ArticleBMC women's health2024
Development of predictive models for pathological response status in breast cancer after neoadjuvant therapy based on peripheral blood inflammatory indexes.
Article in BMC women's health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic value of the HALP score in breast cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Development and internal validation of a nomogram for predicting neoadjuvant therapy efficacy in breast cancer based on the HALP score.Translational cancer research · 2026Article
- Article
- Development and validation of nomograms predicting survival in operable breast cancer patients at reproductive age after breast conserving surgery and postoperative radiotherapy based on SEER database.Translational cancer research · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAchieving a pathological complete response (pCR) after neoadjuvant therapy (NAT) is considered to be a critical factor for a favourable prognosis in breast cancer. However, discordant pathological complete response (DpCR), characterised by isolated responses in the breast or axillary, represents an intermediate pathological response category between no response and complete response. This study aims to investigate predictive factors and develop models based on peripheral blood inflammatory indexes to more accurately predict NAT outcomes.
methodA total of 789 eligible patients were enrolled in this retrospective study. The patients were randomized into training and validation cohort according to a 7:3 ratio. Lasso and uni/multivariable logistic regression analysis were applied to identify the predictor variables. Two Nomograms combining clinico-pathologic features and peripheral blood inflammatory indexes were developed.
resultMolecular Subtype, HALP, P53, and FAR were used to construct the predictive models for traditional non pCR (T-NpCR) and total-pCR (TpCR). The T-NpCR group was divided into DpCR and non pCR (NpCR) subgroups to construct a new model to more accurately predict NAT outcomes. cN, HALP, FAR, Molecular Subtype, and RMC were used to construct the predictive models for NpCR and DpCR. The receiver operating characteristic (ROC) curves indicate that the model exhibits robust predictive capacity. Clinical Impact Curves (CIC) and Decision Curve Analysis (DCA) indicate that the models present a superior clinical utility.
conclusionHALP and FAR were identified as peripheral blood inflammatory index predictors for accurately predicting NAT outcomes.
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