Pan-immune-inflammation value and prognostic nutritional index can predict the 2-year recurrence of triple-negative breast cancer patients after radical resection

PIV and PNI can serve as effective indicators for predicting recurrence within 2 years after radical surgery in TNBC patients, providing a more scientific reference for clinicians.

Authors

Institutions

Publication Details

Journal
Frontiers in Oncology
Published
2026-02-03
DOI
https://doi.org/10.3389/fonc.2026.1739129
Citations
1
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
Field-Weighted Citation Impact
24.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pan-immune-inflammation value and prognostic nutritional index can predict the 2-year recurrence of triple-negative breast cancer patients after radical resection

Liguo Yang, Zhigang Yang, Yang Dai, Zhenhua Xia et al.
1 citations
Frontiers in Oncology
Inflammatory Biomarkers in Disease Prognosis
24.20
article

Pan-immune-inflammation value and prognostic nutritional index can predict the 2-year recurrence of triple-negative breast cancer patients after radical resection

Liguo Yang, Zhigang Yang, Yang Dai, Zhenhua Xia, Jun Zhang
article en
1 citations

Abstract

PIV and PNI can serve as effective indicators for predicting recurrence within 2 years after radical surgery in TNBC patients, providing a more scientific reference for clinicians.

Frontiers in OncologyVol. 16
YangPu Geriatric Hospital (CN), Yangpu Hospital of Tongji University (CN)
Zero hunger
Openalex Percentile: Top 1%
Inflammatory Biomarkers in Disease Prognosis
24.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.