ArticleJournal of personalized medicine2023
Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An interpretable machine learning model for predicting brain metastasis in breast cancer.Frontiers in medicine · 2026Article
- Machine learning-based prediction of disease-free survival in breast cancer patients with non-pathological complete response after neoadjuvant chemotherapy: a retrospective multicenter cohort study.American journal of cancer research · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
purposeWhile a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients.
methodsA retrospective analysis of 607 non-pCR BC patients was conducted (2012-2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, ac-curacy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan-Meier method.
resultsBoth pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all
conclusionTwo robust and effective nomo-grams were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC.
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