Cargando…

The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein

BACKGROUND: The aim of this study was to develop a radiomics machine learning model based on computed tomography (CT) that can predict whether thymic epithelial tumors (TETs) can be separated from veins during surgery and to compare the accuracy of the radiomics model to that of radiologists. METHOD...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Zhiyang, Wang, Fuqiang, Zhang, Hanlu, Zheng, Hong, Zhou, Xue, Wang, Zhensong, Xie, Shenglong, Peng, Lei, Wang, Xuyang, Wang, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498270/
https://www.ncbi.nlm.nih.gov/pubmed/37711814
http://dx.doi.org/10.21037/qims-22-1050
_version_ 1785105484562300928
author Li, Zhiyang
Wang, Fuqiang
Zhang, Hanlu
Zheng, Hong
Zhou, Xue
Wang, Zhensong
Xie, Shenglong
Peng, Lei
Wang, Xuyang
Wang, Yun
author_facet Li, Zhiyang
Wang, Fuqiang
Zhang, Hanlu
Zheng, Hong
Zhou, Xue
Wang, Zhensong
Xie, Shenglong
Peng, Lei
Wang, Xuyang
Wang, Yun
author_sort Li, Zhiyang
collection PubMed
description BACKGROUND: The aim of this study was to develop a radiomics machine learning model based on computed tomography (CT) that can predict whether thymic epithelial tumors (TETs) can be separated from veins during surgery and to compare the accuracy of the radiomics model to that of radiologists. METHODS: Patients who underwent thymectomy at our hospital from 2009 to 2017 were included in the screening process. After the selection of patients according to the inclusion and exclusion criteria, the cohort was randomly divided into training and testing groups, and CT images of these patients were collected. Subsequently, two-dimensional (2D) and three-dimensional (3D) regions of interest were labelled using ITK-SNAP 3.8.0 software, and Radiomics features were extracted using Python software (Python Software Foundation) and selected through the least absolute shrinkage and selection operator (LASSO) regression model. To construct the classifier, a support vector machine (SVM) was employed, and a nomogram was created using logistic regression to predict vascular inseparable TETs based on the radiomics score (radscore) and image features. To assess the accuracy of these models, area under receiver operating characteristic (ROC) curves of these models were calculated, and differences among the models were identified using the Delong test. RESULTS: In this retrospective study, 204 patients with TETs were included, among whom 21 were diagnosed with surgical vascularly inseparable TETs. The area under ROC curve (AUC) of the 2D model, 3D model, 2D + 3D model, and radiologist diagnoses were 0.94, 0.92, 0.95, and 0.87 in the training cohort and 0.95, 0.92, 0.98, and 0.78 in testing cohort, respectively. The Delong test revealed a significant improvement in the performance of the radiomics models compared to radiologists’ diagnoses. The logistic regression selected 3 image features, namely maximum diameter of the tumor, degree of abutment of vessel circumference >50%, and absence of the mediastinal fat layer or space between the tumor and surrounding structures. These features, along with the radscore, were included to develop a nomogram. The AUCs of this nomogram were 0.99 in both the training set and testing set, and the Delong test did not find a significant difference between ROC plots of the nomogram and radiomics models. CONCLUSIONS: The proposed radiomics model could accurately predict surgical vascularly inseparable TETs preoperatively and was shown to have a higher predictive value than the radiologists.
format Online
Article
Text
id pubmed-10498270
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher AME Publishing Company
record_format MEDLINE/PubMed
spelling pubmed-104982702023-09-14 The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein Li, Zhiyang Wang, Fuqiang Zhang, Hanlu Zheng, Hong Zhou, Xue Wang, Zhensong Xie, Shenglong Peng, Lei Wang, Xuyang Wang, Yun Quant Imaging Med Surg Original Article BACKGROUND: The aim of this study was to develop a radiomics machine learning model based on computed tomography (CT) that can predict whether thymic epithelial tumors (TETs) can be separated from veins during surgery and to compare the accuracy of the radiomics model to that of radiologists. METHODS: Patients who underwent thymectomy at our hospital from 2009 to 2017 were included in the screening process. After the selection of patients according to the inclusion and exclusion criteria, the cohort was randomly divided into training and testing groups, and CT images of these patients were collected. Subsequently, two-dimensional (2D) and three-dimensional (3D) regions of interest were labelled using ITK-SNAP 3.8.0 software, and Radiomics features were extracted using Python software (Python Software Foundation) and selected through the least absolute shrinkage and selection operator (LASSO) regression model. To construct the classifier, a support vector machine (SVM) was employed, and a nomogram was created using logistic regression to predict vascular inseparable TETs based on the radiomics score (radscore) and image features. To assess the accuracy of these models, area under receiver operating characteristic (ROC) curves of these models were calculated, and differences among the models were identified using the Delong test. RESULTS: In this retrospective study, 204 patients with TETs were included, among whom 21 were diagnosed with surgical vascularly inseparable TETs. The area under ROC curve (AUC) of the 2D model, 3D model, 2D + 3D model, and radiologist diagnoses were 0.94, 0.92, 0.95, and 0.87 in the training cohort and 0.95, 0.92, 0.98, and 0.78 in testing cohort, respectively. The Delong test revealed a significant improvement in the performance of the radiomics models compared to radiologists’ diagnoses. The logistic regression selected 3 image features, namely maximum diameter of the tumor, degree of abutment of vessel circumference >50%, and absence of the mediastinal fat layer or space between the tumor and surrounding structures. These features, along with the radscore, were included to develop a nomogram. The AUCs of this nomogram were 0.99 in both the training set and testing set, and the Delong test did not find a significant difference between ROC plots of the nomogram and radiomics models. CONCLUSIONS: The proposed radiomics model could accurately predict surgical vascularly inseparable TETs preoperatively and was shown to have a higher predictive value than the radiologists. AME Publishing Company 2023-07-03 2023-09-01 /pmc/articles/PMC10498270/ /pubmed/37711814 http://dx.doi.org/10.21037/qims-22-1050 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Li, Zhiyang
Wang, Fuqiang
Zhang, Hanlu
Zheng, Hong
Zhou, Xue
Wang, Zhensong
Xie, Shenglong
Peng, Lei
Wang, Xuyang
Wang, Yun
The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein
title The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein
title_full The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein
title_fullStr The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein
title_full_unstemmed The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein
title_short The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein
title_sort predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498270/
https://www.ncbi.nlm.nih.gov/pubmed/37711814
http://dx.doi.org/10.21037/qims-22-1050
work_keys_str_mv AT lizhiyang thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT wangfuqiang thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT zhanghanlu thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT zhenghong thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT zhouxue thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT wangzhensong thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT xieshenglong thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT penglei thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT wangxuyang thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT wangyun thepredictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT lizhiyang predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT wangfuqiang predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT zhanghanlu predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT zhenghong predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT zhouxue predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT wangzhensong predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT xieshenglong predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT penglei predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT wangxuyang predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein
AT wangyun predictivevalueofacomputedtomographybasedradiomicsmodelforthesurgicalseparabilityofthymicepithelialtumorsfromthesuperiorvenacavaandtheleftinnominatevein