NDLI: Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study

Content Provider	Springer Nature : BioMed Central
Author	Zeng, Ying Chen, Jing Lin, Shanyue Liu, Haibo Zhou, Yingjun Zhou, Xiao
Abstract	Background Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathological invasiveness of pure ground-glass nodules present in lung adenocarcinoma. Methods This was a retrospective, cross-sectional, bicentric study with data collected from January 1, 2018, to June 1, 2022. We divided the dataset into a training cohort (n = 88) from one center and an external validation cohort (n = 59) from another center. Radiomic signatures (rad-scores) were obtained after features were selected through correlation and least absolute shrinkage and selection operator analysis. Three machine learning models, a support vector machine model, a random forest model, and a generalized linear model, were then applied to build radiomic models. Results Invasive adenocarcinoma had a higher rad-score (P<0.001) in the GTV and GPTV. The area under the curves (AUC) of GTV, PTV, and GPTV were 0.839, 0.809, and 0.855 in the training cohort and 0.755, 0.777, and 0.801 in the external validation cohort, respectively. The GPTV model had higher AUCs for predicting pathological invasiveness. The random forest model had the best validity and fit for the proposed machine learning approach, suggesting that it may be the most appropriate model. Conclusions GPTV had the highest diagnostic efficiency for predicting pathological invasiveness in patients with pure ground-grass nodules, and the random forest model outperformed other predictive models.
Related Links	https://cardiothoracicsurgery.biomedcentral.com/counter/pdf/10.1186/s13019-024-03289-3.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	17498090
DOI	10.1186/s13019-024-03289-3
Journal	Journal of Cardiothoracic Surgery
Issue Number	1
Volume Number	20
Language	English
Publisher	BioMed Central
Publisher Date	2025-02-11
Access Restriction	Open
Subject Keyword	Cardiac Surgery Thoracic Surgery Computed tomography Pure ground-glass nodules Machine learning Radiomics Invasiveness
Content Type	Text
Resource Type	Article
Subject	Surgery Cardiology and Cardiovascular Medicine Pulmonary and Respiratory Medicine
Journal Impact Factor	1.5/2023
5-Year Journal Impact Factor	1.6/2023

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