Brady SW, Roberts KG, Gu Z, et al. The genomic landscape of pediatric acute lymphoblastic leukemia. Nat Genet. 2022;54(9):1376–89. https://doi.org/10.1038/s41588-022-01159-z.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pui CH, Yang JJ, Hunger SP, Pieters R, et al. Childhood acute lymphoblastic leukemia: progress through collaboration. J Clin Oncol. 2015;33(27):2938–48. https://doi.org/10.1200/JCO.2014.59.1636.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Oskarsson T, Soderhall S, Arvidson J, et al. Relapsed childhood acute lymphoblastic leukemia in the Nordic countries: prognostic factors, treatment and outcome. Haematologica. 2016;101(1):68–76.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bhakta N, Liu Q, Ness KK, et al. The cumulative burden of surviving childhood cancer: an initial report from the St. Jude lifetime cohort study (SJLIFE). Lancet. 2017;390(10112):2569–82. https://doi.org/10.1016/S0140-6736(17)31610-0.

Article  PubMed  PubMed Central  Google Scholar 

Diesch-Furlanetto T, Gabriel M, Zajac-Spychala O, Cattoni A, Hoeben BAW, Balduzzi A. Late effects after haematopoietic stem cell transplantation in ALL, long-term follow-up and transition: a step into adult life. Front Pediatr. 2021;24(9):773895. https://doi.org/10.3389/fped.2021.773895.

Article  Google Scholar 

Schmiegelow K, Forestier E, Hellebostad M, et al. Long-term results of NOPHO ALL-92 and ALL-2000 studies of childhood acute lymphoblastic leukemia. Leukemia. 2010;24(2):345–54. https://doi.org/10.1038/leu.2009.251.

Article  CAS  PubMed  Google Scholar 

Inaba H, Mullighan CG. Pediatric acute lymphoblastic leukemia. Haematologica. 2020;105(11):2524–39. https://doi.org/10.3324/haematol.2020.247031.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Terwilliger T, Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J. 2017;7(6):e577. https://doi.org/10.1038/bcj.2017.53.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pieters R, de Groot-Kruseman H, Van der Velden V, et al. Successful therapy reduction and intensification for childhood acute lymphoblastic leukemia based on minimal residual disease monitoring: study ALL10 from the dutch childhood oncology group. J Clin Oncol. 2016;34(22):2591–601. https://doi.org/10.1200/JCO.2015.64.6364.

Article  PubMed  Google Scholar 

Maloney KW, Devidas M, Wang C, et al. Outcome in children with standard-risk B-cell acute lymphoblastic leukemia: results of children’s oncology group trial AALL0331. J Clin Oncol. 2020;38(6):602–12. https://doi.org/10.1200/JCO.19.01086.

Article  CAS  PubMed  Google Scholar 

Steinherz PG, Seibel NL, Sather H, et al. Treatment of higher risk acute lymphoblastic leukemia in young people (CCG-1961), long-term follow-up: a report from the children’s oncology group. Leukemia. 2019;33(9):2144–54. https://doi.org/10.1038/s41375-019-0422-z.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Toft N, Birgens H, Abrahamsson J, et al. Results of NOPHO ALL2008 treatment for patients aged 1–45 years with acute lymphoblastic leukemia. Leukemia. 2018;32(3):606–15. https://doi.org/10.1038/leu.2017.265.

Article  CAS  PubMed  Google Scholar 

Vora A, Goulden N, Wade R, et al. Treatment reduction for children and young adults with low-risk acute lymphoblastic leukemia defined by minimal residual disease (UKALL 2003): a randomized controlled trial. Lancet Oncol. 2013;14(3):199–209. https://doi.org/10.1016/S1470-2045(12)70600-9.

Article  CAS  PubMed  Google Scholar 

Mondelaers V, Suciu S, De Moerloose B, et al. Prolonged versus standard native E coli. asparaginase therapy in childhood acute lymphoblastic leukemia and non-Hodgkin lymphoma: final results of the EORTC-CLG randomized phase III trial 58951. Haematologica. 2017;102(10):1727–38. https://doi.org/10.3324/haematol.2017.165845.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schramm F, Zimmermann M, Jorch N, et al. Daunorubicin during delayed intensification decreases the incidence of infectious complications - a randomized comparison in trial CoALL 08–09. Leuk Lymphoma. 2019;60(1):60–8. https://doi.org/10.1080/10428194.2018.1473575.

Article  CAS  PubMed  Google Scholar 

Hallböök H, Gustafsson G, Smedmyr B, et al. Treatment outcome in young adults and children >10 years of age with acute lymphoblastic leukemia in Sweden: a comparison between a pediatric protocol and an adult protocol. Cancer. 2006;107(7):1551–61. https://doi.org/10.1002/cncr.22189.

