64th ASH Annual Meeting and Exposition: 803. Emerging Tools, Techniques and Artificial Intelligence in Hematology: Clinical Implication of Artificial Intelligence in Precision Hematology

Symposia: Emerging Tools, Techniques and Artificial Intelligence in Hematology Oral and Poster Abstracts
Oral

Research, Lymphoid Leukemias, ALL, Acute Myeloid Malignancies, AML, artificial intelligence (AI), Translational Research, Lymphomas, bioinformatics, Diseases, aggressive lymphoma, Lymphoid Malignancies, computational biology, Myeloid Malignancies, emerging technologies, Technology and Procedures, profiling, imaging, machine learning, molecular testing

391-392 (Ernest N. Morial Convention Center)

Moderators:

Roni Shouval, MD, PhD, Memorial Sloan Kettering Cancer Center and Taiga Nishihori, MD, Moffitt Cancer Center

Disclosures:

Shouval: MyBiotics: Consultancy; Medexus: Consultancy, Ended employment in the past 24 months.

10:30 AM

787

Machine Learning Algorithm Correctly Identifies 95% of Cells in Differential Count of Blood Smears: A Prospective Study on >29,000 Cases and >17 Million Single Cells

Torsten Haferlach, MD, Niroshan Nadarajah^*, Claudia Haferlach, MD, Wolfgang Kern, MD and Christian Pohlkamp, MD^*

MLL Munich Leukemia Laboratory, Munich, Germany

10:45 AM

788

Prediction of Initial Risk Group, B/T Subtype, and ETV6-RUNX1 Translocation in Pediatric Acute Lymphoblastic Leukemia By Deep Convolutional Neural Network Analysis of Giemsa-Stained Whole Slide Images

Gil Shamai¹^*, Ran Schley¹^*, Yoav Binenbaum²^*, Ron Kimmel¹^* and Ronit Elhasid, MD³

¹Technion - Israel Institute of Technology, Haifa, Israel
²Dana farber cancer institute, Boston
³Pediatric Hemato-Oncology Department, Sourasky Medical Center, Tel Aviv, Israel

11:00 AM

789

Regularized Mixture Cure Models Identify a Gene Signature That Improves Risk Stratification within the Favorable-Risk Group in 2017 European Leukemianet (ELN) Classification of Acute Myeloid Leukemia (Alliance 152010)

Kellie J. Archer, PhD¹^*, Han Fu, PhD¹^*, Krzysztof Mrózek, MD, PhD², Deedra Nicolet, MS^3,4, Jessica Kohlschmidt, PhD^4,5, Alice S. Mims, MD^6,7, Geoffrey L. Uy, MD⁸, Wendy Stock, MD^9,10, John C. Byrd, MD¹¹ and Ann-Kathrin Eisfeld, MD³

¹Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH
²Clara D. Bloomfield Center for Leukemia Outcomes Research, The Ohio State University Comprehensive Cancer Center, Columbus, OH
³Clara D. Bloomfield Center for Leukemia Outcomes Research, The Ohio State University, Columbus, OH
⁴Alliance Statistics and Data Management Center, The Ohio State University Comprehensive Cancer Center, Columbus, OH
⁵Clara D. Bloomfield Center for Leukemia Outcomes Research, The Ohio State University Comprehensive Cancer Center, Columbus
⁶Division of Hematology, Department of Internal Medicine, The Ohio State University, Columbus, OH
⁷Arthur G. James Cancer Center Hospital, The Ohio State University Comprehensive Cancer Center, Columbus, OH
⁸Division of Oncology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO
⁹University of Chicago, Chicago, IL
¹⁰Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL
¹¹University of Cincinnati College of Medicine, Cincinnati, OH

11:15 AM

790

Evaluation of a Transparent Artificial Intelligence (AI) Disease Classification System with Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS) Data in a Prospective Study with 325 Cases

Niroshan Nadarajah^*, Sven Maschek^*, Stephan Hutter, PhD^*, Manja Meggendorfer, PhD, Wolfgang Kern, MD, Claudia Haferlach, MD and Torsten Haferlach, MD

MLL Munich Leukemia Laboratory, Munich, Germany

11:30 AM

791

Multi-Omics Data Integration of Genomics and RNA-Splicing in Myeloid Neoplasia

Arda Durmaz¹^*, Carmelo Gurnari, MD¹, Courtney E. Hershberger, PhD², Simona Pagliuca, MD, PhD³, Hassan Awada, MD⁴^*, Hussein Awada¹^*, Minako Mori, MD, PhD⁵^*, Yasuo Kubota, MD, PhD¹^*, Tariq Kewan, MD¹^*, Waled Bahaj, MD¹, John Barnard, PhD²^*, Jacob Scott, MD¹^*, Richard A Padgett, PhD⁶^*, Torsten Haferlach, MD⁷, Jaroslaw P. Maciejewski, MD, PhD, FACP¹ and Valeria Visconte, PhD¹

¹Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic Foundation, Cleveland, OH
²Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH
³Service d'Hématologie Clinique, Hôpital Brabois, CHRU de Nancy, Nancy, France
⁴Roswell Park Comprehensive Cancer Center, Buffalo, NY
⁵Department of Hematology and Oncology, Kyoto University, Kyoto, Japan
⁶Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Foundation, Cleveland, OH
⁷MLL Munich Leukemia Laboratory, Munich, Germany

11:45 AM

792

Prediction of Poor Outcome after Tisagenlecleucel in Patients with Relapsed or Refractory Diffuse Large B Cell Lymphoma (DLBCL) Using Artificial Intelligence Analysis of Pre-Infusion PET/CT

Margot Jak¹^*, Bas H.M. van der Velden, PhD²^*, Bart de Keizer, MD PhD³^*, Sjoerd G Elias, MD PhD⁴^*, Monique C. Minnema, MD, PhD⁵ and Kenneth G.A. Gilhuijs, PhD²^*

¹Department of Hematology, University Medical Center Utrecht, Utrecht, Netherlands
²Image Sciences Institute, The University Medical Center Utrecht, Utrecht, Netherlands
³Radiology, The University Medical Center Utrecht, Utrecht, Netherlands
⁴Julius Center for Health Sciences and Primary Care, The University Medical Center Utrecht, Utrecht, Netherlands
⁵Department of Hematology, University Medical Center, Utrecht, Netherlands, Utrecht, Netherlands

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^*signifies non-member of ASH

803.O1.6 803. Emerging Tools, Techniques and Artificial Intelligence in Hematology: Clinical Implication of Artificial Intelligence in Precision Hematology