临床参数结合CT放射组学特征预测儿童神经母细胞瘤长期疗效的研究进展
Research Advances in Predicting Long-Term Efficacy of Pediatric Neuroblastoma Using Clinical Parameters Combined with CT Radiomics Features
DOI: 10.12677/acm.2025.1592538, PDF,   
作者: 李金融, 徐 晔*:重庆医科大学附属儿童医院儿科学院放射科,儿童发育疾病研究教育部重点实验室,儿童发育重大疾病国家国际科技合作基地,结构性出生缺陷与器官修复重建重庆市重点实验室,重庆
关键词: 神经母细胞瘤放射组学增强CT预后Neuroblastoma Radiomics Enhanced CT Prognosis
摘要: 神经母细胞瘤(Neuroblastoma, NB)是儿童最常见的实体性恶性肿瘤之一,预后存在较明显的个体间差异。治疗前,准确预测NB患儿的长期疗效对制定个体化的治疗方案至关重要,既往文献对影响NB疗效的各种因素均做了较深入研究。然而,鉴于恶性肿瘤生物学特征的复杂性,独立地研究个别和少数几个特征难以充分反映NB的危险性和治疗的效果。近年,放射组学作为新兴的影像分析技术受到普遍重视,该技术通过提取医学影像中的大量定量特征,为肿瘤的诊断、治疗和预后提供了新的思路。本文就近5年文献对NB的预后评估及放射组学在NB诊断的应用研究进展予以综述。
Abstract: Neuroblastoma (NB) is one of the most common solid malignancies in children, and its prognosis varies considerably among individual patients. Therefore, accurately predicting the long-term outcomes of NB children before treatment implementation is crucial for formulating individualized therapeutic regimens. The literature has conducted in-depth research on various factors influencing NB treatment efficacy. However, given the complexity of the biological characteristics of malignant tumors, studying individual or a few features in isolation is insufficient to fully reflect the risk profile of NB and the effectiveness of treatment. In recent years, radiomics, as an emerging medical image analysis technique, has provided new insights into tumor diagnosis, treatment, and prognosis by extracting large numbers of quantitative features from medical images. This article provides a review of research progress over the past five years concerning prognostic assessment in NB and the application of radiomics in NB diagnosis.
文章引用:李金融, 徐晔. 临床参数结合CT放射组学特征预测儿童神经母细胞瘤长期疗效的研究进展[J]. 临床医学进展, 2025, 15(9): 645-652. https://doi.org/10.12677/acm.2025.1592538

参考文献

[1] Irwin, M.S. and Park, J.R. (2015) Neuroblastoma: Paradigm for precision Medicine. Pediatric Clinics of North America, 62, 225-256. [Google Scholar] [CrossRef] [PubMed]
[2] Cheng, H., Zhang, L., Yang, S., Ren, Q., Chang, S., Jin, Y., et al. (2023) Integration of Clinical Characteristics and Molecular Signatures of the Tumor Microenvironment to Predict the Prognosis of Neuroblastoma. Journal of Molecular Medicine, 101, 1421-1436. [Google Scholar] [CrossRef] [PubMed]
[3] Moreno, L., Guo, D., Irwin, M.S., Berthold, F., Hogarty, M., Kamijo, T., et al. (2020) A Nomogram of Clinical and Biologic Factors to Predict Survival in Children Newly Diagnosed with High‐Risk Neuroblastoma: An International Neuroblastoma Risk Group Project. Pediatric Blood & Cancer, 68, e28794. [Google Scholar] [CrossRef] [PubMed]
[4] Liu, G., Poon, M., Zapala, M.A., Temple, W.C., Vo, K.T., Matthay, K.K., et al. (2022) Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma. Journal of Digital Imaging, 35, 605-612. [Google Scholar] [CrossRef] [PubMed]
[5] Okawa, S. and Saika, K. (2022) International Variations in Neuroblastoma Incidence in Children and Adolescents. Japanese Journal of Clinical Oncology, 52, 656-658. [Google Scholar] [CrossRef] [PubMed]
[6] Jiang, M., Stanke, J. and Lahti, J.M. (2011) The Connections between Neural Crest Development and Neuroblastoma. Current Topics in Developmental Biology, 94, 77-127. [Google Scholar] [CrossRef] [PubMed]
[7] Muntean, L., Falup-Pecurariu, O., Voda, D. and Papa, A. (2022) Primary Paratesticular Neuroblastoma in a Newborn—Case Presentation and Literature Review. Pediatrics & Neonatology, 63, 315-316. [Google Scholar] [CrossRef] [PubMed]
[8] Hong, S.H., Wietlisbach, L., Galli, S., Mahajan, A., Zhu, S., Tilan, J., et al. (2017) Abstract 1940: Prenatal Stress Increases Malignancy of Neuroblastoma Tumors in TH-MYCN Animal Model. Cancer Research, 77, 1940-1940. [Google Scholar] [CrossRef
