, All -Recherche Google, Cancer facts and figures, 2015.

A. A. Ahmed, L. Zhang, N. Reddivalla, and M. Hetherington, Neuroblastoma in children: Update on clinicopathologic and genetic prognostic factors, Pediatr Hematol Oncol. avr, vol.34, issue.3, pp.165-85, 2017.

S. L. Cohn, A. Pearson, W. B. London, T. Monclair, P. F. Ambros et al., The International Neuroblastoma Risk Group (INRG) classification system: an INRG Task Force report, J Clin Oncol Off J Am Soc Clin Oncol. 10 janv, vol.27, issue.2, pp.289-97, 2009.

N. Cheung, M. A. Dyer, and . Neuroblastoma, Cancer Genomics, and Immunotherapy, vol.13, issue.6, pp.397-411, 2013.

S. B. Whittle, V. Smith, E. Doherty, S. Zhao, S. Mccarty et al., Overview and recent advances in the treatment of neuroblastoma, Expert Rev Anticancer Ther. 3 avr, vol.17, issue.4, pp.369-86, 2017.

E. Sokol and A. V. Desai, The Evolution of Risk Classification for Neuroblastoma, vol.11

W. B. London, R. P. Castleberry, K. K. Matthay, A. T. Look, R. C. Seeger et al., Evidence for an age cutoff greater than 365 days for neuroblastoma risk group stratification in the Children's Oncology Group, J Clin Oncol Off J Am Soc Clin Oncol. 20 sept, vol.23, issue.27, pp.6459-65, 2005.

M. L. Schmidt, A. Lal, R. C. Seeger, J. M. Maris, H. Shimada et al., Favorable prognosis for patients 12 to 18 months of age with stage 4 nonamplified MYCN neuroblastoma: a Children's Cancer Group Study, J Clin Oncol Off J Am Soc Clin Oncol. 20 sept, vol.23, issue.27, pp.6474-80, 2005.

D. Valteau-couanet, G. Schleiermacher, S. Sarnacki, and C. Pasqualini,

, Bull Cancer (Paris), vol.105, pp.918-942, 2018.

D. A. Morgenstern, U. Pötschger, L. Moreno, V. Papadakis, C. Owens et al., Risk stratification of high-risk metastatic neuroblastoma: A report from the HR-NBL-1/SIOPEN study, Pediatr Blood Cancer, vol.65, issue.11, p.27363, 2018.

K. K. Matthay, C. P. Reynolds, R. C. Seeger, H. Shimada, E. S. Adkins et al.,

, Long-term results for children with high-risk neuroblastoma treated on a randomized trial of myeloablative therapy followed by 13-cis-retinoic acid: a children's oncology group study, J Clin Oncol Off J Am Soc Clin Oncol. 1 mars, vol.27, issue.7, pp.1007-1020, 2009.

R. Ladenstein, U. Pötschger, A. Pearson, P. Brock, R. Luksch et al., Busulfan and melphalan versus carboplatin, etoposide, and melphalan as high-dose chemotherapy for high-risk neuroblastoma (HR-NBL1/SIOPEN): an international, randomised, multi-arm, open-label, phase 3 trial, Lancet Oncol, vol.18, issue.4, pp.500-514, 2017.

W. B. London, R. Bagatell, B. J. Weigel, E. Fox, D. Guo et al., Historical time to disease progression and progression-free survival in patients with recurrent/refractory neuroblastoma treated in the modern era on Children's Oncology Group early-phase trials, Cancer. 15 déc, vol.123, issue.24, pp.4914-4937, 2017.

N. O. Basta, G. C. Halliday, G. Makin, J. Birch, R. Feltbower et al., Factors associated with recurrence and survival length following relapse in patients with neuroblastoma, Br J Cancer, vol.115, issue.9, pp.1048-57, 2016.

H. E. Skipper, F. M. Schabel, W. S. Wilcox, . Experimental, . X. Of-potential-anticancer-agents et al., Cancer Chemother Rep. févr, vol.35, pp.1-111, 1964.

P. M. Altrock, L. L. Liu, and F. Michor, The mathematics of cancer: integrating quantitative models, Nat Rev Cancer. déc, vol.15, issue.12, pp.730-775, 2015.

