Observational studyCurrent oncology (Toronto, Ont.)2025
Construction of a Nomogram Model for Predicting Pathologic Complete Response in Breast Cancer Neoadjuvant Chemotherapy Based on the Pan-Immune Inflammation Value.
Observational study in Current oncology (Toronto, Ont.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Article
- Early identification of neoadjuvant therapy non-response via multimodal immune-imaging biomarkers in breast cancer.Frontiers in immunology · 2026Article
- Clinical and pathological indicators for predicting disease-free survival after radical surgery in elderly patients with early-stage triple-negative invasive ductal carcinoma of the breast.Frontiers in oncology · 2026Article
- Association of the inflammatory microenvironment of obesity with pathological complete response in patients with breast cancer receiving neoadjuvant chemotherapy.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe pan-immune inflammation value (PIV) has unclear predictive utility for pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). This study aimed to evaluate the PIV's predictive value and develop a nomogram integrating PIV for individualized pCR prediction.
methodsIn a retrospective multicenter study of 507 NAC-treated patients (training cohort: 357; validation cohort: 150), independent predictors of pCR were identified through univariate and multivariate logistic regression. A nomogram was constructed and validated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) evaluated the improvement in performance after incorporating the PIV indicator.
resultsThe high PIV patients (cutoff: 316.533) had significantly lower pCR rates than the low PIV patients (
conclusionsThe PIV is an independent predictor of pCR, and the PIV-based nomogram provides a reliable tool for optimizing NAC response prediction in breast cancer.
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