影像组学在儿童横纹肌肉瘤诊疗中的研究进展
Advances in Radiomics for the Diagnosis and Treatment of Pediatric Rhabdomyosarcoma
DOI: 10.12677/acm.2026.163769, PDF,   
作者: 罗皓月, 何 玲*:重庆医科大学附属儿童医院放射科,国家儿童健康与疾病临床医学研究中心,儿童发育疾病研究教育部重点实验室,儿科学重庆市重点实验室,重庆
关键词: 影像组学儿童横纹肌肉瘤预后预测疗效评估Radiomics Pediatric Rhabdomyosarcoma Prognosis Prediction Treatment Response Assessment
摘要: 儿童横纹肌肉瘤(Rhabdomyosarcoma, RMS)是儿童期最常见的软组织肉瘤,其异质性强、预后差异大,精准诊疗仍是临床面临的重大挑战。传统影像学检查(如MRI、CT)主要提供肿瘤形态学信息,对其内在的异质性、治疗反应早期评估及预后预测能力有限。影像组学作为一种新兴的定量分析方法,能提供RMS的精准诊疗。
Abstract: Pediatric Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children, characterized by significant heterogeneity and variable prognosis. Precision diagnosis and treatment remain a major clinical challenge. Conventional imaging techniques (e.g., MRI, CT) primarily provide morphological information about the tumor, with limited capability to assess its intrinsic heterogeneity, evaluate early treatment response, and predict prognosis. As an emerging quantitative analysis method, radiomics holds promise for enabling precise diagnosis and treatment of RMS.
文章引用:罗皓月, 何玲. 影像组学在儿童横纹肌肉瘤诊疗中的研究进展[J]. 临床医学进展, 2026, 16(3): 114-118. https://doi.org/10.12677/acm.2026.163769

参考文献

[1] McCarville, M.B., Spunt, S.L. and Pappo, A.S. (2001) Rhabdomyosarcoma in Pediatric Patients: The Good, the Bad, and the Unusual. American Journal of Roentgenology, 176, 1563-1569. [Google Scholar] [CrossRef] [PubMed]
[2] Donaldson, S.S. and Belli, J.A. (1984) A Rational Clinical Staging System for Childhood Rhabdomyosarcoma. Journal of Clinical Oncology, 2, 135-139. [Google Scholar] [CrossRef] [PubMed]
[3] Rudzinski, E.R., Anderson, J.R., Hawkins, D.S., Skapek, S.X., Parham, D.M. and Teot, L.A. (2015) The World Health Organization Classification of Skeletal Muscle Tumors in Pediatric Rhabdomyosarcoma: A Report from the Children's Oncology Group. Archives of Pathology & Laboratory Medicine, 139, 1281-1287. [Google Scholar] [CrossRef] [PubMed]
[4] Amer, K.M., Thomson, J.E., Congiusta, D., Dobitsch, A., Chaudhry, A., Li, M., et al. (2019) Epidemiology, Incidence, and Survival of Rhabdomyosarcoma Subtypes: SEER and ICES Database Analysis. Journal of Orthopaedic Research, 37, 2226-2230. [Google Scholar] [CrossRef] [PubMed]
[5] Tian, L., Cai, Y., Li, X. and Cai, J. (2022) Computed Tomography (CT) Features of Pelvic Rhabdomyosarcoma (RMS) in Children. Current Medical Imaging Formerly Current Medical Imaging Reviews, 18, 299-304. [Google Scholar] [CrossRef] [PubMed]
[6] de Vries, I.S.A., van Ewijk, R., Adriaansen, L.M.E., Bohte, A.E., Braat, A.J.A.T., Fajardo, R.D., et al. (2023) Imaging in Rhabdomyosarcoma: A Patient Journey. Pediatric Radiology, 53, 788-812. [Google Scholar] [CrossRef] [PubMed]
[7] van Ewijk, R., Vaarwerk, B., Breunis, W.B., Schoot, R.A., ter Horst, S.A.J., van Rijn, R.R., et al. (2021) The Value of Early Tumor Size Response to Chemotherapy in Pediatric Rhabdomyosarcoma. Cancers, 13, Article 510. [Google Scholar] [CrossRef] [PubMed]
[8] Avanzo, M., Wei, L., Stancanello, J., Vallières, M., Rao, A., Morin, O., et al. (2020) Machine and Deep Learning Methods for Radiomics. Medical Physics, 47, e185-e202. [Google Scholar] [CrossRef] [PubMed]
[9] Sheng, J., Li, T., Zhang, H., Xu, H., Cai, X., Xu, R., et al. (2023) CT and MR Imaging Features of Soft Tissue Rhabdoid Tumor: Compared with Rhabdomyosarcoma in Children. Frontiers in Pediatrics, 11, Article 1199444. [Google Scholar] [CrossRef] [PubMed]
[10] Sheng, J., Li, T., Xu, H., et al. (2024) Evaluation of Clinical and Imaging Features for Differentiating Rhabdomyosarcoma from Neuroblastoma in Pediatric Soft Tissue. Frontiers in Oncology, 14, Article 1289532.
