Can MRI accurately identify which patients with operable breast cancer will have a pathologic complete response after neoadjuvant therapy?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".