ArticleFrontiers in oncology2024
The role of systemic immune-inflammation index in predicting pathological complete response of breast cancer after neoadjuvant therapy and the establishment of related predictive model.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review and Meta-Analysis of AI-Assisted Mammography and the Systemic Immune-Inflammation Index in Breast Cancer: Diagnostic and Prognostic Perspectives.Medicina (Kaunas, Lithuania) · 2025Pooled it
- A Novel Biological Index for Predicting Neoadjuvant Treatment Response in HER2-Positive Breast Cancer: The Tumor-Immune-Proliferation-Inflammation (TIPI) Score.Journal of clinical medicine · 2026Article
- Associations between pretreatment composite inflammatory indices and pathological response to neoadjuvant therapy in breast cancer: a retrospective cohort study.Frontiers in oncology · 2026Article
- Inflammatory and Nutritional Markers Predicting Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Breast Cancer: A Multicenter Real-World Study.Journal of clinical medicine · 2025Article
- HALP-H Index as a Prognostic Biomarker for Predicting Pathological Complete Response in Early-Stage HER2-Positive Breast Cancer-A Multicenter Retrospective Cohort Study.Journal of clinical medicine · 2025Article
- Association of the inflammatory microenvironment of obesity with pathological complete response in patients with breast cancer receiving neoadjuvant chemotherapy.Frontiers in endocrinology · 2025Article
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3 authors.
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Abstract
Objective: To investigate the role of systemic immune-inflammation index (SII) in complete pathological response (pCR) of breast cancer patients after neoadjuvant chemotherapy, and to establish and validate a nomogram for predicting pCR. Methods: Breast cancer patients were selected from the First Affiliated Hospital of Xi'an Jiaotong University from January 2020 to December 2023. The optimal cut-off value of SII was calculated via ROC curve. The correlation between SII and clinicopathological characteristics was analyzed by Chi-square test. Logistic regression analysis was performed to evaluate the factors that might affect pCR. Based on the results of Logistic regression analysis, a nomogram for predicting pCR was established and validated. Results: A total of 112 breast cancer patients were included in this study. 33.04% of the patients achieved pCR after neoadjuvant therapy. Chi-square test showed that SII was significantly correlated with pCR (P=0.001). Logistic regression analysis suggested that Ki-67 (P=0.039), therapy cycle (P<0.001), CEA (P=0.025) and SII (P=0.019) were independent predictors of pCR after neoadjuvant chemotherapy. A nomogram based on Ki-67, therapy cycle, CEA and SII showed a good predictive ability. Conclusion: Ki-67, therapy cycle, CEA and SII were independent predictors of pCR of breast cancer after neoadjuvant chemotherapy. The nomogram based on the above positive factors showed a good predictive ability.
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