Study on the value of 3D visualization in differentiating IA and non-IA pulmonary ground-glass nodules.


Journal

Clinical radiology
ISSN: 1365-229X
Titre abrégé: Clin Radiol
Pays: England
ID NLM: 1306016

Informations de publication

Date de publication:
10 Aug 2024
Historique:
received: 25 01 2024
revised: 04 07 2024
accepted: 03 08 2024
medline: 13 9 2024
pubmed: 13 9 2024
entrez: 12 9 2024
Statut: aheadofprint

Résumé

To determine the most effective diagnostic markers and their associated thresholds for Ground-glass nodules (GGN) for identification of invasive adenocarcinoma (IA) and non-IA (including atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA)), and to explore the application in preoperative surgical evaluation. A total of 126 cases, confirmed by pathology, were retrospectively analyzed. 70 cases were classified as the IA group, while the non-IA group consisted of cases of AAH, AIS, and MIA, with a total of 56 cases. The study compared the differences in demographic, morphological, and three-dimensional (3D) quantitative parameters between the two groups. There were statistically significant differences in various signs such as air bronchogram, lobulation, pleural indentation, spiculation, shape, and margin between the two groups. Additionally, Statistical significance was observed in all 3D quantitative parameters for both groups. Notably, when 3D volume of lesions exceeded 447 mm Reasonable utilization of 3D visualization technology can effectively aid in distinguishing between IA and non-IA. When coupled with clinical data and CT signs, this technique holds vital importance in directing the evaluation of surgical interventions prior to surgery.

Identifiants

pubmed: 39266373
pii: S0009-9260(24)00420-3
doi: 10.1016/j.crad.2024.08.008
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Auteurs

J Chen (J)

Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR China; Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang 330000, PR China.

X Zeng (X)

Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR China; Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang 330000, PR China.

F Li (F)

Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR China; Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang 330000, PR China.

J Peng (J)

Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR China; Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang 330000, PR China. Electronic address: jidongpeng2021@163.com.

Classifications MeSH