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Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors

This study aimed to develop and validate an analysis system based on preoperative computed tomography (CT) to predict the risk stratification in pediatric malignant peripheral neuroblastic tumors (PNTs). A total of 405 patients with malignant PNTs (184 girls and 221 boys; mean age, 33.8 ± 29.1 month...

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Autores principales: Wang, Xiaoxia, Wang, Xinrong, Wu, Tingfan, Hu, Liwei, Xu, Min, Tang, Jingyan, Li, Xin, Zhong, Yumin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10681616/
https://www.ncbi.nlm.nih.gov/pubmed/38013377
http://dx.doi.org/10.1097/MD.0000000000035690
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author Wang, Xiaoxia
Wang, Xinrong
Wu, Tingfan
Hu, Liwei
Xu, Min
Tang, Jingyan
Li, Xin
Zhong, Yumin
author_facet Wang, Xiaoxia
Wang, Xinrong
Wu, Tingfan
Hu, Liwei
Xu, Min
Tang, Jingyan
Li, Xin
Zhong, Yumin
author_sort Wang, Xiaoxia
collection PubMed
description This study aimed to develop and validate an analysis system based on preoperative computed tomography (CT) to predict the risk stratification in pediatric malignant peripheral neuroblastic tumors (PNTs). A total of 405 patients with malignant PNTs (184 girls and 221 boys; mean age, 33.8 ± 29.1 months) were retrospectively evaluated between January 2010 and June 2018. Radiomic features were extracted from manually segmented tumors on preoperative CT images. Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were used to eliminate redundancy and select features. A risk model was built to stratify low-, intermediate-, and high-risk groups. An image-defined risk factor (IDRFs) model was developed to classify 266 patients with malignant PNTs and one or more IDRFs into high-risk and non-high-risk groups. The performance of the predictive models was evaluated with respect to accuracy (Acc) and receiver operating characteristic (ROC) curve, including the area under the ROC curve (AUC). The risk model demonstrated good discrimination capability, with an area under the curve (AUC) of 0.903 to distinguish high-risk from non-high-risk groups, and 0.747 to classify intermediate- and low-risk groups. In the IDRF-based risk model with the number of IDRFs, the AUC was 0.876 for classifying the high-risk and non-high-risk groups. Radiomic analysis based on preoperative CT images has the potential to stratify the risk of pediatric malignant PNTs. It had outstanding efficiency in distinguishing patients in the high-risk group, and this predictive model of risk stratification could assist in selecting optimal aggressive treatment options.
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spelling pubmed-106816162023-11-24 Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors Wang, Xiaoxia Wang, Xinrong Wu, Tingfan Hu, Liwei Xu, Min Tang, Jingyan Li, Xin Zhong, Yumin Medicine (Baltimore) 5700 This study aimed to develop and validate an analysis system based on preoperative computed tomography (CT) to predict the risk stratification in pediatric malignant peripheral neuroblastic tumors (PNTs). A total of 405 patients with malignant PNTs (184 girls and 221 boys; mean age, 33.8 ± 29.1 months) were retrospectively evaluated between January 2010 and June 2018. Radiomic features were extracted from manually segmented tumors on preoperative CT images. Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were used to eliminate redundancy and select features. A risk model was built to stratify low-, intermediate-, and high-risk groups. An image-defined risk factor (IDRFs) model was developed to classify 266 patients with malignant PNTs and one or more IDRFs into high-risk and non-high-risk groups. The performance of the predictive models was evaluated with respect to accuracy (Acc) and receiver operating characteristic (ROC) curve, including the area under the ROC curve (AUC). The risk model demonstrated good discrimination capability, with an area under the curve (AUC) of 0.903 to distinguish high-risk from non-high-risk groups, and 0.747 to classify intermediate- and low-risk groups. In the IDRF-based risk model with the number of IDRFs, the AUC was 0.876 for classifying the high-risk and non-high-risk groups. Radiomic analysis based on preoperative CT images has the potential to stratify the risk of pediatric malignant PNTs. It had outstanding efficiency in distinguishing patients in the high-risk group, and this predictive model of risk stratification could assist in selecting optimal aggressive treatment options. Lippincott Williams & Wilkins 2023-11-24 /pmc/articles/PMC10681616/ /pubmed/38013377 http://dx.doi.org/10.1097/MD.0000000000035690 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle 5700
Wang, Xiaoxia
Wang, Xinrong
Wu, Tingfan
Hu, Liwei
Xu, Min
Tang, Jingyan
Li, Xin
Zhong, Yumin
Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors
title Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors
title_full Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors
title_fullStr Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors
title_full_unstemmed Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors
title_short Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors
title_sort computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors
topic 5700
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10681616/
https://www.ncbi.nlm.nih.gov/pubmed/38013377
http://dx.doi.org/10.1097/MD.0000000000035690
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