Radiomics in Wilms' tumor: from visualization to personalized prediction
DOI:
https://doi.org/10.15574/PS.2026.1(90).119124Keywords:
Wilms’ tumor, radiomics, recurrence prediction, childrenAbstract
Wilms’ tumor is the most common malignant renal tumor in children and remains a significant challenge in pediatric oncologic surgery despite high survival rates. The lack of standardized noninvasive methods for the quantitative assessment of imaging data limits the accurate prediction of disease course, risk of recurrence, and bilateral renal involvement.
Aim - to analyze current scientific evidence on the application of radiomics in pediatric Wilms’ tumor and to evaluate its diagnostic and prognostic potential for clinical practice.
Radiomics is based on the extraction of quantitative textural, morphological, and intensity features from computed tomography images, enabling an objective assessment of tumor heterogeneity. Recent studies demonstrate high effectiveness of radiomics in the differential diagnosis of Wilms’ tumor and other pediatric renal tumors, as well as in predicting treatment response, recurrence risk, and survival outcomes.
Conclusions. The integration of radiomic markers with clinical, radiological, and pathomorphological data facilitates the implementation of a personalized treatment approach, contributing to the optimization of surgical tactics and the improvement of long-term outcomes in children with nephroblastoma.
The authors declare no conflict of interest.
References
Ahmed HU, Arya M, Levitt G et al. (2007). Primary malignant non-Wilms' renal tumours in children. Part I. Lancet Oncol. 8(8): 730-737. https://doi.org/10.1016/S1470-2045(07)70241-3; PMid:17679083
Ahmed HU, Arya M, Levitt G et al. (2007). Treatment of primary malignant non-Wilms' renal tumours in children. Part II. Lancet Oncol. 8(9): 842-848. https://doi.org/10.1016/S1470-2045(07)70276-0; PMid:17765193
Alhashim M, Anan N, Tamal M et al. (2024). Optimization of Wilms tumour management using radiomics: a review. BJR Open. 2024;6(1):tzae034. Erratum in: BJR Open. 6(1): tzae044. https://doi.org/10.1093/bjro/tzae034; PMCid:PMC11525052
Arimura H, Soufi M, Kamezawa H et al. (2019). Radiomics with artificial intelligence for precision medicine in radiation therapy. J Radiat Res. 60(1): 150-157. https://doi.org/10.1093/jrr/rry077; PMid:30247662 PMCid:PMC6373667
Balis F, Green DM, Anderson C et al. (2021). Wilms tumor (nephroblastoma), Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 19(8): 945-977. https://doi.org/10.6004/jnccn.2021.0037; PMid:34416707
Bonaïti-Pellié C, Chompret A, Tournade MF et al. (1992). Genetics and epidemiology of Wilms' tumor: the French Wilms' tumor study. Med Pediatr Oncol. 20(4): 284-291. https://doi.org/10.1002/mpo.2950200404; PMid:1318995
Breslow NE, Olson J, Moksness J et al. (1996). Familial Wilms' tumor: a descriptive study. Med Pediatr Oncol. 27(5): 398-403. https://doi.org/10.1002/(SICI)1096-911X(199611)27:5<398::AID-MPO2>3.0.CO;2-H
Breslow N, Olshan A, Beckwith JB et al. (1994). Ethnic variation in the incidence, diagnosis, prognosis, and follow-up of children with Wilms' tumor. J Natl Cancer Inst. 86(1): 49-51. https://doi.org/10.1093/jnci/86.1.49; PMid:8271283
Brisse HJ, Blanc T, Schleiermacher G et al. (2017). Radiogenomics of neuroblastomas: relationships between imaging phenotypes, tumor genomic profile and survival. PLoS One. 12(9): e0185190. https://doi.org/10.1371/journal.pone.0185190; PMid:28945781 PMCid:PMC5612658
Chagaluka G, Paintsil V, Renner L et al. (2020). Improvement of overall survival in the Collaborative Wilms Tumour Africa Project. Pediatr Blood Cancer. 67(9): e28383. https://doi.org/10.1002/pbc.28383; PMid:32391983
