A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules

A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics...

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Bibliographic Details
Main Authors: Leonardo Rundo, Roberta Eufrasia Ledda, Christian di Noia, Evis Sala, Giancarlo Mauri, Gianluca Milanese, Nicola Sverzellati, Giovanni Apolone, Maria Carla Gilardi, Maria Cristina Messa, Isabella Castiglioni, Ugo Pastorino
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Diagnostics
Subjects:
pulmonary nodules
lung cancer screening
low-dose computed tomography
lung cancer risk stratification
radiomics
machine learning
Online Access:https://www.mdpi.com/2075-4418/11/9/1610

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