Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features

The clinical-radiomic model has the potential to predict axillary lymph node metastasis in breast cancer.

Authors

Institutions

Publication Details

Journal
Frontiers in Oncology
Published
2024-11-19
DOI
https://doi.org/10.3389/fonc.2024.1415584
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.40

Funders

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

Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features

Qiang Chen, Qing Lin, Luting Zhang, Rong Xu et al.
1 citations
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
0.40
article

Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features

Qiang Chen, Qing Lin, Luting Zhang, Rong Xu, Jinxian Yan, Haibin Sun, Quchang Ouyang
article en
1 citations

Abstract

The clinical-radiomic model has the potential to predict axillary lymph node metastasis in breast cancer.

Frontiers in OncologyVol. 14
Fujian University of Traditional Chinese Medicine (CN), Education Department of Fujian Province (CN)
National Natural Science Foundation of China, Natural Science Foundation of Fujian Province
Good health and well-being
Openalex Percentile: Top 33%
Radiomics and Machine Learning in Medical Imaging
0.40
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.