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Record W2408119890 · doi:10.4137/bcbcr.s38432

A Retrospective Study Evaluating the Impact of Preoperative Breast MRI on Surgical Decision-Making in Young Patients (≤50 Years) with Invasive Breast Cancer

2016· article· en· W2408119890 on OpenAlexaff
Som D. Mukherjee, Nicole Hodgson, Peter Lovrics, Kavita Dhamanaskar, Terry Minuk, Jonathan Sussman

Bibliographic record

VenueBreast Cancer Basic and Clinical Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsSt. Joseph's HospitalMcMaster University
Fundersnot available
KeywordsMedicineBreast cancerLumpectomyBreast MRIMammographyBreast ultrasoundMagnetic resonance imagingRadiologyMastectomyBreast surgeryPhysical examinationUltrasoundRetrospective cohort studyBreast imagingBreast diseaseCancerSurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Breast magnetic resonance imaging (MRI) is considered a more sensitive diagnostic test for detecting invasive breast cancer than mammography or breast ultrasound. Breast MRI may be particularly useful in younger premenopausal women with higher density breast tissue for differentiating between dense fibroglandular breast tissue and breast malignancies. The main objective of this study was to determine the impact of preoperative breast MRI on surgical decision-making in young women with breast cancer. METHODS: A retrospective review of patients with newly diagnosed invasive breast cancer and age of ≤50 years was performed. All patients underwent physical examination, preoperative mammogram, breast ultrasound, and bilateral breast MRI. Two breast cancer surgeons reviewed the preoperative mammogram report, breast ultrasound report, and physical examination summary and were asked if they would recommend a lumpectomy, a quandrantectomy, or a mastectomy. A few weeks later, the two surgeons were shown the same information with the breast MRI report and were asked what type of surgery they would now recommend. In each case, MRI was classified by two adjudicators as having affected the surgical outcome in a positive, negative, or neutral fashion. A positive impact was defined as the situation where breast MRI detected additional disease that was not found on physical examination, mammogram, or breast ultrasound and led to an appropriate change in surgical management. A negative impact was defined as the situation where breast MRI led the surgeon to recommend more extensive surgery, with less extensive disease actually found at pathology. No impact was defined as the situation where MRI findings did not alter surgical recommendations or outcomes. RESULTS: Of 37 patients whose charts were reviewed, five patients were deemed to be ineligible due to having received neoadjuvant chemotherapy, having previous breast implants, or having had their tumor fully excised during biopsy. In total, 32 patients met the inclusion criteria of this study and were appropriate for analysis. The median age of our study patient population was 42 years. The pathologic diagnosis was invasive ductal carcinoma in 91% (29/32) of patients and invasive lobular carcinoma in 9% (3/32) of patients. For surgeon A, clinical management was altered in 21/32 (66%) patients, and for surgeon B, management was altered in 13/32 (41%) patients. The most common change in surgical decision-making after breast MRI was from breast-conserving surgery to a mastectomy. Mastectomy rates were similar between both surgeons after breast MRI. After reviewing the pathology results and comparing them with the breast MRI results, it was determined that breast MRI led to a positive outcome in 13/32 (41%) patients. Breast MRI led to no change in surgical management in 15/32 (47%) patients and resulted in a negative change in surgical management in 4/32 (13%) patients. Bilateral breast MRI detected a contralateral breast cancer in 2/32 (6%) patients. CONCLUSIONS: Preoperative breast MRI alters surgical management in a significant proportion of younger women diagnosed with breast cancer. Prospective studies are needed to confirm these findings and to help determine if this change in surgical decision-making will result in improved local control.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.087
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.466
Teacher spread0.415 · 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 teacher head, 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

Citations13
Published2016
Admission routes1
Has abstractyes

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