[11] Chen, X., Huang, Y., He, L., Zhang, T., Zhang, L. and Ding, H. (2020) CT-Based Radiomics to Differentiate Pelvic Rhabdomyosarcoma from Yolk Sac Tumors in Children. Frontiers in Oncology, 10, Article 584272. [Google Scholar] [CrossRef] [PubMed]
[12] Osama, A., Karam, A., Atef, A., Arafat, M., Afifi, R.W., Mokhtar, M., et al. (2025) Integrative Multi-Omics Profiling of Rhabdomyosarcoma Subtypes Reveals Distinct Molecular Pathways and Biomarker Signatures. Cells, 14, Article 1115. [Google Scholar] [CrossRef] [PubMed]
[13] Orsatti, G., Zucchetta, P., Varotto, A., Crimì, F., Weber, M., Cecchin, D., et al. (2021) Volumetric Histograms-Based Analysis of Apparent Diffusion Coefficients and Standard Uptake Values for the Assessment of Pediatric Sarcoma at Staging: Preliminary Results of a PET/MRI Study. La radiologia medica, 126, 878-885. [Google Scholar] [CrossRef] [PubMed]
[14] Gowin, E., Jończyk-Potoczna, K., Sosnowska-Sienkiewicz, P., Belen Larque, A., Kurzawa, P. and Januszkiewicz-Lewandowska, D. (2021) Semi-Automatic Volumetric and Standard Three-Dimensional Measurements for Primary Tumor Evaluation and Response to Treatment Assessment in Pediatric Rhabdomyosarcoma. Journal of Personalized Medicine, 11, Article 717. [Google Scholar] [CrossRef] [PubMed]
[15] Ghosh, A., Li, H., Towbin, A., Turpin, B. and Trout, A. (2025) T2-Weighted MRI Radiomics for the Prediction of Pediatric and Young Adult Rhabdomyosarcoma Alveolar Subtype and Distant Metastasis: A Pilot Study. Pediatric Radiology, 55, 1149-1161. [Google Scholar] [CrossRef] [PubMed]
[16] Giraudo, C., Fichera, G., Stramare, R., Bisogno, G., Motta, R., Evangelista, L., et al. (2022) Radiomic Features as Biomarkers of Soft Tissue Paediatric Sarcomas: Preliminary Results of a PET/MR Study. Radiology and Oncology, 56, 138-141. [Google Scholar] [CrossRef] [PubMed]
[17] Rhee, D.S., Rodeberg, D.A., Baertschiger, R.M., Aldrink, J.H., Lautz, T.B., Grant, C., et al. (2020) Update on Pediatric Rhabdomyosarcoma: A Report from the APSA Cancer Committee. Journal of Pediatric Surgery, 55, 1987-1995. [Google Scholar] [CrossRef] [PubMed]
[18] Mirghaderi, P., Valizadeh, P., Haseli, S., Kim, H.S., Azhideh, A., Nyflot, M.J., et al. (2025) Performance of Radiomics and Deep Learning Models in Predicting Distant Metastases in Soft Tissue Sarcomas: A Systematic Review and Meta-Analysis. Academic Radiology, 32, 6773-6795. [Google Scholar] [CrossRef] [PubMed]
[19] Zhang, G., Peng, Y., Su, Y., Mei, L., Fang, J., Liu, Y., et al. (2026) Intratumoral and Peritumoral Radiomics for the Pretreatment Prediction of Response to Neoadjuvant Chemotherapy in Rhabdomyosarcoma: A Multicenter Retrospective Cohort Study. Insights into Imaging, 17, Article No. 3. [Google Scholar] [CrossRef
[20] Ghosh, A., Li, H., Towbin, A.J., Turpin, B.K. and Trout, A.T. (2024) Histogram Analysis of Apparent Diffusion Coefficient Maps Provides Genotypic and Pretreatment Phenotypic Information in Pediatric and Young Adult Rhabdomyosarcoma. Academic Radiology, 31, 2550-2561. [Google Scholar] [CrossRef] [PubMed]
[21] van Ewijk, R., Chatziantoniou, C., Adams, M., Bertolini, P., Bisogno, G., Bouhamama, A., et al. (2023) Quantitative Diffusion-Weighted MRI Response Assessment in Rhabdomyosarcoma: An International Retrospective Study on Behalf of the European Paediatric Soft Tissue Sarcoma Study Group Imaging Committee. Pediatric Radiology, 53, 2539-2551. [Google Scholar] [CrossRef] [PubMed]
[22] Zhang, G., Wang, S., Su, Y., Peng, Y., Fang, J., Liu, Y., et al. (2025) MRI Based Intratumoral-Peritumoral Habitat Radiomics for Prediction of Overall Survival in Rhabdomyosarcoma: A Multicenter Study. Academic Radiology, 32, 7407-7418. [Google Scholar] [CrossRef