Development and validation of a CT-based radiomics signature for identifying high-risk neuroblastomas under the revised Children's Oncology Group classification system.
computed tomography
neuroblastomas
radiomics
risk stratification
Journal
Pediatric blood & cancer
ISSN: 1545-5017
Titre abrégé: Pediatr Blood Cancer
Pays: United States
ID NLM: 101186624
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
revised:
09
02
2023
received:
07
12
2022
accepted:
13
02
2023
pubmed:
8
3
2023
medline:
25
3
2023
entrez:
7
3
2023
Statut:
ppublish
Résumé
To develop and validate a radiomics signature based on computed tomography (CT) for identifying high-risk neuroblastomas. This retrospective study included 339 patients with neuroblastomas, who were classified into high-risk and non-high-risk groups according to the revised Children's Oncology Group classification system. These patients were then randomly divided into a training set (n = 237) and a testing set (n = 102). Pretherapy CT images of the arterial phase were segmented by two radiologists. Pyradiomics package and FeAture Explorer software were used to extract and process radiomics features. Radiomics models based on linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM) were constructed, and the area under the curve (AUC), 95% confidence interval (CI), and accuracy were calculated. The optimal LDA, LR, and SVM models had 11, 12, and 14 radiomics features, respectively. The AUC of the LDA model in the training and testing sets were 0.877 (95% CI: 0.833-0.921) and 0.867 (95% CI: 0.797-0.937), with an accuracy of 0.823 and 0.804, respectively. The AUC of the LR model in the training and testing sets were 0.881 (95% CI: 0.839-0.924) and 0.855 (95% CI: 0.781-0.930), with an accuracy of 0.823 and 0.804, respectively. The AUC of the SVM model in the training and testing sets were 0.879 (95% CI: 0.836-0.923) and 0.862 (95% CI: 0.791-0.934), with an accuracy of 0.827 and 0.804, respectively. CT-based radiomics is able to identify high-risk neuroblastomas and may provide additional image biomarkers for the identification of high-risk neuroblastomas.
Sections du résumé
BACKGROUND
To develop and validate a radiomics signature based on computed tomography (CT) for identifying high-risk neuroblastomas.
PROCEDURE
This retrospective study included 339 patients with neuroblastomas, who were classified into high-risk and non-high-risk groups according to the revised Children's Oncology Group classification system. These patients were then randomly divided into a training set (n = 237) and a testing set (n = 102). Pretherapy CT images of the arterial phase were segmented by two radiologists. Pyradiomics package and FeAture Explorer software were used to extract and process radiomics features. Radiomics models based on linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM) were constructed, and the area under the curve (AUC), 95% confidence interval (CI), and accuracy were calculated.
RESULTS
The optimal LDA, LR, and SVM models had 11, 12, and 14 radiomics features, respectively. The AUC of the LDA model in the training and testing sets were 0.877 (95% CI: 0.833-0.921) and 0.867 (95% CI: 0.797-0.937), with an accuracy of 0.823 and 0.804, respectively. The AUC of the LR model in the training and testing sets were 0.881 (95% CI: 0.839-0.924) and 0.855 (95% CI: 0.781-0.930), with an accuracy of 0.823 and 0.804, respectively. The AUC of the SVM model in the training and testing sets were 0.879 (95% CI: 0.836-0.923) and 0.862 (95% CI: 0.791-0.934), with an accuracy of 0.827 and 0.804, respectively.
CONCLUSIONS
CT-based radiomics is able to identify high-risk neuroblastomas and may provide additional image biomarkers for the identification of high-risk neuroblastomas.
Substances chimiques
Biomarkers
0
Types de publication
Randomized Controlled Trial
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e30280Informations de copyright
© 2023 Wiley Periodicals LLC.
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