ArticleEuropean journal of breast health2026
Interim Breast MRI for Predicting Pathologic Complete Response After Neoadjuvant Chemotherapy in Early Breast Cancer.
Article in European journal of breast health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is essential for treatment decision-making in breast cancer. The value of integrated prediction models combining interim magnetic resonance imaging (MRI) findings with clinicopathological factors remains uncertain. This study aimed to compare interim and post-NAC MRI in predicting pCR and to assess the performance of prediction models integrating MRI and clinicopathological variables. Materials and Methods: We retrospectively analyzed 249 patients with early-stage breast cancer who underwent MRI before, during (interim), and after NAC. Clinicopathological variables were selected via stepwise regression based on the minimum Akaike information criterion. Four predictive models were developed: Model A used clinicopathological variables alone; Model B added interim MRI; Model C added post-NAC MRI; and Model D incorporated both interim and post-NAC MRI. Model performance was assessed using receiver operating characteristic curve analysis, calibration plots, and decision curve analysis. Results: Sixty-two (25%) patients achieved pCR. Independent predictors in Model A included hormone receptor status, human epidermal growth factor receptor 2 status, and clinical tumor size. The areas under the curves were 0.721 (Model A), 0.819 (Model B), 0.847 (Model C), and 0.848 (Model D). Model B (interim MRI) demonstrated the highest sensitivity (0.99) and negative predictive value (0.97), enabling early identification of pCR, while Models C and D showed only modest improvements. In decision curve analysis, calibration and clinical utility were superior in models incorporating MRI compared with Model A. Conclusion: Interim MRI demonstrated pCR-predictive performance comparable to that of post-NAC MRI, supporting its potential utility as a clinically meaningful tool for early treatment decision-making. However, its relatively high false-positive rate suggests that caution is warranted when applying it to guide surgical de-escalation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.