Whole-tumor radiomics analysis of T2-weighted imaging in differentiating neuroblastoma from ganglioneuroblastoma/ganglioneuroma in children: an exploratory study.

Purpose: To examine the potential of whole-tumor radiomics analysis of T2-weighted imaging (T2WI) in differentiating neuroblastoma (NB) from ganglioneuroblastoma/ganglioneuroma (GNB/GN) in children. Materials and methods: This study included 102 children with peripheral neuroblastic tumors, comprisi...

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Publicado en:Abdominal Radiology Vol. 48; no. 4; pp. 1372 - 1383
Autores principales: Wang, Haoru, Chen, Xin, Yu, Wenqing, Xie, Mingye, Zhang, Li, Ding, Hao, Li, Ting, Qin, Jinjie, He, Ling
Formato: Journal Article
Publicado: Springer Nature Apr2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2023
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s00261-023-03862-9
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        atl: Whole-tumor radiomics analysis of T2-weighted imaging in differentiating neuroblastoma from ganglioneuroblastoma/ganglioneuroma in children: an exploratory study.
      aug:
        au:
          Wang, Haoru
          Chen, Xin
          Yu, Wenqing
          Xie, Mingye
          Zhang, Li
          Ding, Hao
          Li, Ting
          Qin, Jinjie
          He, Ling
        affil: Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, 400014, Chongqing, China
      sug:
      ab: Purpose: To examine the potential of whole-tumor radiomics analysis of T2-weighted imaging (T2WI) in differentiating neuroblastoma (NB) from ganglioneuroblastoma/ganglioneuroma (GNB/GN) in children. Materials and methods: This study included 102 children with peripheral neuroblastic tumors, comprising 47 NB patients and 55 GNB/GN patients, which were randomly divided into a training group (n = 72) and a test group (n = 30). Radiomics features were extracted from T2WI images, and feature dimensionality reduction was applied. Linear discriminant analysis was used to construct radiomics models, and one-standard error role combined with leave-one-out cross-validation was used to choose the optimal radiomics model with the least predictive error. Subsequently, the patient age at initial diagnosis and the selected radiomics features were incorporated to construct a combined model. The receiver operator characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve (CIC) were applied to evaluate the diagnostic performance and clinical utility of the models. Results: Fifteen radiomics features were eventually chosen to construct the optimal radiomics model. The area under the curve (AUC) of the radiomics model in the training group and test group was 0.940 [95% confidence interval (CI) 0.886, 0.995] and 0.799 (95%CI 0.632, 0.966), respectively. The combined model, which incorporated patient age and radiomics features, achieved an AUC of 0.963 (95%CI 0.925, 1.000) in the training group and 0.871 (95%CI 0.744, 0.997) in the test group. DCA and CIC demonstrated that the radiomics model and combined model could provide benefits at various thresholds, with the combined model being superior to the radiomics model. Conclusion: Radiomics features derived from T2WI, in combination with the age of the patient at initial diagnosis, may offer a quantitative method for distinguishing NB from GNB/GN, thus aiding in the pathological differentiation of peripheral neuroblastic tumors in children.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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