S. Benzekry, E. Pasquier, D. Barbolosi, B. Lacarelle, F. Barlési et al., Metronomic reloaded: Theoretical models bringing chemotherapy into the era of precision medicine, Semin Cancer Biol. déc, vol.35, pp.53-61, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01195547

D. Barbolosi, J. Ciccolini, B. Lacarelle, F. Barlési, and N. André, Computational oncology--mathematical modelling of drug regimens for precision medicine, Nat Rev Clin Oncol. avr, vol.13, issue.4, pp.242-54, 2016.

, Multiscale Cancer Modeling, 2019.

A. Rajkomar, J. Dean, and I. Kohane, Machine Learning in Medicine, N Engl J Med. 4 avr, vol.380, issue.14, pp.1347-58, 2019.

, Computational Modelling of Metastasis Development in Renal Cell Carcinoma

M. Bilous, C. Serdjebi, A. Boyer, P. Tomasini, C. Pouypoudat et al., Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer. Sci Rep, vol.9, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01928442

C. Nicolò, C. Périer, M. Prague, G. Macgrogan, O. Saut et al., Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer. bioRxiv. 10 mai, vol.634428, 2019.

S. Benzekry, A. Tracz, M. Mastri, R. Corbelli, D. Barbolosi et al., Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach, Cancer Res. 1 févr, vol.76, issue.3, pp.535-582, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01222046

Q. M. Pang, K. Li, L. J. Ma, and R. P. Sun, Clinical research on neuroblastoma based on serum lactate dehydrogenase, J Biol Regul Homeost Agents. mars, vol.29, issue.1, pp.131-135, 2015.

C. Dorneburg, M. Fischer, T. Barth, W. Mueller-klieser, B. Hero et al., LDHA in Neuroblastoma Is Associated with Poor Outcome and Its Depletion Decreases 23

, Neuroblastoma Growth Independent of Aerobic Glycolysis. Clin Cancer Res Off J Am Assoc Cancer Res, vol.24, issue.22, pp.5772-83, 2018.

B. L. Shulkin and B. Shapiro, Current concepts on the diagnostic use of MIBG in children, J Nucl Med Off Publ Soc Nucl Med. avr, vol.39, issue.4, pp.679-88, 1998.

K. K. Matthay, B. Shulkin, R. Ladenstein, J. Michon, F. Giammarile et al., Criteria for evaluation of disease extent by 123I-metaiodobenzylguanidine scans in neuroblastoma: a report for the International Neuroblastoma Risk Group (INRG) Task Force, Br J Cancer. 27 avr, vol.102, issue.9, pp.1319-1345, 2010.

T. Ara and Y. A. Declerck, Mechanisms of invasion and metastasis in human neuroblastoma, Cancer Metastasis Rev. déc, vol.25, issue.4, pp.645-57, 2006.

G. Bleeker, G. Tytgat, J. A. Adam, H. N. Caron, L. Kremer et al., 123I-MIBG scintigraphy and 18F-FDG-PET imaging for diagnosing neuroblastoma, Cochrane Database Syst Rev. 29 sept, issue.9, p.9263, 2015.

R. Ladenstein, B. Lambert, U. Pötschger, M. Castellani, V. Lewington et al., Validation of the mIBG skeletal SIOPEN scoring method in two independent high-risk neuroblastoma populations: the SIOPEN/HR-NBL1 and COG-A3973 trials, Eur J Nucl Med Mol Imaging. févr, vol.45, issue.2, pp.292-305, 2018.

J. R. Park, R. Bagatell, S. L. Cohn, A. D. Pearson, J. G. Villablanca et al., Revisions to the International Neuroblastoma Response Criteria: A Consensus Statement From the National Cancer Institute Clinical Trials Planning Meeting, J Clin Oncol. 1 août, vol.35, issue.22, pp.2580-2587, 2017.

K. Iwata, K. Kawasaki, and N. Shigesada, A dynamical model for the growth and size distribution of multiple metastatic tumors, J Theor Biol. 21 mars, vol.203, issue.2, pp.177-86, 2000.

N. Hartung, Efficient resolution of metastatic tumor growth models by reformulation into integral equations, Discrete Contin Dyn Syst -B. 1 janv, vol.20, issue.2, p.445, 2015.

J. S. Spratt, J. S. Meyer, and J. A. Spratt, Rates of growth of human solid neoplasms: Part I, J Surg Oncol, vol.60, issue.2, pp.137-183, 1995.

