Jan-Niklas Eckardt
Overview
Explore the profile of Jan-Niklas Eckardt including associated specialties, affiliations and a list of published articles.
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23
Citations
351
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Recent Articles
1.
Stasik S, Eckardt J, Rollig C, Baldus C, Platzbecker U, Serve H, et al.
Br J Haematol
. 2025 Mar;
PMID: 40041934
IKZF1 mutations are recurrent alterations in acute myeloid leukaemia (AML), and hotspot point mutation, N159S, has recently been associated with unique gene expression and adverse risk. To better understand the...
2.
Griesshammer M, Al-Ali H, Eckardt J, Fiegl M, Gothert J, Jentsch-Ullrich K, et al.
Ann Hematol
. 2025 Jan;
104(1):295-306.
PMID: 39888352
The prefibrotic phase of primary myelofibrosis (pre-PMF) represents a distinct subentity within the spectrum of myeloproliferative neoplasms (MPNs), recognized by the World Health Organization (WHO) and the International Consensus Classification...
3.
Kerle I, Gross T, Kogler A, Arnold J, Werner M, Eckardt J, et al.
NPJ Precis Oncol
. 2025 Jan;
9(1):9.
PMID: 39794422
Precision oncology offers new cancer treatment options, yet sequencing methods vary in type and scope. In this study, we compared whole-exome/whole-genome (WES/WGS) and transcriptome sequencing (TS) with broad panel sequencing...
4.
Ruhnke L, Bill M, Zukunft S, Eckardt J, Schafer S, Stasik S, et al.
Blood Adv
. 2024 Nov;
PMID: 39504561
In 2022, the European LeukemiaNet (ELN) risk stratification for patients with AML has been updated. We aimed to validate the prognostic value of the 2022 ELN classification (ELN22) evaluating 1,570...
5.
Hahn W, Ahmadi N, Hoffmann K, Eckardt J, Sedlmayr M, Wolfien M
Stud Health Technol Inform
. 2024 Aug;
316:1472-1476.
PMID: 39176482
This study advances the utility of synthetic study data in hematology, particularly for Acute Myeloid Leukemia (AML), by facilitating its integration into healthcare systems and research platforms through standardization into...
6.
Ahmadi N, Zoch M, Guengoeze O, Facchinello C, Mondorf A, Stratmann K, et al.
Orphanet J Rare Dis
. 2024 Aug;
19(1):298.
PMID: 39143600
Background: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be...
7.
Ma J, Xie R, Ayyadhury S, Ge C, Gupta A, Gupta R, et al.
Nat Methods
. 2024 Mar;
21(6):1103-1113.
PMID: 38532015
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters...
8.
Truhn D, Eckardt J, Ferber D, Kather J
NPJ Precis Oncol
. 2024 Mar;
8(1):72.
PMID: 38519519
The technological progress in artificial intelligence (AI) has massively accelerated since 2022, with far-reaching implications for oncology and cancer research. Large language models (LLMs) now perform at human-level competency in...
9.
Eckardt J, Hahn W, Rollig C, Stasik S, Platzbecker U, Muller-Tidow C, et al.
NPJ Digit Med
. 2024 Mar;
7(1):76.
PMID: 38509224
Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation...
10.
Eckardt J, Stasik S, Rollig C, Petzold A, Sauer T, Scholl S, et al.
Leukemia
. 2023 Oct;
37(12):2395-2403.
PMID: 37833543
Genetic lesions of IKZF1 are frequent events and well-established markers of adverse risk in acute lymphoblastic leukemia. However, their function in the pathophysiology and impact on patient outcome in acute...