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Cited by 355

The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index.

Multiple myeloma current treatment algorithms

Open access
Rajkumar, S. Vincent, Kumar, Shaji·2020·Blood Cancer Journal

Abstract

The treatment of multiple myeloma (MM) continues to evolve rapidly with arrival of multiple new drugs, and emerging data from randomized trials to guide therapy. Along the disease course, the choice of specific therapy is affected by many variables including age, performance status, comorbidities, and eligibility for stem cell transplantation. In addition, another key variable that affects treatment strategy is risk stratification of patients into standard and high-risk MM. High-risk MM is defined by the presence of t(4;14), t(14;16), t(14;20), gain 1q, del(17p), or p53 mutation. In this paper, we provide algorithms for the treatment of newly diagnosed and relapsed MM based on the best available evidence. We have relied on data from randomized controlled trials whenever possible, and when appropriate trials to guide therapy are not available, our recommendations reflect best practices based on non-randomized data, and expert opinion. Each algorithm has been designed to facilitate easy decision-making for practicing clinicians. In all patients, clinical trials should be considered first, prior to resorting to the standard of care algorithms we outline.

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Metadata

Authors
Rajkumar, S. V., & Kumar, S.
Venue
Blood Cancer Journal
Published
2020-09-28
Volume
10
Issue
9
Pages
94-94
ISSN
2044-5385 (ISSN-L)
OpenAlex
W3091008757
Cited by
355
References
99
FWCI
25.79
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Multiple myeloma current treatment algorithms. https://app.cochat.ai/papers/10.1038/s41408-020-00359-2
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Rajkumar, S. V., & Kumar, S. (2020). Multiple myeloma current treatment algorithms. Blood Cancer Journal. https://doi.org/10.1038/s41408-020-00359-2

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.