ArticleBreast care (Basel, Switzerland)2022
Multivariable Models Based on Baseline Imaging Features and Clinicopathological Characteristics to Predict Breast Pathologic Response after Neoadjuvant Chemotherapy in Patients with Breast Cancer.
Article in Breast care (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Ultrasound for predicting pathological response to neoadjuvant chemotherapy in patients with breast cancer: a systematic review and meta-analysis.BMC cancer · 2026Pooled it
- Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer.BMC cancer · 2026Article
- Development and multicenter validation of an immunoinflammatory marker-MRI model for pathological complete response after neoadjuvant chemotherapy in invasive breast cancer.Translational cancer research · 2026Article
- Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics.Frontiers in medicine · 2025Article
- Personalized prediction of pathological complete response in breast cancer neoadjuvant therapy: a nomogram combining quantitative MRI biomarkers and molecular subtypes.Frontiers in oncology · 2025Article
- Dynamic ultrasound-based modeling predictive of response to neoadjuvant chemotherapy in patients with early breast cancer.Scientific reports · 2024Article
- Efficacy, cardiotoxicity and factors affecting pathologic complete response of neoadjuvant chemotherapy with anthracycline-containing verses anthracycline-free regimens plus dual HER2 blockade for HER2-positive early-stage breast cancer: a retrospective study.Translational cancer research · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Currently, the accurate evaluation and prediction of response to neoadjuvant chemotherapy (NAC) remains a great challenge. We developed several multivariate models based on baseline imaging features and clinicopathological characteristics to predict the breast pathologic complete response (pCR). Methods: We retrospectively collected clinicopathological and imaging data of patients who received NAC and subsequent surgery for breast cancer at our hospital from June 2014 till September 2020. We used mammography, ultrasound, and magnetic resonance imaging (MRI) to investigate the breast tumors at baseline. Results: A total of 308 patients were included and 111 patients achieved pCR. The HER-2 status and Ki-67 index were significant factors for pCR on univariate analysis and in all multivariate models. Among the prediction models in this study, the ultrasound plus MRI model performed best, producing an area under curve of 0.801 (95% CI 0.749-0.852), a sensitivity of 0.797, and a specificity of 0.676. Conclusion: Among the multivariable models constructed in this study, the ultrasound plus MRI model performed best in predicting the probability of pCR after NAC. Further validation is required before it is generalized.
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