machine learning algorithms may be of benefit, although future studies will be needed to elucidate
relevant mechanisms for biological explanations.
In conclusion, all present efforts will certainly lead to a change in our way how we assess prognosis in
MDS. Further improvement is to be expected on the way to individualized patient approaches.
Selected Literature
Greenberg P. et al. Blood 1997; 89: 2079–2088
Malcovati L. et al. J Clin Oncol. 2007; 25: 3503-10
Garcia-Manero G. et al. Leukemia 2008;22: 538-43
Kantarjian H. et al. Cancer 2008; 113: 1351-61
Bejar R. et al. N Engl J Med 2011; 364: 2496-506
Greenberg PL. et al. Blood 2012; 120: 2454–2465
Papaemmanuil E et al. Blood 2013; 122: 3616-27
Jonas BA, Greenberg PL. Best Pract Res Clin Haematol. 2015; 28: 3–13
Pfeilstöcker M. et al. Blood 2016; 128: 902-10
Nazha A. et al. Leukemia 2017 Dec; 31: 2848-2850
Benton CB. et al. Am J Hematol 2018; 93: 1245-1253
Cazzola M. N Engl J Med 2020; 383:1358-1374
SCIENTIFIC PROGRAMME
SESSION I
ETIOLOGY OF MDS
SESSION II
BIOLOGY OF MDS –
GENETIC ABNORMALITIES
SESSION III
BIOLOGY OF MDS:
STEM CELLS AND THE
MICROENVIRONMENT
SESSION IV
DIAGNOSTIC WORKUP
AND PROGNOSTIC
FACTORS IN MDS
SESSION V
SPECIFIC SUBTYPES
OF MDS, BASED ON
MORPHOLOGY AND
MOLECULAR BIOLOGY
SESSION VI
TREATMENT OF MDS
SESSION VII
CURRENT PROGRESS IN
THE TREATMENT OF MDS
SESSION VIII
FUTURE TREATMENTS
AND TREATMENT
STRATEGIES IN MDS
SESSION IX
LATE-BREAKING TALKS
SELECTED ABSTRACTS
FOR AN ORAL
PRESENTATION
SELECTED ABSTRACTS
AS E-POSTERS
DISCLOSURES