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Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma

BACKGROUND: This retrospective study aimed to develop and validate a combined model based [(18)F]FDG PET/CT radiomics and clinical parameters for predicting recurrence in high-risk pediatric neuroblastoma patients. METHODS: Eighty-four high-risk neuroblastoma patients were retrospectively enrolled a...

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Autores principales: Feng, Lijuan, Qian, Luodan, Yang, Shen, Ren, Qinghua, Zhang, Shuxin, Qin, Hong, Wang, Wei, Wang, Chao, Zhang, Hui, Yang, Jigang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148481/
https://www.ncbi.nlm.nih.gov/pubmed/35643445
http://dx.doi.org/10.1186/s12880-022-00828-z
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author Feng, Lijuan
Qian, Luodan
Yang, Shen
Ren, Qinghua
Zhang, Shuxin
Qin, Hong
Wang, Wei
Wang, Chao
Zhang, Hui
Yang, Jigang
author_facet Feng, Lijuan
Qian, Luodan
Yang, Shen
Ren, Qinghua
Zhang, Shuxin
Qin, Hong
Wang, Wei
Wang, Chao
Zhang, Hui
Yang, Jigang
author_sort Feng, Lijuan
collection PubMed
description BACKGROUND: This retrospective study aimed to develop and validate a combined model based [(18)F]FDG PET/CT radiomics and clinical parameters for predicting recurrence in high-risk pediatric neuroblastoma patients. METHODS: Eighty-four high-risk neuroblastoma patients were retrospectively enrolled and divided into training and test sets according to the ratio of 3:2. [(18)F]FDG PET/CT images of the tumor were segmented by 3D Slicer software and the radiomics features were extracted. The effective features were selected by the least absolute shrinkage and selection operator to construct the radiomics score (Rad_score). And the radiomics model (R_model) was constructed based on Rad_score for prediction of recurrence. Then, univariate and multivariate analyses were used to screen out the independent clinical risk parameters and construct the clinical model (C_model). A combined model (RC_model) was developed based on the Rad_score and independent clinical risk parameters and presented as radiomics nomogram. The performance of the above three models was assessed by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS: Seven radiomics features were selected for building the R_model. The AUCs of the C_model in training and test sets were 0.744 (95% confidence interval [CI], 0.595–0.874) and 0.750 (95% CI, 0.577–0.904), respectively. The R_model yielded AUCs of 0.813 (95% CI, 0.685–0.916) and 0.869 (95% CI, 0.715–0.985) in the training and test sets, respectively. The RC_model demonstrated the largest AUCs of 0.889 (95% CI, 0.794–0.963) and 0.892 (95% CI, 0.758–0.992) in the training and test sets, respectively. DCA demonstrated that RC_model added more net benefits than either the C_model or the R_model for predicting recurrence in high-risk pediatric neuroblastoma. CONCLUSIONS: The combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice.
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spelling pubmed-91484812022-05-30 Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma Feng, Lijuan Qian, Luodan Yang, Shen Ren, Qinghua Zhang, Shuxin Qin, Hong Wang, Wei Wang, Chao Zhang, Hui Yang, Jigang BMC Med Imaging Research BACKGROUND: This retrospective study aimed to develop and validate a combined model based [(18)F]FDG PET/CT radiomics and clinical parameters for predicting recurrence in high-risk pediatric neuroblastoma patients. METHODS: Eighty-four high-risk neuroblastoma patients were retrospectively enrolled and divided into training and test sets according to the ratio of 3:2. [(18)F]FDG PET/CT images of the tumor were segmented by 3D Slicer software and the radiomics features were extracted. The effective features were selected by the least absolute shrinkage and selection operator to construct the radiomics score (Rad_score). And the radiomics model (R_model) was constructed based on Rad_score for prediction of recurrence. Then, univariate and multivariate analyses were used to screen out the independent clinical risk parameters and construct the clinical model (C_model). A combined model (RC_model) was developed based on the Rad_score and independent clinical risk parameters and presented as radiomics nomogram. The performance of the above three models was assessed by the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS: Seven radiomics features were selected for building the R_model. The AUCs of the C_model in training and test sets were 0.744 (95% confidence interval [CI], 0.595–0.874) and 0.750 (95% CI, 0.577–0.904), respectively. The R_model yielded AUCs of 0.813 (95% CI, 0.685–0.916) and 0.869 (95% CI, 0.715–0.985) in the training and test sets, respectively. The RC_model demonstrated the largest AUCs of 0.889 (95% CI, 0.794–0.963) and 0.892 (95% CI, 0.758–0.992) in the training and test sets, respectively. DCA demonstrated that RC_model added more net benefits than either the C_model or the R_model for predicting recurrence in high-risk pediatric neuroblastoma. CONCLUSIONS: The combined model performed well for predicting recurrence in high-risk pediatric neuroblastoma, which can facilitate disease follow-up and management in clinical practice. BioMed Central 2022-05-28 /pmc/articles/PMC9148481/ /pubmed/35643445 http://dx.doi.org/10.1186/s12880-022-00828-z Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Feng, Lijuan
Qian, Luodan
Yang, Shen
Ren, Qinghua
Zhang, Shuxin
Qin, Hong
Wang, Wei
Wang, Chao
Zhang, Hui
Yang, Jigang
Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma
title Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma
title_full Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma
title_fullStr Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma
title_full_unstemmed Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma
title_short Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma
title_sort clinical parameters combined with radiomics features of pet/ct can predict recurrence in patients with high-risk pediatric neuroblastoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148481/
https://www.ncbi.nlm.nih.gov/pubmed/35643445
http://dx.doi.org/10.1186/s12880-022-00828-z
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