ArticleFrontiers in oncology2024
Pre-operative MRI in evaluating pathologic complete response to neoadjuvant chemotherapy in patients with breast cancer: a study focused on influencing factors of baseline clinical-pathological and imaging features.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Prospective comparison of [European journal of nuclear medicine and molecular imaging · 2026Trial
- Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.BMC medical imaging · 2026Article
- Multimodal radiomics for early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: integration of amide proton transfer weighted imaging in radiomics.BMC medical imaging · 2026Article
- Research on Omitting Sentinel Lymph Node Biopsy after Neoadjuvant Therapy in cN1 Breast Cancer Patients.Cancer medicine · 2026Article
- Pre-Treatment Breast MRI Features and ADC Values as Predictors of Pathologic Complete Response in Breast Cancer: A Molecular Subtype-Based Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Personalized prediction of pathological complete response in breast cancer neoadjuvant therapy: a nomogram combining quantitative MRI biomarkers and molecular subtypes.Frontiers in oncology · 2025Article
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Abstract
Purpose: To investigate what pre-treatment clinical-pathological features and MRI characteristics influence the performance of breast MRI in assessing the pathologic complete response (pCR) of breast cancer patients to Neoadjuvant Chemotherapy (NAC). Methods: A total of 225 patients with pathologically-confirmed breast cancer who underwent pre- and post-NAC breast MRI between January 2020 and April 2023 were retrospectively analyzed. All patients were categorized into radiologic complete response (rCR) and non-rCR groups based on pre-operative MRI. Univariable and multivariable logistic regression were used to identify independent clinicopathological and imaging features associated with imaging-pathological discordance. The performance of pre-operative MRI for predicting pCR to NAC was assessed according to the baseline characteristics of the clinicopathological data and pre-NAC MRI. In addition, the discrepancy between the pre-operative MRI and post-operative pathological findings was further analyzed by a case-control approach. Results: Among 225 patients, 99 (44.0%) achieved pCR after NAC. MRI showed the overall sensitivity of 97.6%, specificity of 58.6%, accuracy of 80.4%, a positive predictive value (PPV) of 75.0%, and a negative predictive value (NPV) of 95.1% in identifying pCR. Of baseline features, presence of ductal carcinoma Conclusion: Presence of DCIS, luminal B or HER2-enriched subtype, multicentric or multifocal lesions, segmental or regional distribution of NME and rim enhancement of mass may lead to a decrease in diagnostic accuracy of MRI in patients of breast cancer treated with NAC.
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