F. E. Harrell, K. L. Lee, and D. B. Mark, Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Stat Med. 28 févr, vol.15, issue.4, pp.361-87, 1996.

K. L. Gallik, R. W. Treffy, L. M. Nacke, K. Ahsan, M. Rocha et al., Neural crest and cancer: Divergent travelers on similar paths, Mech Dev, vol.148, pp.89-99, 2017.

L. Borriello, R. C. Seeger, S. Asgharzadeh, and Y. A. Declerck, More than the genes, the tumor microenvironment in neuroblastoma, Cancer Lett. 28 sept, vol.380, issue.1, pp.304-318, 2016.

K. P. Wilkie and P. Hahnfeldt, Tumor-immune dynamics regulated in the microenvironment inform the transient nature of immune-induced tumor dormancy, Cancer Res. 15 juin, vol.73, issue.12, pp.3534-3578, 2013.

T. Vanichapol, S. Chutipongtanate, U. Anurathapan, and S. Hongeng, Immune Escape Mechanisms and Future Prospects for Immunotherapy in Neuroblastoma, BioMed Res Int, p.1812535, 2018.

L. Jubierre, C. Jiménez, E. Rovira, A. Soriano, C. Sábado et al., Targeting of epigenetic regulators in neuroblastoma, Exp Mol Med, vol.27, issue.4, p.51, 2018.

S. Benzekry, C. Lamont, A. Beheshti, A. Tracz, J. Ebos et al., Classical mathematical models for description and prediction of experimental tumor growth, PLoS Comput Biol. août, vol.10, issue.8, p.1003800, 2014.

J. Ciccolini, D. Barbolosi, C. Meille, A. Lombard, C. Serdjebi et al., Pharmacokinetics and Pharmacodynamics-Based Mathematical Modeling Identifies an Optimal Protocol for Metronomic Chemotherapy, Cancer Res, vol.01, issue.17, pp.4723-4756, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01789578

Y. He, A. Kodali, and D. I. Wallace, Predictive Modeling of Neuroblastoma Growth Dynamics in Xenograft Model After Bevacizumab Anti-VEGF Therapy, Bull Math Biol, vol.80, issue.8, pp.2026-2074, 2018.

J. C. Kasemeier-kulesa, S. Schnell, T. Woolley, J. A. Spengler, J. A. Morrison et al., Predicting neuroblastoma using developmental signals and a logic-based model, Biophys Chem, vol.238, pp.30-38, 2018.

M. R. Hidalgo, A. Amadoz, C. Çubuk, J. Carbonell-caballero, and J. Dopazo, Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome, Biol Direct, vol.22, issue.1, p.16, 2018.

L. Zhang, C. Lv, Y. Jin, G. Cheng, Y. Fu et al., Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma, vol.9

P. Depuydt, J. Koster, V. Boeva, T. D. Hocking, F. Speleman et al., Metamining of copy number profiles of high-risk neuroblastoma tumors, Sci Data, vol.30, p.180240, 2018.

B. M. Salazar, E. A. Balczewski, C. Y. Ung, and S. Zhu, Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology, Int J Mol Sci. 27 déc, vol.18, issue.1, 2016.

H. E. Skipper, F. M. Schabel, L. B. Mellett, J. A. Montgomery, L. J. Wilkoff et al., Implications of biochemical, cytokinetic, pharmacologic, and toxicologic relationships in the design of optimal therapeutic schedules, Cancer Chemother Rep. déc, vol.54, issue.6, pp.431-50, 1970.

D. A. Morgenstern, R. Bagatell, S. L. Cohn, M. D. Hogarty, J. M. Maris et al., The challenge of defining « ultra-high-risk » neuroblastoma, Pediatr Blood Cancer. avr, vol.66, issue.4, p.27556, 2019.

J. R. Park, S. G. Kreissman, W. B. London, A. Naranjo, S. L. Cohn et al., A phase III randomized clinical trial (RCT) of tandem myeloablative autologous stem cell transplant (ASCT) using peripheral blood stem cell (PBSC) as consolidation therapy for high-risk neuroblastoma (HR-NB): A Children's Oncology Group (COG) study, J Clin Oncol. 20 juin, vol.34, pp.3-3, 2016.

, All -Recherche Google, Cancer facts and figures, 2015.

