Literature DB >> 24319225

Prognostic models in myelodysplastic syndromes.

Rafael Bejar1.   

Abstract

Establishing the prognosis for patients with myelodysplastic syndromes (MDS) is a key element of their care. It helps patients understand the severity of their disease and set expectations for their future. For physicians, an accurate estimate of prognosis drives decisions about the timing and choice of therapeutic options to consider. The International Prognostic Scoring System (IPSS) has been the standard tool for MDS risk stratification since it was released in 1997. It has been used to describe patients in pivotal clinical trials and is a key element of practice guidelines. Subsequent changes to the classification scheme for MDS and an underestimation of risk in some patients from the low and intermediate-1 categories have led to the development of several newer prognostic models. The most recent is the revised IPSS (IPSS-R), which addresses several of the perceived deficiencies of its predecessor. Despite their utility, none of the available prognostic systems incorporates disease-related molecular abnormalities such as somatic mutations. These lesions are present in the nearly all cases and many have been shown to improve upon existing prognostic models. However, the interpretation of somatic mutations can be challenging and it is not yet clear how best to combine them with clinical predictors of outcome. Here I review several prognostic scoring systems developed after the IPSS and describe the emerging use of molecular markers to refine risk stratification in the MDS patient population.

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Year:  2013        PMID: 24319225     DOI: 10.1182/asheducation-2013.1.504

Source DB:  PubMed          Journal:  Hematology Am Soc Hematol Educ Program        ISSN: 1520-4383


  8 in total

Review 1.  Genetic predisposition syndromes: when should they be considered in the work-up of MDS?

Authors:  Daria V Babushok; Monica Bessler
Journal:  Best Pract Res Clin Haematol       Date:  2014-11-12       Impact factor: 3.020

2.  Differences in community and academic practice patterns for newly diagnosed myelodysplastic syndromes (MDS) patients.

Authors:  Daniel F Pease; Julie A Ross; Jenny N Poynter; Phuong L Nguyen; Betsy Hirsch; Adina Cioc; Michelle A Roesler; Erica D Warlick
Journal:  Cancer Epidemiol       Date:  2015-02-18       Impact factor: 2.984

Review 3.  Molecular Data and the IPSS-R: How Mutational Burden Can Affect Prognostication in MDS.

Authors:  Aziz Nazha; Rafael Bejar
Journal:  Curr Hematol Malig Rep       Date:  2017-10       Impact factor: 3.952

4.  Validation of the revised International Prognostic Scoring System in patients with myelodysplastic syndrome in Japan: results from a prospective multicenter registry.

Authors:  Hiroshi Kawabata; Kaoru Tohyama; Akira Matsuda; Kayano Araseki; Tomoko Hata; Takahiro Suzuki; Hidekazu Kayano; Kei Shimbo; Yuji Zaike; Kensuke Usuki; Shigeru Chiba; Takayuki Ishikawa; Nobuyoshi Arima; Masaharu Nogawa; Akiko Ohta; Yasushi Miyazaki; Kinuko Mitani; Keiya Ozawa; Shunya Arai; Mineo Kurokawa; Akifumi Takaori-Kondo
Journal:  Int J Hematol       Date:  2017-05-11       Impact factor: 2.490

Review 5.  Molecular Testing in Myelodysplastic Syndromes for the Practicing Oncologist: Will the Progress Fulfill the Promise?

Authors:  Aziz Nazha; Mikkael A Sekeres; Steven D Gore; Amer M Zeidan
Journal:  Oncologist       Date:  2015-07-20

6.  Risk stratification in myelodysplastic syndromes: is there a role for gene expression profiling?

Authors:  Amer M Zeidan; Thomas Prebet; Ehab Saad Aldin; Steven David Gore
Journal:  Expert Rev Hematol       Date:  2014-02-24       Impact factor: 2.929

Review 7.  Pathogenesis of myelodysplastic syndromes: an overview of molecular and non-molecular aspects of the disease.

Authors:  Valeria Visconte; Ramon V Tiu; Heesun J Rogers
Journal:  Blood Res       Date:  2014-12-23

8.  Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications.

Authors:  Amir Foroushani; Rupesh Agrahari; Roderick Docking; Linda Chang; Gerben Duns; Monika Hudoba; Aly Karsan; Habil Zare
Journal:  BMC Med Genomics       Date:  2017-03-16       Impact factor: 3.063

  8 in total

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