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Abstract P3-03-20: The association of preoperative MRI with surgical decision-making in patients with early-stage breast cancer: A multi-institutional analysis

2022· article· en· W4220923359 on OpenAlexaff
Peter Borowsky, Seraphina Choi, Orly Morgan, Amy K White, Claudya Morin, Jose Net, Susan B. Kesmodel, Neha Goel, Yamini Patel, Alexa Griffiths, Joshua A. Feinberg, Aaron Kangas‐Dick, Charusheela Andaz, Christina Giuliano, Natalie Zelenko, Donna‐Marie Manasseh, Patrick I. Borgen, Kristin E. Rojas

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineLumpectomyBreast cancerMastectomyBreast MRIStage (stratigraphy)CohortCancerRetrospective cohort studyBreast surgerySurgeryInternal medicineMammography

Abstract

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Abstract Background: Lumpectomy with radiation (breast conservation) and mastectomy have equivalent overall survival. However, recent studies suggest that patients undergoing breast conservation have lower rates of recurrence compared to those undergoing mastectomy. A 2013 meta-analysis by Houssami, et al. found that the rate of mastectomy in patients who had preoperative MRI was 16%, twice as high as that in patients who did not have preoperative MRI. A multi-institutional analysis was performed to investigate the possible impact of modern MRI on the surgical management of early-stage breast cancer (ESBC). Methods: A retrospective comparative cohort study included patients with in-situ and invasive breast cancer eligible for breast conservation surgery from two institutions (NY and FL). Patients who received preoperative systemic therapy were excluded. Eligibility for breast conservation was defined as clinical stage Tis-2. Risk factors for the bilateral or multicentric disease were compared between the two groups including breast density, menopausal status, and concerning family history. The rate of ipsilateral mastectomy in lumpectomy-eligible women was compared between patients who did and did not receive preoperative MRI. Chi-square analysis was used to compare rates between groups. P values <0.05 were considered statistically significant. Results: 505 patients diagnosed between 1/2016-4/2019 (NY) and 2/2020-12/2020 (FL) underwent primary surgery for ESBC. 434 did not receive neoadjuvant therapy and were included in the analysis. 292 (67.3%) had preoperative MRI. There was no difference in the proportion of patients who were premenopausal or who met the criteria for genetic testing by family history. The largest dimension on preoperative imaging was similar between the two groups. Patients who had MRI were younger (median age 58 vs. 68, p<0.001) and more likely to have group 3 or 4 breast density (64.5% vs. 27.1%, p<0.001). Patients who underwent preoperative MRI were twice as likely to undergo mastectomy as their first surgery (32.6% vs. 15.3%, p<0.001). The rate of re-excision was similar between the two groups (MRI 13.0% vs. no MRI 10.8% p=0.511). Of note, the final pathologic size of the invasive or in-situ component was similar between the two groups (Table 1). Conclusion: Younger age and greater breast density are associated with preoperative MRI receipt and all three factors likely play a role in choosing mastectomy. Young women with dense breasts represent a unique cohort of patients that may be particularly susceptible to cancer-related worry and anxiety related to additional biopsies, and therefore may be more likely to opt against continued breast imaging. Since approximately 70% of patients with ESBC undergo preoperative MRI, future work should focus on mitigating these challenges to improve shared decision-making. Table 1.Comparison of ESBC Patients Who Did and Did Not Receive MRIMRI (n=292) %, median (IQR)No MRI (n=142)%, median (IQR)p-valueAge58 years (50-65)68 years (60-76)<0.001bPremenopausal27%21%0.108Dense Breasts65%27%<0.001bMeet Criteria for Genetic Testing40%38%0.207Imaging Size13 mm (9-21)12 mm (8-20)0.315Mastectomy as First Surgery33%15%<0.001bPlan for Repeat Surgerya20%24%0.402Re-excision13%11%0.511Pathologic Size (Invasive)13 mm (8-20)13 mm (9-22)0.482Pathologic Size (DCIS)10 mm (5-20)8 mm (3-15)0.093aRepeat surgery includes re-excision, completion mastectomy, and axillary dissectionbDenotes significant p-values. Citation Format: Peter A Borowsky, Seraphina Choi, Orly Morgan, Amy K White, Claudya Morin, Jose Net, Susan Kesmodel, Neha Goel, Yamini Patel, Alexa Griffiths, Joshua A Feinberg, Aaron Kangas-Dick, Charusheela Andaz, Christina Giuliano, Natalie Zelenko, Donna-Marie Manasseh, Patrick Borgen, Kristin E Rojas. The association of preoperative MRI with surgical decision-making in patients with early-stage breast cancer: A multi-institutional analysis [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 P3-03-20.

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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)0.004
metaresearch head score (Gemma)0.009
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 score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.341
Teacher spread0.324 · 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
Published2022
Admission routes1
Has abstractyes

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