Whole-tumor Radiomics Analysis of T2-weighted Imaging in Differentiating Neuroblastoma from Ganglioneuroblastoma/ganglioneuroma in Children: an Exploratory Study

Overview

Journal Abdom Radiol (NY)

Publisher Springer

Specialties Gastroenterology
Radiology

Date 2023 Mar 9

PMID 36892608

Authors

Haoru Wang

Xin Chen

Wenqing Yu

Mingye Xie

Li Zhang

Hao Ding

Ting Li

Jinjie Qin

Ling He

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Giant ganglioneuroma of the mediastinum: a case report.

Song L, Zhang J, Tian B, Li Y, Gu X, Zhang Y Front Oncol. 2024; 14:1408456.

PMID: 39479013 PMC: 11521780. DOI: 10.3389/fonc.2024.1408456.

A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior.

Wang H, Chen X, He L, Ding H, Xie M, Cai J Abdom Radiol (NY). 2024; 49(8):2942-2952.

PMID: 38900321 DOI: 10.1007/s00261-024-04411-8.

Application of Machine Learning and Deep EfficientNets in Distinguishing Neonatal Adrenal Hematomas From Neuroblastoma in Enhanced Computed Tomography Images.

Xie L, Gong Y, Dong K, Shen C, Duan B, Dong R World J Oncol. 2024; 15(1):81-89.

PMID: 38274719 PMC: 10807921. DOI: 10.14740/wjon1744.

A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.

Wang H, Chen X, He L Pediatr Radiol. 2023; 53(13):2742-2755.

PMID: 37945937 DOI: 10.1007/s00247-023-05792-6.

Imaging of Ganglioneuroma: A Literature Review and a Rare Case of Cystic Presentation in an Adolescent Girl.

Pacella G, Brunese M, Donnarumma F, Barrassi M, Bellifemine F, Sciaudone G Diagnostics (Basel). 2023; 13(13).

PMID: 37443583 PMC: 10341194. DOI: 10.3390/diagnostics13132190.

References

Shimada H, Ikegaki N . Genetic and Histopathological Heterogeneity of Neuroblastoma and Precision Therapeutic Approaches for Extremely Unfavorable Histology Subgroups. Biomolecules. 2022; 12(1). PMC: 8773700. DOI: 10.3390/biom12010079. View

Lonergan G, Schwab C, Suarez E, Carlson C . Neuroblastoma, ganglioneuroblastoma, and ganglioneuroma: radiologic-pathologic correlation. Radiographics. 2002; 22(4):911-34. DOI: 10.1148/radiographics.22.4.g02jl15911. View

Choi J, Ro J . Mediastinal neuroblastoma, ganglioneuroblastoma, and ganglioneuroma: Pathology review and diagnostic approach. Semin Diagn Pathol. 2021; 39(2):120-130. DOI: 10.1053/j.semdp.2021.06.007. View

Schmelz K, Toedling J, Huska M, Cwikla M, Kruetzfeldt L, Proba J . Spatial and temporal intratumour heterogeneity has potential consequences for single biopsy-based neuroblastoma treatment decisions. Nat Commun. 2021; 12(1):6804. PMC: 8611017. DOI: 10.1038/s41467-021-26870-z. View

Voss S . Staging and following common pediatric malignancies: MRI versus CT versus functional imaging. Pediatr Radiol. 2018; 48(9):1324-1336. DOI: 10.1007/s00247-018-4162-4. View

Scherer A, Niehues T, Engelbrecht V, Modder U . Imaging diagnosis of retroperitoneal ganglioneuroma in childhood. Pediatr Radiol. 2001; 31(2):106-10. DOI: 10.1007/s002470000381. View

Swift C, Eklund M, Kraveka J, Alazraki A . Updates in Diagnosis, Management, and Treatment of Neuroblastoma. Radiographics. 2018; 38(2):566-580. DOI: 10.1148/rg.2018170132. View

Dumba M, Jawad N, McHugh K . Neuroblastoma and nephroblastoma: a radiological review. Cancer Imaging. 2015; 15:5. PMC: 4446071. DOI: 10.1186/s40644-015-0040-6. View

Cai J, Zeng Y, Zheng H, Qin Y, T K, Zhao J . Retroperitoneal ganglioneuroma in children: CT and MRI features with histologic correlation. Eur J Radiol. 2010; 75(3):315-20. DOI: 10.1016/j.ejrad.2010.05.040. View

10.

Guan Y, Zhang W, Zeng Q, Chen G, He J . CT and MRI findings of thoracic ganglioneuroma. Br J Radiol. 2012; 85(1016):e365-72. PMC: 3587058. DOI: 10.1259/bjr/53395088. View

11.

Choi M, Lee Y, Jung S, Han D . High-resolution 3D T2-weighted SPACE sequence with compressed sensing for the prostate gland: diagnostic performance in comparison with conventional T2-weighted images. Abdom Radiol (NY). 2022; 48(3):1090-1099. DOI: 10.1007/s00261-022-03777-x. View

12.

Wang H, Chen X, Liu H, Yu C, He L . [Computed tomography-based radiomics for differential of retroperitoneal neuroblastoma and ganglioneuroblastoma in children]. Nan Fang Yi Ke Da Xue Xue Bao. 2021; 41(10):1569-1576. PMC: 8586852. DOI: 10.12122/j.issn.1673-4254.2021.10.17. View

13.

Chen X, Wang H, Huang K, Liu H, Ding H, Zhang L . CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma. Front Oncol. 2021; 11:687884. PMC: 8181422. DOI: 10.3389/fonc.2021.687884. View

14.

Liu G, Poon M, Zapala M, Temple W, Vo K, Matthay K . Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma. J Digit Imaging. 2022; 35(3):605-612. PMC: 9156639. DOI: 10.1007/s10278-022-00607-w. View

15.

Yang Z, Cai Y, Chen Y, Ai Z, Chen F, Wang H . A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma. Acad Radiol. 2022; 30(9):1946-1961. DOI: 10.1016/j.acra.2022.12.006. View

16.

Ghosh A, Yekeler E, Dalal D, Holroyd A, States L . Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN-amplification in neuroblastomas: preliminary results. Eur Radiol. 2022; 32(12):8453-8462. DOI: 10.1007/s00330-022-08750-2. View

17.

Gassenmaier S, Tsiflikas I, Fuchs J, Grimm R, Urla C, Esser M . Feasibility and possible value of quantitative semi-automated diffusion weighted imaging volumetry of neuroblastic tumors. Cancer Imaging. 2020; 20(1):89. PMC: 7745476. DOI: 10.1186/s40644-020-00366-3. View

18.

Kamiya A, Murayama S, Kamiya H, Yamashiro T, Oshiro Y, Tanaka N . Kurtosis and skewness assessments of solid lung nodule density histograms: differentiating malignant from benign nodules on CT. Jpn J Radiol. 2013; 32(1):14-21. DOI: 10.1007/s11604-013-0264-y. View

19.

Fujima N, Homma A, Harada T, Shimizu Y, Tha K, Kano S . The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies. Cancer Imaging. 2019; 19(1):5. PMC: 6360729. DOI: 10.1186/s40644-019-0193-9. View

20.

Goyal A, Razik A, Kandasamy D, Seth A, Das P, Ganeshan B . Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study. Abdom Radiol (NY). 2019; 44(10):3336-3349. DOI: 10.1007/s00261-019-02122-z. View