Article  CAS  PubMed  Google Scholar 

Stanulla M, Cavé H, Moorman AV. IKZF1 deletions in pediatric acute lymphoblastic leukemia: still a poor prognostic marker? Blood. 2020;135(4):252–60. https://doi.org/10.1182/blood.2019000813.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Olsson L, Ivanov Öfverholm I, Norén-Nyström U, et al. The clinical impact of IKZF1 deletions in pediatric B-cell precursor acute lymphoblastic leukemia is independent of minimal residual disease stratification in Nordic society for pediatric hematology and oncology treatment protocols used between 1992 and 2013. Br J Haematol. 2015;170(6):847–58. https://doi.org/10.1111/bjh.13514.

Article  CAS  PubMed  Google Scholar 

Eapen M, Raetz E, Zhang MJ, et al. Outcomes after HLA-matched sibling transplantation or chemotherapy in children with B-precursor acute lymphoblastic leukemia in a second remission: a collaborative study of the children’s oncology group and the Center for international blood and marrow transplant research. Blood. 2006;107(12):4961–7.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schroeder H, Gustafsson G, Saarinen-Pihkala UM, et al. Allogeneic bone marrow transplantation in second remission of childhood acute lymphoblastic leukemia: a population-based case control study from the Nordic countries. Bone Marrow Transplant. 1999;23(6):555–60.

Article  CAS  PubMed  Google Scholar 

Grupp SA, Kalos M, Barrett D, et al. Chimeric antigen receptor-modified T cells for acute lymphoid leukemia. N Engl J Med. 2013;368(16):1509–18.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wong M, Mayoh C, Lau LMS, et al. Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk pediatric cancer. Nat Med. 2020;26(11):1742–53. https://doi.org/10.1038/s41591-020-1072-4.

Article  CAS  PubMed  Google Scholar 

Villani A, Davidson S, Kanwar N, et al. The clinical utility of integrative genomics in childhood cancer extends beyond targetable mutations. Nat Cancer. 2023;4(2):203–21. https://doi.org/10.1038/s43018-022-00474-y.

Article  CAS  PubMed  Google Scholar 

Alvarnas JC, Brown PA, Aoun P, et al. Acute lymphoblastic leukemia. J Natl Compr Canc Netw. 2015;13(10):1240–79. https://doi.org/10.6004/jnccn.2015.0153.

Article  CAS  PubMed  Google Scholar 

Bataller A, Garrido A, Guijarro F, et al. European LeukemiaNet 2017 risk stratification for acute myeloid leukemia: validation in a risk-adapted protocol. Blood Adv. 2022;6(4):1193–206. https://doi.org/10.1182/bloodadvances.2021005585.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Young TA, Thompson S. The importance of accounting for the uncertainty of published prognostic model estimates. Int J Technol Assess Health Care. 2004;20(4):481–7. https://doi.org/10.1017/s0266462304001394.

Article  PubMed  Google Scholar 

Nordlund J, Bäcklin CL, Zachariadis V, et al. DNA methylation-based subtype prediction for pediatric acute lymphoblastic leukemia. Clin Epigenetics. 2015;7(1):11. https://doi.org/10.1186/s13148-014-0039-z.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tran TH, Langlois S, Meloche C, et al. Whole-transcriptome analysis in acute lymphoblastic leukemia: a report from the DFCI ALL Consortium Protocol 16–001. Blood Adv. 2022;6(4):1329–41. https://doi.org/10.1182/bloodadvances.2021005634.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Löschmann L, Smorodina D. Deep learning for survival analysis. Retrieved 2020;5, 2023, from https://towardsdatascience.com/survival-analysis-predict-time-to-event-with-machine-learning-part-i-ba52f9ab9a46

Mosquera-Orgueira A, Pérez-Encinas M, Hernández-Sánchez A, et al. Machine learning improves risk stratification in myelofibrosis: an analysis of the Spanish registry of myelofibrosis. Hemasphere. 2022;7(1):e818. https://doi.org/10.1097/HS9.0000000000000818.

Article  PubMed  PubMed Central  Google Scholar 

Mosquera Orgueira A, Perez Encinas M, Diaz Varela NA, et al. Supervised machine learning improves risk stratification in newly diagnosed myelodysplastic syndromes: an analysis of the Spanish group of myelodysplastic syndromes. Blood. 2022;140(Supplement 1):1132–4. https://doi.org/10.1182/blood-2022-159429.

Article  Google Scholar 

Mosquera Orgueira A, González Pérez MS, Díaz Arias JÁ, et al. Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data. Leukemia. 2021;35(10):2924–35. https://doi.org/10.1038/s41375-021-01286-2.

Article  CAS