published2023
Admission routes1
Has abstractyes

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