MRI Performance in Detecting pCR After Neoadjuvant Chemotherapy by Molecular Subtype of Breast Cancer
Bibliographic record
Abstract
BACKGROUND: MRI performance in detecting pathologic complete response (pCR) post-neoadjuvant chemotherapy (NAC) in breast cancer has been previously explored. However, since tumor response varies by molecular subtype, it is plausible that imaging performance also varies. Therefore, we performed a literature review on subtype-specific MRI performance in detecting pCR post-NAC. METHODS: Two reviewers searched Cochrane, PubMed, and EMBASE for articles published between 2013 and 2018 that examined MRI performance in detecting pCR post-NAC. After filtering, ten primary research articles were included. Statistical metrics, such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were extracted per study for triple negative, HR+/HER2-, and HER2+ patients. RESULTS: Ten studies involving 2310 patients were included. In triple negative breast cancer, MRI showed NPV (58-100%) and PPV (72.7-94.7%) across 446 patients and sensitivity (45.5-100%) and specificity (49-94.4%) in 375 patients. In HR+/HER2- breast cancer patients, MRI showed NPV (29.4-100%) and PPV (21.4-95.1%) across 851 patients and sensitivity (43-100%) and specificity (45-93%) across 780 patients. In HER2+-enriched subtype, MRI showed NPV (62-94.6%) and PPV (34.9-72%) in 243 patients and sensitivity (36.2-83%) and specificity (47-90%) in 255 patients. CONCLUSION: MRI accuracy in detecting pCR post-NAC by subtype is not as consistent, nor as high, as individual studies suggest. Larger studies using standardized pCR definition with appropriate timing of surgery and MRI need to be conducted. This study has shown that MRI is in fact not an accurate prediction of pCR, and thus, clinicians may need to rely on other approaches such as biopsies of the tumor bed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".