[9] Brodeur, G.M., Minturn, J.E., Ho, R., Simpson, A.M., Iyer, R., Varela, C.R., et al. (2009) TRK Receptor Expression and Inhibition in Neuroblastomas. Clinical Cancer Research, 15, 3244-3250. [Google Scholar] [CrossRef] [PubMed]
[10] Longo, L., Panza, E., Schena, F., Seri, M., Devoto, M., Romeo, G., et al. (2007) Genetic Predisposition to Familial Neuroblastoma: Identification of Two Novel Genomic Regions at 2p and 12p. Human Heredity, 63, 205-211. [Google Scholar] [CrossRef] [PubMed]
[11] Mossé, Y.P., Laudenslager, M., Longo, L., Cole, K.A., Wood, A., Attiyeh, E.F., et al. (2008) Identification of ALK as a Major Familial Neuroblastoma Predisposition Gene. Nature, 455, 930-935. [Google Scholar] [CrossRef] [PubMed]
[12] Schumacher‐Kuckelkorn, R., Volland, R., Gradehandt, A., Hero, B., Simon, T. and Berthold, F. (2016) Lack of Immunocytological GD2 Expression on Neuroblastoma Cells in Bone Marrow at Diagnosis, during Treatment, and at Recurrence. Pediatric Blood & Cancer, 64, 46-56. [Google Scholar] [CrossRef] [PubMed]
[13] 中国抗癌协会小儿肿瘤专业委员会, 中华医学会小儿外科学分会肿瘤学组. 儿童神经母细胞瘤诊疗专家共识CCCG-NB-2021方案[J]. 中华小儿外科杂志, 2022, 43(7): 588-598.
[14] Maris, J.M., Hogarty, M.D., Bagatell, R. and Cohn, S.L. (2007) Neuroblastoma. The Lancet, 369, 2106-2120. [Google Scholar] [CrossRef] [PubMed]
[15] Caron, H.N. (2010) Are Thoracic Neuroblastomas Really Different? Pediatric Blood & Cancer, 54, 867-867. [Google Scholar] [CrossRef] [PubMed]
[16] McGranahan, N. and Swanton, C. (2017) Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell, 168, 613-628. [Google Scholar] [CrossRef] [PubMed]
[17] Johnsen, J.I., Dyberg, C. and Wickström, M. (2019) Neuroblastoma—A Neural Crest Derived Embryonal Malignancy. Frontiers in Molecular Neuroscience, 12, Article 9. [Google Scholar] [CrossRef] [PubMed]
[18] Nuchtern, J.G., London, W.B., Barnewolt, C.E., Naranjo, A., McGrady, P.W., Geiger, J.D., et al. (2012) A Prospective Study of Expectant Observation as Primary Therapy for Neuroblastoma in Young Infants. Annals of Surgery, 256, 573-580. [Google Scholar] [CrossRef] [PubMed]
[19] Whittle, S.B., Smith, V., Doherty, E., Zhao, S., McCarty, S. and Zage, P.E. (2017) Overview and Recent Advances in the Treatment of Neuroblastoma. Expert Review of Anticancer Therapy, 17, 369-386. [Google Scholar] [CrossRef] [PubMed]
[20] Pinto, N.R., Applebaum, M.A., Volchenboum, S.L., Matthay, K.K., London, W.B., Ambros, P.F., et al. (2015) Advances in Risk Classification and Treatment Strategies for Neuroblastoma. Journal of Clinical Oncology, 33, 3008-3017. [Google Scholar] [CrossRef] [PubMed]
[21] Jamal-Hanjani, M., Quezada, S.A., Larkin, J. and Swanton, C. (2015) Translational Implications of Tumor Heterogeneity. Clinical Cancer Research, 21, 1258-1266. [Google Scholar] [CrossRef] [PubMed]
[22] Ramón y Cajal, S., Sesé, M., Capdevila, C., Aasen, T., De Mattos-Arruda, L., Diaz-Cano, S.J., et al. (2020) Clinical Implications of Intratumor Heterogeneity: Challenges and Opportunities. Journal of Molecular Medicine, 98, 161-177. [Google Scholar] [CrossRef] [PubMed]
[23] Teshiba, R., Kawano, S., Wang, L.L., He, L., Naranjo, A., London, W.B., et al. (2014) Age-Dependent Prognostic Effect by Mitosis-Karyorrhexis Index in Neuroblastoma: A Report from the Children’s Oncology Group. Pediatric and Developmental Pathology, 17, 441-449. [Google Scholar] [CrossRef] [PubMed]
[24] Monclair, T., Brodeur, G.M., Ambros, P.F., Brisse, H.J., Cecchetto, G., Holmes, K., et al. (2009) The International Neuroblastoma Risk Group (INRG) Staging System: An INRG Task Force Report. Journal of Clinical Oncology, 27, 298-303. [Google Scholar] [CrossRef] [PubMed]
