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A nomogram for predicting lymphovascular invasion in lung adenocarcinoma
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Abstract
Backgroud
Lymphovascular invasion (LVI) is histological factor that is closely related to prognosis of lung adenocarcinoma (LAC).The primary aim was to investigate the value of a nomogram incorporating clinical and computed tomography (CT) factors to predict LVI in LAC.
Methods
A total of 450 patients with LAC were retrospectively enrolled. Clinical data and CT features were analyzed to identify independent predictors of LVI. A nomogram incorporating the independent predictors of LVI was built. The performance of the nomogram was evaluated by assessing its discriminative ability and clinical utility.
Results
Carcinoembryonic antigen (CEA) level, maximum tumor diameter, spiculation, and vacuole sign were independent predictors of LVI. The LVI prediction nomogram showed good discrimination in the training set [area under the curve (AUC), 0.800] and the test set (AUC, 0.790).
Conclusions
The nomogram developed in this study can predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-making.
Springer Science and Business Media LLC
Title: A nomogram for predicting lymphovascular invasion in lung adenocarcinoma
Description:
Abstract
Backgroud
Lymphovascular invasion (LVI) is histological factor that is closely related to prognosis of lung adenocarcinoma (LAC).
The primary aim was to investigate the value of a nomogram incorporating clinical and computed tomography (CT) factors to predict LVI in LAC.
Methods
A total of 450 patients with LAC were retrospectively enrolled.
Clinical data and CT features were analyzed to identify independent predictors of LVI.
A nomogram incorporating the independent predictors of LVI was built.
The performance of the nomogram was evaluated by assessing its discriminative ability and clinical utility.
Results
Carcinoembryonic antigen (CEA) level, maximum tumor diameter, spiculation, and vacuole sign were independent predictors of LVI.
The LVI prediction nomogram showed good discrimination in the training set [area under the curve (AUC), 0.
800] and the test set (AUC, 0.
790).
Conclusions
The nomogram developed in this study can predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-making.
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