Back to Search Start Over

A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors

Authors :
Ayten Kayi Cangir
Kaan Orhan
Yusuf Kahya
Ayse Uğurum Yücemen
İslam Aktürk
Hilal Ozakinci
Aysegul Gursoy Coruh
Serpil Dizbay Sak
Source :
Diagnostics, Vol 12, Iss 2, p 416 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Radiomics is a new image processing technology developed in recent years. In this study, CT radiomic features are evaluated to differentiate pulmonary hamartomas (PHs) from pulmonary carcinoid tumors (PCTs). A total of 138 patients (78 PCTs and 60 PHs) were evaluated. The Radcloud platform (Huiying Medical Technology Co., Ltd., Beijing, China) was used for managing the data, clinical data, and subsequent radiomics analysis. Two hand-crafted radiomics models are prepared in this study: the first model includes the data regarding all of the patients to differentiate between the groups; the second model includes 78 PCTs and 38 PHs without signs of fat tissue. The separation of the training and validation datasets was performed randomly using an (8:2) ratio and 620 random seeds. The results revealed that the MLP method (RF) was best for PH (AUC = 0.999) and PCT (AUC = 0.999) for the first model (AUC = 0.836), and PC (AUC = 0.836) in the test set for the second model. Radiomics tumor features derived from CT images are useful to differentiate the carcinoid tumors from hamartomas with high accuracy. Radiomics features may be used to differentiate PHs from PCTs with high levels of accuracy, even without the presence of fat on the CT. Advances in knowledge: CT-based radiomic holds great promise for a more accurate preoperative diagnosis of solitary pulmonary nodules (SPNs).

Details

Language :
English
ISSN :
20754418
Volume :
12
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.4692b6d3c3f04ce4b71b9ddb70494e7e
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics12020416