[25] Cohn, S.L., Pearson, A.D.J., London, W.B., Monclair, T., Ambros, P.F., Brodeur, G.M., et al. (2009) The International Neuroblastoma Risk Group (INRG) Classification System: An INRG Task Force Report. Journal of Clinical Oncology, 27, 289-297. [Google Scholar] [CrossRef] [PubMed]
[26] Campbell, K., Kao, P., Naranjo, A., Kamijo, T., Ramanujachar, R., London, W.B., et al. (2022) Clinical and Biological Features Prognostic of Survival after Relapse or Progression of INRGSS Stage MS Pattern Neuroblastoma: A Report from the International Neuroblastoma Risk Group (INRG) Project. Pediatric Blood & Cancer, 70, e30054. [Google Scholar] [CrossRef] [PubMed]
[27] Chang, H., Liu, Y., Lu, M., Jou, S., Yang, Y., Lin, D., et al. (2016) A Multidisciplinary Team Care Approach Improves Outcomes in High-Risk Pediatric Neuroblastoma Patients. Oncotarget, 8, 4360-4372. [Google Scholar] [CrossRef] [PubMed]
[28] Spix, C., Pastore, G., Sankila, R., Stiller, C.A. and Steliarova-Foucher, E. (2006) Neuroblastoma Incidence and Survival in European Children (1978-1997): Report from the Automated Childhood Cancer Information System Project. European Journal of Cancer, 42, 2081-2091. [Google Scholar] [CrossRef] [PubMed]
[29] Brodeur, G.M. (2003) Neuroblastoma: Biological Insights into a Clinical Enigma. Nature Reviews Cancer, 3, 203-216. [Google Scholar] [CrossRef] [PubMed]
[30] Brodeur, G.M., Fong, C.T., Morita, M., Griffith, R., Hayes, F.A. and Seeger, R.C. (1988) Molecular Analysis and Clinical Significance of N-Myc Amplification and Chromosome 1p Monosomy in Human Neuroblastomas. Progress in Clinical and Biological Research, 271, 3-15.
[31] Brodeur, G.M., Seeger, R.C., Barrett, A., Berthold, F., Castleberry, R.P., D’Angio, G., et al. (1988) International Criteria for Diagnosis, Staging, and Response to Treatment in Patients with Neuroblastoma. Journal of Clinical Oncology, 6, 1874-1881. [Google Scholar] [CrossRef] [PubMed]
[32] Brodeur, G.M., Seeger, R.C., Schwab, M., Varmus, H.E. and Bishop, J.M. (1984) Amplification of N-myc in Untreated Human Neuroblastomas Correlates with Advanced Disease Stage. Science, 224, 1121-1124. [Google Scholar] [CrossRef] [PubMed]
[33] Cheung, N.V., Ostrovnaya, I., Kuk, D. and Cheung, I.Y. (2015) Bone Marrow Minimal Residual Disease Was an Early Response Marker and a Consistent Independent Predictor of Survival after Anti-GD2 Immunotherapy. Journal of Clinical Oncology, 33, 755-763. [Google Scholar] [CrossRef] [PubMed]
[34] Smith, V. and Foster, J. (2018) High-Risk Neuroblastoma Treatment Review. Children, 5, Article 114. [Google Scholar] [CrossRef] [PubMed]
[35] Tolbert, V.P. and Matthay, K.K. (2018) Neuroblastoma: Clinical and Biological Approach to Risk Stratification and Treatment. Cell and Tissue Research, 372, 195-209. [Google Scholar] [CrossRef] [PubMed]
[36] Depuydt, P., Boeva, V., Hocking, T.D., Cannoodt, R., Ambros, I.M., Ambros, P.F., et al. (2018) Genomic Amplifications and Distal 6q Loss: Novel Markers for Poor Survival in High-Risk Neuroblastoma Patients. JNCI: Journal of the National Cancer Institute, 110, 1084-1093. [Google Scholar] [CrossRef] [PubMed]
[37] Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G.P.M., Granton, P., et al. (2012) Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer, 48, 441-446. [Google Scholar] [CrossRef] [PubMed]
[38] Zhang, X., Zhang, Y., Zhang, G., Qiu, X., Tan, W., Yin, X., et al. (2022) Deep Learning with Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Frontiers in Oncology, 12, Article 773840. [Google Scholar] [CrossRef] [PubMed]
[39] Sarker, I.H. (2021) Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2, Article No. 420. [Google Scholar] [CrossRef] [PubMed]
