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

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...

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Autores principales: Cangir, Ayten Kayi, Orhan, Kaan, Kahya, Yusuf, Uğurum Yücemen, Ayse, Aktürk, İslam, Ozakinci, Hilal, Gursoy Coruh, Aysegul, Dizbay Sak, Serpil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871366/
https://www.ncbi.nlm.nih.gov/pubmed/35204507
http://dx.doi.org/10.3390/diagnostics12020416
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author Cangir, Ayten Kayi
Orhan, Kaan
Kahya, Yusuf
Uğurum Yücemen, Ayse
Aktürk, İslam
Ozakinci, Hilal
Gursoy Coruh, Aysegul
Dizbay Sak, Serpil
author_facet Cangir, Ayten Kayi
Orhan, Kaan
Kahya, Yusuf
Uğurum Yücemen, Ayse
Aktürk, İslam
Ozakinci, Hilal
Gursoy Coruh, Aysegul
Dizbay Sak, Serpil
author_sort Cangir, Ayten Kayi
collection PubMed
description 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).
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spelling pubmed-88713662022-02-25 A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors Cangir, Ayten Kayi Orhan, Kaan Kahya, Yusuf Uğurum Yücemen, Ayse Aktürk, İslam Ozakinci, Hilal Gursoy Coruh, Aysegul Dizbay Sak, Serpil Diagnostics (Basel) Article 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). MDPI 2022-02-05 /pmc/articles/PMC8871366/ /pubmed/35204507 http://dx.doi.org/10.3390/diagnostics12020416 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cangir, Ayten Kayi
Orhan, Kaan
Kahya, Yusuf
Uğurum Yücemen, Ayse
Aktürk, İslam
Ozakinci, Hilal
Gursoy Coruh, Aysegul
Dizbay Sak, Serpil
A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors
title A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors
title_full A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors
title_fullStr A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors
title_full_unstemmed A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors
title_short A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors
title_sort ct-based radiomic signature for the differentiation of pulmonary hamartomas from carcinoid tumors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871366/
https://www.ncbi.nlm.nih.gov/pubmed/35204507
http://dx.doi.org/10.3390/diagnostics12020416
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