Chen X, Wang H, Huang K et al. (2021). CT-based radiomics signature with machine learning predicts MYCN amplification in pediatric abdominal neuroblastoma. Front Oncol. 11: 687884. https://doi.org/10.3389/fonc.2021.687884; PMid:34109133 PMCid:PMC8181422
Deng Y, Wang H, He L. (2024). CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children. BMC Med Imaging. 24(1): 13. https://doi.org/10.1186/s12880-023-01184-2; PMid:38182986 PMCid:PMC10768092
Di Giannatale A, Di Paolo PL, Curione D et al. (2021). Radiogenomics prediction for MYCN amplification in neuroblastoma. Pediatr Blood Cancer. 68(10): e29110. https://doi.org/10.1002/pbc.29110; PMid:34003574
Grimm LJ, Mazurowski MA. (2020). Breast cancer radiogenomics: current status and future directions. Acad Radiol. 27(1): 39-46. https://doi.org/10.1016/j.acra.2019.09.012; PMid:31818385
Hild O, Berriet P, Nallet J et al. (2024). Automation of Wilms' tumor segmentation by artificial intelligence. Cancer Imaging. 24(1): 83. https://doi.org/10.1186/s40644-024-00729-0; PMid:38956718 PMCid:PMC11218149
Hol JA, Jewell R, Chowdhury T et al. (2021). Wilms tumour surveillance in at-risk children: literature review and recommendations from the SIOP-Europe Host Genome Working Group and SIOP Renal Tumour Study Group. Eur J Cancer. 153: 51-63. https://doi.org/10.1016/j.ejca.2021.05.014; PMid:34134020
Howlader N, Noone AM, Krapcho M et al. (2019). SEER Cancer Statistics Review (CSR) 1975-2016. Bethesda (MD): National Cancer Institute; Available from: https://seer.cancer.gov/csr/1975_2016/.
Kang W, Qiu X, Luo Y et al. (2023). Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med. 21(1): 598. https://doi.org/10.1186/s12967-023-04437-4; PMid:37674169 PMCid:PMC10481579
Khalak S, Nakonechnyi A. (2026). Erdheim-Chester disease mimicking Wilms tumor in a child: a diagnostic challenge. BMC Pediatr. 26(1): 352. https://doi.org/10.1186/s12887-026-06678-w; PMid:41820933 PMCid:PMC13093981
Kikinis R, Pieper SD, Vosburgh KG. (2014). 3D Slicer: a platform for subject-specific image analysis and clinical support. In: Jolesz FA, editor. Intraoperative imaging and image-guided therapy. New York: Springer: 277-289. https://doi.org/10.1007/978-1-4614-7657-3_19
Koska IO, Ozcan HN, Tan AA et al. (2024). Radiomics in differential diagnosis of Wilms tumor and neuroblastoma with adrenal location in children. Eur Radiol. 34(8): 5016-5027. https://doi.org/10.1007/s00330-024-10589-8; PMid:38311701 PMCid:PMC11255001
Lambin P, Leijenaar RTH, Deist TM et al. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 14(12): 749-762. https://doi.org/10.1038/nrclinonc.2017.141; PMid:28975929
Leslie SW, Sajjad H, Sharma S. (2018). Cancer, Wilms (Nephroblastoma). Treasure Island (FL): StatPearls Publishing.
Li W, Sun Y, Zhang G et al. (2024). Automated segmentation and volume prediction in pediatric Wilms' tumor CT using nnU-Net. BMC Pediatr. 24(1): 321. https://doi.org/10.1186/s12887-024-04775-2; PMid:38724944 PMCid:PMC11080230
Li Z, Li J, Li Z et al. (2023). A radiomics nomogram for preoperative prediction of nephron-sparing surgery in bilateral Wilms tumor. Quant Imaging Med Surg. 13(7): 4234-4244. https://doi.org/10.21037/qims-22-1129; PMid:37456324 PMCid:PMC10347350
Narod SA, Hawkins MM, Robertson CM et al. (1997). Congenital anomalies and childhood cancer in Great Britain. Am J Hum Genet. 60(3): 474-485.
National Cancer Institute. (2023). NCCR*Explorer: An interactive website for NCCR cancer statistics [Internet]. Bethesda (MD): NCI [cited 2024 Sep 22]. URL: https://nccrexplorer.ccdi.cancer.gov.
