562-04-001 ISMRM Abstract

Multiparametric MRI Radiomics for Noninvasive Risk Stratification in Pediatric Neuroblastoma

Primary: Analysis Methods - Radiomics

Secondary: Pediatrics - Cancer

562-04-001 · AI/DL in Pediatrics · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row C

Keywords: Radiomics Machine learning Neuroblastoma

Accepted

Matthias Anders¹, Federico Mollica ¹, Tom Meyer², Reda Tahan¹, Hedwig Deubzer³, Simon Veldhoen¹, Corona Metz¹

¹Department of Pediatric Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

²Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

³Department of Pediatric Hematology and Oncology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

Presenting Author: Federico Mollica

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References

1. Ponzoni, M., Bachetti, T., Corrias, M. V., et al. 2022. Recent advances in the developmental origin of neuroblastoma: an overview. Journal of Experimental & Clinical Cancer Research, 41(1), 92. DOI: 10.1186/s13046-022-02281-w [doi]

2. Irwin, M. S., Goldsmith, K. C. 2024. Current and emerging biomarkers: impact on risk stratification for neuroblastoma. Journal of the National Comprehensive Cancer Network, 22(6). DOI: 10.6004/jnccn.2024.7051 [doi]

3. Gillies, R. J., Kinahan, P. E., Hricak, H. 2016. Radiomics: images are more than pictures, they are data. Radiology, 278(2), 563-577. DOI: 10.1148/radiol.2015151169 [doi]

4. Zwanenburg, A., Vallières, M., Abdalah, M. A., et al. 2020. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2), 328-338. DOI: 10.1148/radiol.2020191145 [doi]

5. Kim, J., Choi, Y. H., Yoon, H., et al. 2024. Prediction of High-Risk Neuroblastoma Among Neuroblastic Tumors Using Radiomics Features Derived from Magnetic Resonance Imaging: A Pilot Study. Yonsei Medical Journal, 65(5), 293-301. DOI: 10.3349/ymj.2023.0192 [doi]

6. Mylona, E., Kalantzopoulos, C.Ν., Regge, D., et al. 2024. Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences. Insights into Imaging, 15(1), 265. DOI: 10.1186/s13244-024-01783-9 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/562-04-001