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Can MRI accurately identify which patients with operable breast cancer will have a pathologic complete response after neoadjuvant therapy?

2012· article· en· W2599497396 on OpenAlexaff
Carolyn Nessim, Isabelle Trop, André Robidoux, Eleftherios P. Mamounas, Jean-François Boileau

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsHôtel-Dieu de MontréalCentre Hospitalier de l’Université de MontréalUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineBreast cancerNeoadjuvant therapyChemotherapyBreast MRIOncologyComplete responseCancerInternal medicineRadiologyStage (stratigraphy)Magnetic resonance imagingMammography

Abstract

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616 Background: With the introduction of targeted therapy based on tumor subtypes, an increasing number of patients that receive neoadjuvant chemotherapy achieve a pathologic complete response (pCR). Previous studies have shown that the accuracy of MRI is poor at predicting the response to neoadjuvant chemotherapy in locally advanced and often non-resectable breast cancers, where the rate of pCR is low. The purpose of this study is to evaluate MRI’s ability to predict a pCR in operable breast cancers after neoadjuvant therapy. Methods: All patients enrolled in the NSABP B-40, B-41, FB-5 and FB-6 protocols in a single tertiary care centre, that had an MRI done before and after neoadjuvant therapy were reviewed. A radiologist, blinded to the pathology results, interpreted the pre- and post- treatment MRI’s and made a prediction as to whether or not patients would have a pCR. In this study, a true negative was defined as a reading of a complete response on MRI that was confirmed as a pCR on final pathology. pCR was defined as having no residual invasive or in situ disease in the breast. Results: 129 women with a median age of 51 years were identified. 90% had invasive ductal carcinoma; 8% had invasive lobular. 58% were ER+, 21% were triple negative and 21% were Her2+. 16% of patients had a pCR. 25% of patients had no residual invasive cancer in the breast. pCR rates for ER+ tumors was 5%, triple negative 37%, and Her2+ 26%. 19% of patients that had a pCR had a total mastectomy. The sensitivity and specificity of MRI for predicting residual disease were 88% and 52% respectively. The positive predictive value was 90% and the negative predictive value was 46% with an accuracy of 82%. Conclusions: MRI has limited value for determining which patients had a pCR after neoadjuvant chemotherapy, even in operable breast cancers. When residual disease is suspected on MRI, it is unlikely that a pCR has been achieved. Surgical excision following neoadjuvant therapy remains the gold standard to identify which patients have achieved a pCR. Other modalities will need to be used in order to accurately determine which patients would be eligible for studies evaluating non operative management following neoadjuvant therapy.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.163
GPT teacher head0.478
Teacher spread0.315 · 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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Citations1
Published2012
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