, All -Recherche Google, Cancer facts and figures, 2015.
Neuroblastoma in children: Update on clinicopathologic and genetic prognostic factors, Pediatr Hematol Oncol. avr, vol.34, issue.3, pp.165-85, 2017. ,
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. ,
, Cancer Genomics, and Immunotherapy, vol.13, issue.6, pp.397-411, 2013.
Overview and recent advances in the treatment of neuroblastoma, Expert Rev Anticancer Ther. 3 avr, vol.17, issue.4, pp.369-86, 2017. ,
The Evolution of Risk Classification for Neuroblastoma, vol.11 ,
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. ,
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. ,
,
, Bull Cancer (Paris), vol.105, pp.918-942, 2018.
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. ,
,
, 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.
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. ,
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. ,
Factors associated with recurrence and survival length following relapse in patients with neuroblastoma, Br J Cancer, vol.115, issue.9, pp.1048-57, 2016. ,
, Cancer Chemother Rep. févr, vol.35, pp.1-111, 1964.
The mathematics of cancer: integrating quantitative models, Nat Rev Cancer. déc, vol.15, issue.12, pp.730-775, 2015. ,
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
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.
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
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
Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer. bioRxiv. 10 mai, vol.634428, 2019. ,
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
Clinical research on neuroblastoma based on serum lactate dehydrogenase, J Biol Regul Homeost Agents. mars, vol.29, issue.1, pp.131-135, 2015. ,
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.
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. ,
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. ,
Mechanisms of invasion and metastasis in human neuroblastoma, Cancer Metastasis Rev. déc, vol.25, issue.4, pp.645-57, 2006. ,
123I-MIBG scintigraphy and 18F-FDG-PET imaging for diagnosing neuroblastoma, Cochrane Database Syst Rev. 29 sept, issue.9, p.9263, 2015. ,
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. ,
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. ,
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. ,
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. ,
Rates of growth of human solid neoplasms: Part I, J Surg Oncol, vol.60, issue.2, pp.137-183, 1995. ,
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. ,
Neural crest and cancer: Divergent travelers on similar paths, Mech Dev, vol.148, pp.89-99, 2017. ,
More than the genes, the tumor microenvironment in neuroblastoma, Cancer Lett. 28 sept, vol.380, issue.1, pp.304-318, 2016. ,
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. ,
Immune Escape Mechanisms and Future Prospects for Immunotherapy in Neuroblastoma, BioMed Res Int, p.1812535, 2018. ,
Targeting of epigenetic regulators in neuroblastoma, Exp Mol Med, vol.27, issue.4, p.51, 2018. ,
Classical mathematical models for description and prediction of experimental tumor growth, PLoS Comput Biol. août, vol.10, issue.8, p.1003800, 2014. ,
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
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. ,
Predicting neuroblastoma using developmental signals and a logic-based model, Biophys Chem, vol.238, pp.30-38, 2018. ,
Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome, Biol Direct, vol.22, issue.1, p.16, 2018. ,
Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma, vol.9 ,
Metamining of copy number profiles of high-risk neuroblastoma tumors, Sci Data, vol.30, p.180240, 2018. ,
Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology, Int J Mol Sci. 27 déc, vol.18, issue.1, 2016. ,
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. ,
The challenge of defining « ultra-high-risk » neuroblastoma, Pediatr Blood Cancer. avr, vol.66, issue.4, p.27556, 2019. ,
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.
Neuroblastoma in children: Update on clinicopathologic and genetic prognostic factors, Pediatr Hematol Oncol. avr, vol.34, issue.3, pp.165-85, 2017. ,
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. ,
, Cancer Genomics, and Immunotherapy, vol.13, issue.6, pp.397-411, 2013.
Overview and recent advances in the treatment of neuroblastoma, Expert Rev Anticancer Ther. 3 avr, vol.17, issue.4, pp.369-86, 2017. ,
The Evolution of Risk Classification for Neuroblastoma, vol.11 ,
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. ,
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. ,
,
, Bull Cancer (Paris), vol.105, pp.918-942, 2018.
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. ,
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. ,
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. ,
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. ,
Factors associated with recurrence and survival length following relapse in patients with neuroblastoma, Br J Cancer, vol.115, issue.9, pp.1048-57, 2016. ,
, Cancer Chemother Rep. févr, vol.35, pp.1-111, 1964.
The mathematics of cancer: integrating quantitative models, Nat Rev Cancer. déc, vol.15, issue.12, pp.730-775, 2015. ,
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
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.
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
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
Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer. bioRxiv. 10 mai, vol.634428, 2019. ,
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
Clinical research on neuroblastoma based on serum lactate dehydrogenase, J Biol Regul Homeost Agents. mars, vol.29, issue.1, pp.131-135, 2015. ,
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. ,
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. ,
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. ,
Mechanisms of invasion and metastasis in human neuroblastoma, Cancer Metastasis Rev. déc, vol.25, issue.4, pp.645-57, 2006. ,
123I-MIBG scintigraphy and 18F-FDG-PET imaging for diagnosing neuroblastoma, Cochrane Database Syst Rev. 29 sept, issue.9, p.9263, 2015. ,
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. ,
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.
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. ,
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. ,
Rates of growth of human solid neoplasms: Part I, J Surg Oncol, vol.60, issue.2, pp.137-183, 1995. ,
, , p.12, 2019.
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. ,
Neural crest and cancer: Divergent travelers on similar paths, Mech Dev, vol.148, pp.89-99, 2017. ,
More than the genes, the tumor microenvironment in neuroblastoma, Cancer Lett. 28 sept, vol.380, issue.1, pp.304-318, 2016. ,
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. ,
Immune Escape Mechanisms and Future Prospects for Immunotherapy in Neuroblastoma, BioMed Res Int, p.1812535, 2018. ,
Targeting of epigenetic regulators in neuroblastoma, Exp Mol Med, vol.27, issue.4, p.51, 2018. ,
Classical mathematical models for description and prediction of experimental tumor growth, PLoS Comput Biol. août, vol.10, issue.8, p.1003800, 2014. ,
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
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. ,
Predicting neuroblastoma using developmental signals and a logic-based model, Biophys Chem, vol.238, pp.30-38, 2018. ,
Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome, Biol Direct, vol.22, issue.1, p.16, 2018. ,
Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma, vol.9 ,
Metamining of copy number profiles of high-risk neuroblastoma tumors, Sci Data, vol.30, p.180240, 2018. ,
Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology, Int J Mol Sci. 27 déc, vol.18, issue.1, 2016. ,
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. ,
Is cancer a disease of self-seeding, Nat Med. août, vol.12, issue.8, pp.875-883, 2006. ,
The challenge of defining « ultra-high-risk » neuroblastoma, Pediatr Blood Cancer. avr, vol.66, issue.4, p.27556, 2019. ,
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. ,