A. A. Ahmed, L. Zhang, N. Reddivalla, and M. Hetherington, Neuroblastoma in children: Update on clinicopathologic and genetic prognostic factors, Pediatr Hematol Oncol. avr, vol.34, issue.3, pp.165-85, 2017.

S. L. Cohn, A. Pearson, W. B. London, T. Monclair, P. F. Ambros et al., The International Neuroblastoma Risk Group (INRG) classification system: an INRG Task Force report, J Clin Oncol Off J Am Soc Clin Oncol. 10 janv, vol.27, issue.2, pp.289-97, 2009.

N. Cheung, M. A. Dyer, and . Neuroblastoma, Cancer Genomics, and Immunotherapy, vol.13, issue.6, pp.397-411, 2013.

S. B. Whittle, V. Smith, E. Doherty, S. Zhao, S. Mccarty et al., Overview and recent advances in the treatment of neuroblastoma, Expert Rev Anticancer Ther. 3 avr, vol.17, issue.4, pp.369-86, 2017.

E. Sokol and A. V. Desai, The Evolution of Risk Classification for Neuroblastoma, vol.11

W. B. London, R. P. Castleberry, K. K. Matthay, A. T. Look, R. C. Seeger et al., Evidence for an age cutoff greater than 365 days for neuroblastoma risk group stratification in the Children's Oncology Group, J Clin Oncol Off J Am Soc Clin Oncol. 20 sept, vol.23, issue.27, pp.6459-65, 2005.

M. L. Schmidt, A. Lal, R. C. Seeger, J. M. Maris, H. Shimada et al., Favorable prognosis for patients 12 to 18 months of age with stage 4 nonamplified MYCN neuroblastoma: a Children's Cancer Group Study, J Clin Oncol Off J Am Soc Clin Oncol. 20 sept, vol.23, issue.27, pp.6474-80, 2005.

D. Valteau-couanet, G. Schleiermacher, S. Sarnacki, and C. Pasqualini,

, Bull Cancer (Paris), vol.105, pp.918-942, 2018.

D. A. Morgenstern, U. Pötschger, L. Moreno, V. Papadakis, C. Owens et al., Risk stratification of high-risk metastatic neuroblastoma: A report from the HR-NBL-1/SIOPEN study, Pediatr Blood Cancer, vol.65, issue.11, p.27363, 2018.

K. K. Matthay, C. P. Reynolds, R. C. Seeger, H. Shimada, E. S. Adkins et al., Long-term results for children with high-risk neuroblastoma treated on a randomized trial of myeloablative therapy followed by 13-cis-retinoic acid: a children's oncology group study, J Clin Oncol Off J Am Soc Clin Oncol. 1 mars, vol.27, issue.7, pp.1007-1020, 2009.

R. Ladenstein, U. Pötschger, A. Pearson, P. Brock, R. Luksch et al., Busulfan and melphalan versus carboplatin, etoposide, and melphalan as high-dose chemotherapy for high-risk neuroblastoma (HR-NBL1/SIOPEN): an international, randomised, multi-arm, open-label, phase 3 trial, Lancet Oncol, vol.18, issue.4, pp.500-514, 2017.

W. B. London, R. Bagatell, B. J. Weigel, E. Fox, D. Guo et al., Historical time to disease progression and progression-free survival in patients with recurrent/refractory neuroblastoma treated in the modern era on Children's Oncology Group early-phase trials, Cancer. 15 déc, vol.123, issue.24, pp.4914-4937, 2017.

N. O. Basta, G. C. Halliday, G. Makin, J. Birch, R. Feltbower et al., Factors associated with recurrence and survival length following relapse in patients with neuroblastoma, Br J Cancer, vol.115, issue.9, pp.1048-57, 2016.

H. E. Skipper, F. M. Schabel, W. S. Wilcox, . Experimental, . X. Of-potential-anticancer-agents et al., Cancer Chemother Rep. févr, vol.35, pp.1-111, 1964.

P. M. Altrock, L. L. Liu, and F. Michor, The mathematics of cancer: integrating quantitative models, Nat Rev Cancer. déc, vol.15, issue.12, pp.730-775, 2015.

