A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study

The nomogram developed in this study could predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-makin, and the subgroup clinical model also had good predictive performance for lung cancer patients with lesion ≤ 3 cm in diameter.

Authors

Institutions

Publication Details

Journal
BMC Pulmonary Medicine
Published
2024-11-27
DOI
https://doi.org/10.1186/s12890-024-03400-3
Citations
4
Primary Topic
Lung Cancer Research Studies
Type
article
Field-Weighted Citation Impact
1.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study

Huashan Lin, Haipeng Huang, Kai Li, Xiang Zhao et al.
4 citations
BMC Pulmonary Medicine
Lung Cancer Research Studies
1.13
article

A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study

Huashan Lin, Haipeng Huang, Kai Li, Xiang Zhao, Miaomaio Lin
article en
4 citations

Abstract

The nomogram developed in this study could predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-makin, and the subgroup clinical model also had good predictive performance for lung cancer patients with lesion ≤ 3 cm in diameter.

BMC Pulmonary MedicineVol. 24(1)
Guangxi Medical University (CN), The People's Hospital of Guangxi Zhuang Autonomous Region (CN), First Affiliated Hospital of GuangXi Medical University (CN)
Reduced inequalities
Openalex Percentile: Top 18%
Lung Cancer Research Studies
1.13
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.