ArticleBMC cancer2016
A nomogram for predicting pathological complete response in patients with human epidermal growth factor receptor 2 negative breast cancer.
Article in BMC cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Nomograms Used for Predicting Pathological Complete Response in Early Breast Cancer.Current oncology (Toronto, Ont.) · 2023Pooled it
- Evaluating the prognostic significance of HIST1H4C in breast cancer: implications for neoadjuvant therapy.Scientific reports · 2026Article
- Article
- Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy.NPJ breast cancer · 2025Article
- Dynamic ultrasound-based modeling predictive of response to neoadjuvant chemotherapy in patients with early breast cancer.Scientific reports · 2024Article
- Article
- Article
- A comparison of complete pathologic response rates following neoadjuvant chemotherapy among South African breast cancer patients with and without concurrent HIV infection.Breast cancer research and treatment · 2020Article
- Clinical tumor stage is the most important predictor of pathological complete response rate after neoadjuvant chemotherapy in breast cancer patients.Breast cancer research and treatment · 2017Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe response to neoadjuvant chemotherapy has been proven to predict long-term clinical benefits for patients. Our research is to construct a nomogram to predict pathological complete response of human epidermal growth factor receptor 2 negative breast cancer patients.
methodsWe enrolled 815 patients who received neoadjuvant chemotherapy from 2003 to 2015 and divided them into a training set and a validation set. Univariate logistic regression was performed to screen for predictors and construct the nomogram; multivariate logistic regression was performed to identify independent predictors.
resultsAfter performing the univariate logistic regression analysis in the training set, tumor size, hormone receptor status, regimens of neoadjuvant chemotherapy and cycles of neoadjuvant chemotherapy were the final predictors for the construction of the nomogram. The multivariate logistic regression analysis demonstrated that T4 status, hormone receptor status and receiving regimen of paclitaxel and carboplatin were independent predictors of pathological complete response. The area under the receiver operating characteristic curve of the training set and the validation set was 0.779 and 0.701, respectively.
conclusionsWe constructed and validated a nomogram to predict pathological complete response in human epidermal growth factor receptor 2 negative breast cancer patients. We also identified tumor size, hormone receptor status and paclitaxel and carboplatin regimen as independent predictors of pathological complete response.
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