A geno-clinical decision model for the diagnosis of myelodysplastic syndromes
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- Nathan Radakovich
- Leukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH;
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- Manja Meggendorfer
- MLL Munich Leukemia Laboratory, Munich, Bavaria, Germany;
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- Luca Malcovati
- Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy;
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- C. Beau Hilton
- Leukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH;
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- Mikkael A. Sekeres
- Division of Hematology, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL;
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- Jacob Shreve
- Department of Internal Medicine, Cleveland Clinic, Cleveland, OH;
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- Yazan Rouphail
- College of Arts and Sciences, The Ohio State University, Columbus, OH;
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- Wencke Walter
- Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy;
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- Stephan Hutter
- Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy;
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- Anna Galli
- Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy;
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- Sara Pozzi
- Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy;
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- Chiara Elena
- Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy;
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- Eric Padron
- Department of Malignant Hematology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL;
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- Michael R. Savona
- Department of Medicine and
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- Aaron T. Gerds
- Leukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH;
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- Sudipto Mukherjee
- Leukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH;
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- Yasunobu Nagata
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH
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- Rami S. Komrokji
- Department of Malignant Hematology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL;
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- Babal K. Jha
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH
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- Claudia Haferlach
- Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy;
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- Jaroslaw P. Maciejewski
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH
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- Torsten Haferlach
- MLL Munich Leukemia Laboratory, Munich, Bavaria, Germany;
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- Aziz Nazha
- Leukemia Program, Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH;
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説明
<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>
収録刊行物
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- Blood Advances
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Blood Advances 5 (21), 4361-4369, 2021-10-29
American Society of Hematology
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詳細情報 詳細情報について
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- CRID
- 1360857593791601152
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- ISSN
- 24739537
- 24739529
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- 資料種別
- journal article
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- データソース種別
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- Crossref
- KAKEN