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Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma

PURPOSE: To predict the International Neuroblastoma Pathology Classification (INPC) in neuroblastoma using a computed tomography (CT)-based radiomics approach. METHODS: We enrolled 297 patients with neuroblastoma retrospectively and divided them into a training group (n = 208) and a testing group (n...

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Autores principales: Wang, Haoru, Xie, Mingye, Chen, Xin, Zhu, Jin, Zhang, Li, Ding, Hao, Pan, Zhengxia, He, Ling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267098/
https://www.ncbi.nlm.nih.gov/pubmed/37316589
http://dx.doi.org/10.1186/s13244-023-01418-5
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author Wang, Haoru
Xie, Mingye
Chen, Xin
Zhu, Jin
Zhang, Li
Ding, Hao
Pan, Zhengxia
He, Ling
author_facet Wang, Haoru
Xie, Mingye
Chen, Xin
Zhu, Jin
Zhang, Li
Ding, Hao
Pan, Zhengxia
He, Ling
author_sort Wang, Haoru
collection PubMed
description PURPOSE: To predict the International Neuroblastoma Pathology Classification (INPC) in neuroblastoma using a computed tomography (CT)-based radiomics approach. METHODS: We enrolled 297 patients with neuroblastoma retrospectively and divided them into a training group (n = 208) and a testing group (n = 89). To balance the classes in the training group, a Synthetic Minority Over-sampling Technique was applied. A logistic regression radiomics model based on the radiomics features after dimensionality reduction was then constructed and validated in both the training and testing groups. To evaluate the diagnostic performance of the radiomics model, the receiver operating characteristic curve and calibration curve were utilized. Moreover, the decision curve analysis to assess the net benefits of the radiomics model at different high-risk thresholds was employed. RESULTS: Seventeen radiomics features were used to construct radiomics model. In the training group, radiomics model achieved an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.851 (95% confidence interval (CI) 0.805–0.897), 0.770, 0.694, and 0.847, respectively. In the testing group, radiomics model achieved an AUC, accuracy, sensitivity, and specificity of 0.816 (95% CI 0.725–0.906), 0.787, 0.793, and 0.778, respectively. The calibration curve indicated that the radiomics model was well fitted in both the training and testing groups (p > 0.05). Decision curve analysis further confirmed that the radiomics model performed well at different high-risk thresholds. CONCLUSION: Radiomics analysis of contrast-enhanced CT demonstrates favorable diagnostic capabilities in distinguishing the INPC subgroups of neuroblastoma. GRAPHICAL ABSTRACT: [Image: see text] CRITICAL RELEVANCE STATEMENT: Radiomics features of contrast-enhanced CT images correlate with the International Neuroblastoma Pathology Classification (INPC) of neuroblastoma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01418-5.
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spelling pubmed-102670982023-06-15 Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma Wang, Haoru Xie, Mingye Chen, Xin Zhu, Jin Zhang, Li Ding, Hao Pan, Zhengxia He, Ling Insights Imaging Original Article PURPOSE: To predict the International Neuroblastoma Pathology Classification (INPC) in neuroblastoma using a computed tomography (CT)-based radiomics approach. METHODS: We enrolled 297 patients with neuroblastoma retrospectively and divided them into a training group (n = 208) and a testing group (n = 89). To balance the classes in the training group, a Synthetic Minority Over-sampling Technique was applied. A logistic regression radiomics model based on the radiomics features after dimensionality reduction was then constructed and validated in both the training and testing groups. To evaluate the diagnostic performance of the radiomics model, the receiver operating characteristic curve and calibration curve were utilized. Moreover, the decision curve analysis to assess the net benefits of the radiomics model at different high-risk thresholds was employed. RESULTS: Seventeen radiomics features were used to construct radiomics model. In the training group, radiomics model achieved an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.851 (95% confidence interval (CI) 0.805–0.897), 0.770, 0.694, and 0.847, respectively. In the testing group, radiomics model achieved an AUC, accuracy, sensitivity, and specificity of 0.816 (95% CI 0.725–0.906), 0.787, 0.793, and 0.778, respectively. The calibration curve indicated that the radiomics model was well fitted in both the training and testing groups (p > 0.05). Decision curve analysis further confirmed that the radiomics model performed well at different high-risk thresholds. CONCLUSION: Radiomics analysis of contrast-enhanced CT demonstrates favorable diagnostic capabilities in distinguishing the INPC subgroups of neuroblastoma. GRAPHICAL ABSTRACT: [Image: see text] CRITICAL RELEVANCE STATEMENT: Radiomics features of contrast-enhanced CT images correlate with the International Neuroblastoma Pathology Classification (INPC) of neuroblastoma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01418-5. Springer Vienna 2023-06-14 /pmc/articles/PMC10267098/ /pubmed/37316589 http://dx.doi.org/10.1186/s13244-023-01418-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Wang, Haoru
Xie, Mingye
Chen, Xin
Zhu, Jin
Zhang, Li
Ding, Hao
Pan, Zhengxia
He, Ling
Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma
title Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma
title_full Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma
title_fullStr Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma
title_full_unstemmed Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma
title_short Radiomics analysis of contrast-enhanced computed tomography in predicting the International Neuroblastoma Pathology Classification in neuroblastoma
title_sort radiomics analysis of contrast-enhanced computed tomography in predicting the international neuroblastoma pathology classification in neuroblastoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267098/
https://www.ncbi.nlm.nih.gov/pubmed/37316589
http://dx.doi.org/10.1186/s13244-023-01418-5
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