S. Benzekry, E. Pasquier, D. Barbolosi, B. Lacarelle, F. Barlési et al., Metronomic reloaded: Theoretical models bringing chemotherapy into the era of precision medicine, Semin Cancer Biol. déc, vol.35, pp.53-61, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01195547

D. Barbolosi, J. Ciccolini, B. Lacarelle, F. Barlési, and N. André, Computational oncology--mathematical modelling of drug regimens for precision medicine, Nat Rev Clin Oncol. avr, vol.13, issue.4, pp.242-54, 2016.

, Multiscale Cancer Modeling, 2019.

A. Rajkomar, J. Dean, and I. Kohane, Machine Learning in Medicine, N Engl J Med. 4 avr, vol.380, issue.14, pp.1347-58, 2019.

, Computational Modelling of Metastasis Development in Renal Cell Carcinoma

M. Bilous, C. Serdjebi, A. Boyer, P. Tomasini, C. Pouypoudat et al., Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer. Sci Rep, vol.9, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01928442

C. Nicolò, C. Périer, M. Prague, G. Macgrogan, O. Saut et al., Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer. bioRxiv. 10 mai, vol.634428, 2019.

S. Benzekry, A. Tracz, M. Mastri, R. Corbelli, D. Barbolosi et al., Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach, Cancer Res. 1 févr, vol.76, issue.3, pp.535-582, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01222046

Q. M. Pang, K. Li, L. J. Ma, and R. P. Sun, Clinical research on neuroblastoma based on serum lactate dehydrogenase, J Biol Regul Homeost Agents. mars, vol.29, issue.1, pp.131-135, 2015.

C. Dorneburg, M. Fischer, T. Barth, W. Mueller-klieser, B. Hero et al., LDHA in Neuroblastoma Is Associated with Poor Outcome and Its Depletion Decreases Neuroblastoma Growth Independent of Aerobic Glycolysis, Clin Cancer Res Off J Am Assoc Cancer Res, vol.24, issue.22, pp.5772-83, 2018.

B. L. Shulkin and B. Shapiro, Current concepts on the diagnostic use of MIBG in children, J Nucl Med Off Publ Soc Nucl Med. avr, vol.39, issue.4, pp.679-88, 1998.

K. K. Matthay, B. Shulkin, R. Ladenstein, J. Michon, F. Giammarile et al., Criteria for evaluation of disease extent by 123I-metaiodobenzylguanidine scans in neuroblastoma: a report for the International Neuroblastoma Risk Group (INRG) Task Force, Br J Cancer. 27 avr, vol.102, issue.9, pp.1319-1345, 2010.

T. Ara and Y. A. Declerck, Mechanisms of invasion and metastasis in human neuroblastoma, Cancer Metastasis Rev. déc, vol.25, issue.4, pp.645-57, 2006.

G. Bleeker, G. Tytgat, J. A. Adam, H. N. Caron, L. Kremer et al., 123I-MIBG scintigraphy and 18F-FDG-PET imaging for diagnosing neuroblastoma, Cochrane Database Syst Rev. 29 sept, issue.9, p.9263, 2015.

R. Ladenstein, B. Lambert, U. Pötschger, M. Castellani, V. Lewington et al., Validation of the mIBG skeletal SIOPEN scoring method in two independent high-risk neuroblastoma populations: the SIOPEN/HR-NBL1 and COG-A3973 trials, Eur J Nucl Med Mol Imaging. févr, vol.45, issue.2, pp.292-305, 2018.

J. R. Park, R. Bagatell, S. L. Cohn, A. D. Pearson, J. G. Villablanca et al., Revisions to the International Neuroblastoma Response Criteria: A Consensus Statement From the 22

, National Cancer Institute Clinical Trials Planning Meeting, J Clin Oncol. 1 août, vol.35, issue.22, pp.2580-2587, 2017.

K. Iwata, K. Kawasaki, and N. Shigesada, A dynamical model for the growth and size distribution of multiple metastatic tumors, J Theor Biol. 21 mars, vol.203, issue.2, pp.177-86, 2000.

N. Hartung, Efficient resolution of metastatic tumor growth models by reformulation into integral equations, Discrete Contin Dyn Syst -B. 1 janv, vol.20, issue.2, p.445, 2015.

J. S. Spratt, J. S. Meyer, and J. A. Spratt, Rates of growth of human solid neoplasms: Part I, J Surg Oncol, vol.60, issue.2, pp.137-183, 1995.

. Expasy--cellosaurus, , p.12, 2019.

