The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma.

ABSTRACT:

Background

Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN.

Methods

This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regressi

SUBMITTER: Liu Q 

PROVIDER: S-EPMC7278188 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature