Nomogram models for prediction of axillary lymph node metastasis in breast cancer patients

We developed two preoperative nomograms that can be used to predict ALN metastasis (LN - vs. LN+) and the number of metastatic ALNs (≤ 2 vs. >2) in early breast cancer patients. They were well verified in internal testing groups. The nomograms can help clinicians predict the status of ALNs and guide discussions regarding axillary management.

Authors

Institutions

Publication Details

Journal
BMC Cancer
Published
2025-11-26
DOI
https://doi.org/10.1186/s12885-025-15260-z
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nomogram models for prediction of axillary lymph node metastasis in breast cancer patients

Xuetao Cao, Yuechong Li, Songjie Shen, Yixin Luo et al.
BMC Cancer
Breast Cancer Treatment Studies
article

Nomogram models for prediction of axillary lymph node metastasis in breast cancer patients

Xuetao Cao, Yuechong Li, Songjie Shen, Yixin Luo, Zixi Deng
article en

Abstract

We developed two preoperative nomograms that can be used to predict ALN metastasis (LN - vs. LN+) and the number of metastatic ALNs (≤ 2 vs. >2) in early breast cancer patients. They were well verified in internal testing groups. The nomograms can help clinicians predict the status of ALNs and guide discussions regarding axillary management.

BMC CancerVol. 25(1)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking Union Medical College Hospital (CN)
Peking Union Medical College Hospital
Openalex Percentile: Top 55%
Breast Cancer Treatment Studies
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.