Literature DB >> 29946191

Diagnostic algorithm for lower-risk myelodysplastic syndromes.

Ghulam J Mufti1, Donal P McLornan2, Arjan A van de Loosdrecht3, Ulrich Germing4, Robert P Hasserjian5.   

Abstract

Rapid advances over the past decade have uncovered the heterogeneous genomic and immunologic landscape of myelodysplastic syndromes (MDS). This has led to notable improvements in the accuracy and timing of diagnosis and prognostication of MDS, as well as the identification of possible novel targets for therapeutic intervention. For the practicing clinician, however, this increase in genomic, epigenomic, and immunologic knowledge needs consideration in a "real-world" context to aid diagnostic specificity. Although the 2016 revision to the World Health Organization classification for MDS is comprehensive and timely, certain limitations still exist for day-to-day clinical practice. In this review, we describe an up-to-date diagnostic approach to patients with suspected lower-risk MDS, including hypoplastic MDS, and demonstrate the requirement for an "integrated" diagnostic approach. Moreover, in the era of rapid access to massive parallel sequencing platforms for mutational screening, we suggest which patients should undergo such analyses, when such screening should be performed, and how those data should be interpreted. This is particularly relevant given the recent findings describing age-related clonal hematopoiesis.

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Year:  2018        PMID: 29946191     DOI: 10.1038/s41375-018-0173-2

Source DB:  PubMed          Journal:  Leukemia        ISSN: 0887-6924            Impact factor:   11.528


  2 in total

1.  RUNX1 mutations contribute to the progression of MDS due to disruption of antitumor cellular defense: a study on patients with lower-risk MDS.

Authors:  Monika Kaisrlikova; Jitka Vesela; David Kundrat; Hana Votavova; Michaela Dostalova Merkerova; Zdenek Krejcik; Vladimir Divoky; Marek Jedlicka; Jan Fric; Jiri Klema; Dana Mikulenkova; Marketa Stastna Markova; Marie Lauermannova; Jolana Mertova; Jacqueline Soukupova Maaloufova; Anna Jonasova; Jaroslav Cermak; Monika Belickova
Journal:  Leukemia       Date:  2022-05-03       Impact factor: 12.883

2.  A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA.

Authors:  Konobu Kimura; Yoko Tabe; Tomohiko Ai; Ikki Takehara; Hiroshi Fukuda; Hiromizu Takahashi; Toshio Naito; Norio Komatsu; Kinya Uchihashi; Akimichi Ohsaka
Journal:  Sci Rep       Date:  2019-09-16       Impact factor: 4.379

  2 in total

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