The Evolving Landscape of Myelodysplastic Syndrome Prognostication

Authors

Jacob Shreve, Aziz Nazha^*

Department of Hematology and Medical Oncology, Taussig Cancer Center, Cleveland Clinic, 9500 Euclid Ave., Cleveland, OH, USA

^*Corresponding author. Additional Info: Director, Cleveland Clinic, Center for Clinical Artificial Intelligence; Assistant Professor, Lerner College of Medicine, Case Western Reserve University. Tel.: (216) 445-0320. Email: nazhaa@ccf.org

DOI

10.2991/chi.d.200408.001How to use a DOI?

Keywords

Acute myeloid leukemia (AML); Myelodysplastic syndromes (MDS); Prognostic model; Machine learning

Abstract

Myelodysplastic syndromes (MDSs) are potentially devastating monoclonal deviations of hematopoiesis that lead to bone marrow dysplasia and variable cytopenias. Predicting severity of disease progression and likelihood to undergo acute myeloid leukemia transformation is the basis of treatment strategy. Some patients belong to a low-risk cohort best managed with conservative supportive care, whereas others are included in a high-risk cohort that requires decisive therapy with hematopoietic cell transplantation or hypomethylating agent administration. Risk scoring systems for MDS prognostication were traditionally based on karyotype characteristics and clinical factors readily available from chart review, and validation was typically conducted on de novo MDS patients. However, retrospective analysis found a large subset of patients incorrectly risk-stratified. In this review, the most commonly used scoring systems are evaluated, and pitfalls therein are identified. Emerging technologies such as personal genomics and machine learning are then explored for efficacy in MDS risk modeling. Barriers to clinical adoption of artificial intelligence-derived models are discussed, with focus on approaches meant to increase model interpretability and clinical relevance. Finally, a guiding set of recommendations is proposed for best designing an accurate and universally applicable prognostic model for MDS, which is supported by more than 20 years of observation of traditional scoring system performance, as well as modern efforts in creating hybrid genomic-clinical scoring systems.

Copyright

© 2020 International Academy for Clinical Hematology. Publishing services by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jacob Shreve
AU  - Aziz Nazha
PY  - 2020
DA  - 2020/04/19
TI  - The Evolving Landscape of Myelodysplastic Syndrome Prognostication
JO  - Clinical Hematology International
SP  - 43
EP  - 48
VL  - 2
IS  - 2
SN  - 2590-0048
UR  - https://doi.org/10.2991/chi.d.200408.001
DO  - 10.2991/chi.d.200408.001
ID  - Shreve2020
ER  -