A geno-clinical decision model for the diagnosis of myelodysplastic syndromes-Reference-Cited by-同舟云学术

A geno-clinical decision model for the diagnosis of myelodysplastic syndromes

Published:2021-10-29 Issue:21 Volume:5 Page:4361-4369
ISSN:2473-9529
Container-title:Blood Advances
language:en
Short-container-title:

Author:

Radakovich Nathan¹²,Meggendorfer Manja³,Malcovati Luca⁴^ORCID,Hilton C. Beau¹²^ORCID,Sekeres Mikkael A.⁵,Shreve Jacob⁶,Rouphail Yazan⁷^ORCID,Walter Wencke⁴^ORCID,Hutter Stephan⁴,Galli Anna⁴^ORCID,Pozzi Sara⁴,Elena Chiara⁴,Padron Eric⁸,Savona Michael R.⁹¹⁰^ORCID,Gerds Aaron T.¹^ORCID,Mukherjee Sudipto¹,Nagata Yasunobu¹¹,Komrokji Rami S.⁸^ORCID,Jha Babal K.¹¹^ORCID,Haferlach Claudia⁴,Maciejewski Jaroslaw P.¹¹,Haferlach Torsten³,Nazha Aziz¹

Affiliation:

1. Leukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH;

2. Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH;

3. MLL Munich Leukemia Laboratory, Munich, Bavaria, Germany;

4. Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy;

5. Division of Hematology, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL;

6. Department of Internal Medicine, Cleveland Clinic, Cleveland, OH;

7. College of Arts and Sciences, The Ohio State University, Columbus, OH;

8. Department of Malignant Hematology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL;

9. Department of Medicine and

10. Department of Pediatrics, Program in Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN; and

11. Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH

Abstract

Abstract The differential diagnosis of myeloid malignancies is challenging and subject to interobserver variability. We used clinical and next-generation sequencing (NGS) data to develop a machine learning model for the diagnosis of myeloid malignancies independent of bone marrow biopsy data based on a 3-institution, international cohort of patients. The model achieves high performance, with model interpretations indicating that it relies on factors similar to those used by clinicians. In addition, we describe associations between NGS findings and clinically important phenotypes and introduce the use of machine learning algorithms to elucidate clinicogenomic relationships.

Publisher

American Society of Hematology

Subject

Hematology

Link

https://ashpublications.org/bloodadvances/article-pdf/5/21/4361/1833378/advancesadv2021004755.pdf

Reference22 articles.

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