PDQ Pediatric Treatment Editorial Board. (2002). Wilms tumor and other childhood kidney tumors treatment (PDQ®): health professional version. PDQ Cancer Information Summaries. Bethesda (MD): National Cancer Institute; PMID: 26389282.
Pereira T, Silva F, Claro P et al. (2022). A random forest-based classifier for MYCN status prediction in neuroblastoma using CT images. Annu Int Conf IEEE Eng Med Biol Soc. 2022: 9871349. https://doi.org/10.1109/EMBC48229.2022.9871349; PMid:36086471
Rios P, Bauer H, Schleiermacher G et al. (2020). Environmental exposures related to parental habits in the perinatal period and the risk of Wilms' tumor in children. Cancer Epidemiol. 66: 101706. https://doi.org/10.1016/j.canep.2020.101706; PMid:32247207
Scott RH, Stiller CA, Walker L et al. (2006). Syndromes and constitutional chromosomal abnormalities associated with Wilms tumour. J Med Genet. 43(9): 705-715. https://doi.org/10.1136/jmg.2006.041723; PMid:16690728 PMCid:PMC2564568
Sharaby I, Alksas A, Nashat A et al. (2023). Prediction of Wilms' tumor susceptibility to preoperative chemotherapy using a computer-aided system. Diagnostics (Basel). 13(3): 486. https://doi.org/10.3390/diagnostics13030486; PMid:36766591 PMCid:PMC9914296
Silva F, Pereira T, Morgado J et al. (2021). EGFR assessment in lung cancer CT images using deep unsupervised transfer learning. IEEE Access. 9: 54690-54701. https://doi.org/10.1109/ACCESS.2021.3070701
Smith CP, Czarniecki M, Mehralivand S et al. (2019). Radiomics and radiogenomics of prostate cancer. Abdom Radiol (NY). 44(6): 2021-2034. https://doi.org/10.1007/s00261-018-1660-7; PMid:29926137
Traverso A, Wee L, Dekker A, Gillies R. (2018). Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys. 102(4): 1143-1158. https://doi.org/10.1016/j.ijrobp.2018.05.053; PMid:30170872 PMCid:PMC6690209
Wagner MW, Bilbily A, Beheshti M et al. (2021). Artificial intelligence and radiomics in pediatric molecular imaging. Methods. 188: 37-43. https://doi.org/10.1016/j.ymeth.2020.06.008; PMid:32544594
Ward ZJ, Yeh JM, Bhakta N, Frazier AL, Girardi F, Atun R. (2019). Global childhood cancer survival estimates and priority-setting: a simulation-based analysis. Lancet Oncol. 20(7): 972-983. https://doi.org/10.1016/S1470-2045(19)30273-6; PMid:31129029
Winther JF, Sankila R, Boice JD Jr et al. (2001). Cancer in siblings of children with cancer in the Nordic countries: a population-based cohort study. Lancet. 358(9283): 711-717. https://doi.org/10.1016/S0140-6736(01)05838-X; PMid:11551577
Wu H, Wu C, Zheng H et al. (2021). Radiogenomics of neuroblastoma: CT-based radiomics signature for predicting MYCN amplification. Eur Radiol. 31(8): 5719-5728. https://doi.org/10.1007/s00330-020-07246-1; PMid:33118047
Zhang M, Ye Z, Yuan E et al. (2024). Imaging-based deep learning in kidney diseases: recent progress and future prospects. Insights Imaging. 15(1): 50. https://doi.org/10.1186/s13244-024-01636-5; PMid:38360904 PMCid:PMC10869329
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Paediatric Surgery (Ukraine)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The policy of the Journal “PAEDIATRIC SURGERY. UKRAINE” is compatible with the vast majority of funders' of open access and self-archiving policies. The journal provides immediate open access route being convinced that everyone – not only scientists - can benefit from research results, and publishes articles exclusively under open access distribution, with a Creative Commons Attribution-Noncommercial 4.0 international license(СС BY-NC).
Authors transfer the copyright to the Journal “PAEDIATRIC SURGERY.UKRAINE” when the manuscript is accepted for publication. Authors declare that this manuscript has not been published nor is under simultaneous consideration for publication elsewhere. After publication, the articles become freely available on-line to the public.
Readers have the right to use, distribute, and reproduce articles in any medium, provided the articles and the journal are properly cited.
The use of published materials for commercial purposes is strongly prohibited.
