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A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules by Li Min Xue; Ying Li; Yu Zhang; Shu Chao Wang; Ran Ying Zhang; Jian Ding Ye; Hong Yu; Jin Wei Qiang is a Medicine article available to read on EtoBox.

What is A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules about?

Objectives Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules. Methods A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve. Results The radiomics

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Author

Li Min Xue; Ying Li; Yu Zhang; Shu Chao Wang; Ran Ying Zhang; Jian Ding Ye; Hong Yu; Jin Wei Qiang

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Medicine (Health Sciences)