[40] Liu, S., Sun, W., Yang, S., Duan, L., Huang, C., Xu, J., et al. (2021) Deep Learning Radiomic Nomogram to Predict Recurrence in Soft Tissue Sarcoma: A Multi-Institutional Study. European Radiology, 32, 793-805. [Google Scholar] [CrossRef] [PubMed]
[41] Kirienko, M., Sollini, M., Corbetta, M., Voulaz, E., Gozzi, N., Interlenghi, M., et al. (2021) Radiomics and Gene Expression Profile to Characterise the Disease and Predict Outcome in Patients with Lung Cancer. European Journal of Nuclear Medicine and Molecular Imaging, 48, 3643-3655. [Google Scholar] [CrossRef] [PubMed]
[42] Eertink, J.J., van de Brug, T., Wiegers, S.E., Zwezerijnen, G.J.C., Pfaehler, E.A.G., Lugtenburg, P.J., et al. (2021) 18F-FDG PET Baseline Radiomics Features Improve the Prediction of Treatment Outcome in Diffuse Large B-Cell Lymphoma. European Journal of Nuclear Medicine and Molecular Imaging, 49, 932-942. [Google Scholar] [CrossRef] [PubMed]
[43] Feng, L., Qian, L., Yang, S., Ren, Q., Zhang, S., Qin, H., et al. (2022) Clinical Parameters Combined with Radiomics Features of PET/CT Can Predict Recurrence in Patients with High-Risk Pediatric Neuroblastoma. BMC Medical Imaging, 22, Article No. 102. [Google Scholar] [CrossRef] [PubMed]
[44] 苏勇, 赵充, 谢传淼, 等. 鼻咽癌咽后淋巴结转移的CT、MRI和PET-CT诊断的对比研究[J]. 癌症, 2006, 25(5): 521-525. [Google Scholar] [CrossRef
[45] Wang, H., Chen, X., Yu, W., Xie, M., Zhang, L., Ding, H., et al. (2023) Whole-Tumor Radiomics Analysis of T2-Weighted Imaging in Differentiating Neuroblastoma from Ganglioneuroblastoma/Ganglioneuroma in Children: An Exploratory Study. Abdominal Radiology, 48, 1372-1382. [Google Scholar] [CrossRef] [PubMed]
[46] Callahan, M.J., MacDougall, R.D., Bixby, S.D., Voss, S.D., Robertson, R.L. and Cravero, J.P. (2017) Ionizing Radiation from Computed Tomography versus Anesthesia for Magnetic Resonance Imaging in Infants and Children: Patient Safety Considerations. Pediatric Radiology, 48, 21-30. [Google Scholar] [CrossRef] [PubMed]
[47] Burnand, K., Barone, G., McHugh, K. and Cross, K. (2019) Preoperative Computed Tomography Scanning for Abdominal Neuroblastomas Is Superior to Magnetic Resonance Imaging for Safe Surgical Planning. Pediatric Blood & Cancer, 66, e27955. [Google Scholar] [CrossRef] [PubMed]
[48] Wang, H., Xie, M., Chen, X., Zhu, J., Zhang, L., Ding, H., et al. (2023) Radiomics Analysis of Contrast-Enhanced Computed Tomography in Predicting the International Neuroblastoma Pathology Classification in Neuroblastoma. Insights into Imaging, 14, Article No. 106. [Google Scholar] [CrossRef] [PubMed]
[49] Zhao, L., Shi, L., Huang, S., Cai, T., Guo, W., Gao, X., et al. (2023) Identification and Validation of Radiomic Features from Computed Tomography for Preoperative Classification of Neuroblastic Tumors in Children. BMC Pediatrics, 23, Article No. 262. [Google Scholar] [CrossRef] [PubMed]
[50] Wu, H., Wu, C., Zheng, H., Wang, L., Guan, W., Duan, S., et al. (2020) Radiogenomics of Neuroblastoma in Pediatric Patients: CT-Based Radiomics Signature in Predicting MYCN Amplification. European Radiology, 31, 3080-3089. [Google Scholar] [CrossRef] [PubMed]
[51] Wang, H., Xie, M., Chen, X., Zhu, J., Ding, H., Zhang, L., et al. (2023) Development and Validation of a CT‐Based Radiomics Signature for Identifying High‐Risk Neuroblastomas under the Revised Children’s Oncology Group Classification System. Pediatric Blood & Cancer, 70, e30280. [Google Scholar] [CrossRef] [PubMed]
[52] Zhang, Y., Yang, Y., Ning, G., Wu, X., Yang, G. and Li, Y. (2023) Contrast Computed Tomography-Based Radiomics Is Correlation with COG Risk Stratification of Neuroblastoma. Abdominal Radiology, 48, 2111-2121. [Google Scholar] [CrossRef] [PubMed]