NDLI: Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study

Content Provider	Springer Nature : BioMed Central
Author	Wang, Zhengming Wang, Fei Yang, Yan Fan, Weijie Wen, Li Zhang, Dong
Abstract	Purpose To develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images. Methods This is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility. Results The T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram. Conclusion A nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.
Related Links	https://bmcpulmmed.biomedcentral.com/counter/pdf/10.1186/s12890-024-03360-8.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	14712466
DOI	10.1186/s12890-024-03360-8
Journal	BMC Pulmonary Medicine
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-10-26
Access Restriction	Open
Subject Keyword	Pneumology Respiratory System Internal Medicine Intensive Critical Care Medicine CT image Pulmonary nodule Identification Radiomic Nomogram Pneumology/Respiratory System
Content Type	Text
Resource Type	Article
Subject	Pulmonary and Respiratory Medicine
Journal Impact Factor	2.6/2023
5-Year Journal Impact Factor	3/2023

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