F. E. Harrell, K. L. Lee, and D. B. Mark, Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Stat Med. 28 févr, vol.15, issue.4, pp.361-87, 1996.

K. L. Gallik, R. W. Treffy, L. M. Nacke, K. Ahsan, M. Rocha et al., Neural crest and cancer: Divergent travelers on similar paths, Mech Dev, vol.148, pp.89-99, 2017.

L. Borriello, R. C. Seeger, S. Asgharzadeh, and Y. A. Declerck, More than the genes, the tumor microenvironment in neuroblastoma, Cancer Lett. 28 sept, vol.380, issue.1, pp.304-318, 2016.

K. P. Wilkie and P. Hahnfeldt, Tumor-immune dynamics regulated in the microenvironment inform the transient nature of immune-induced tumor dormancy, Cancer Res. 15 juin, vol.73, issue.12, pp.3534-3578, 2013.

T. Vanichapol, S. Chutipongtanate, U. Anurathapan, and S. Hongeng, Immune Escape Mechanisms and Future Prospects for Immunotherapy in Neuroblastoma, BioMed Res Int, p.1812535, 2018.

L. Jubierre, C. Jiménez, E. Rovira, A. Soriano, C. Sábado et al., Targeting of epigenetic regulators in neuroblastoma, Exp Mol Med, vol.27, issue.4, p.51, 2018.

S. Benzekry, C. Lamont, A. Beheshti, A. Tracz, J. Ebos et al., Classical mathematical models for description and prediction of experimental tumor growth, PLoS Comput Biol. août, vol.10, issue.8, p.1003800, 2014.

J. Ciccolini, D. Barbolosi, C. Meille, A. Lombard, C. Serdjebi et al., Pharmacokinetics and Pharmacodynamics-Based Mathematical Modeling Identifies an Optimal Protocol for Metronomic Chemotherapy, Cancer Res, vol.01, issue.17, pp.4723-4756, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01789578

Y. He, A. Kodali, and D. I. Wallace, Predictive Modeling of Neuroblastoma Growth Dynamics in Xenograft Model After Bevacizumab Anti-VEGF Therapy, Bull Math Biol, vol.80, issue.8, pp.2026-2074, 2018.

J. C. Kasemeier-kulesa, S. Schnell, T. Woolley, J. A. Spengler, J. A. Morrison et al., Predicting neuroblastoma using developmental signals and a logic-based model, Biophys Chem, vol.238, pp.30-38, 2018.

M. R. Hidalgo, A. Amadoz, C. Çubuk, J. Carbonell-caballero, and J. Dopazo, Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome, Biol Direct, vol.22, issue.1, p.16, 2018.

L. Zhang, C. Lv, Y. Jin, G. Cheng, Y. Fu et al., Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma, vol.9

P. Depuydt, J. Koster, V. Boeva, T. D. Hocking, F. Speleman et al., Metamining of copy number profiles of high-risk neuroblastoma tumors, Sci Data, vol.30, p.180240, 2018.

B. M. Salazar, E. A. Balczewski, C. Y. Ung, and S. Zhu, Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology, Int J Mol Sci. 27 déc, vol.18, issue.1, 2016.

H. E. Skipper, F. M. Schabel, L. B. Mellett, J. A. Montgomery, L. J. Wilkoff et al., Implications of biochemical, cytokinetic, pharmacologic, and toxicologic relationships in the design of optimal therapeutic schedules, Cancer Chemother Rep. déc, vol.54, issue.6, pp.431-50, 1970.

L. Norton and J. Massagué, Is cancer a disease of self-seeding, Nat Med. août, vol.12, issue.8, pp.875-883, 2006.

D. A. Morgenstern, R. Bagatell, S. L. Cohn, M. D. Hogarty, J. M. Maris et al., The challenge of defining « ultra-high-risk » neuroblastoma, Pediatr Blood Cancer. avr, vol.66, issue.4, p.27556, 2019.

J. R. Park, S. G. Kreissman, W. B. London, A. Naranjo, S. L. Cohn et al., A phase III randomized clinical trial (RCT) of tandem myeloablative autologous stem cell transplant (ASCT) using peripheral blood stem cell (PBSC) as consolidation therapy for high-risk neuroblastoma (HR-NB): A Children's Oncology Group (COG) study, J Clin Oncol. 20 juin, vol.34, pp.3-3, 2016.