Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation.
Sara ArabyarmohammadiPatrick LeoVidya Sankar ViswanathanAndrew JanowczykGermán CorredorPingfu FuHoward MeyersonLeland L MethenyAnant MadabhushiPublished in: JCO clinical cancer informatics (2022)
The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML/MDS.
Keyphrases
- acute myeloid leukemia
- machine learning
- cell therapy
- allogeneic hematopoietic stem cell transplantation
- free survival
- deep learning
- gene expression
- single cell
- dna damage
- transcription factor
- genome wide
- big data
- magnetic resonance imaging
- computed tomography
- dna methylation
- magnetic resonance
- cell proliferation
- acute lymphoblastic leukemia
- oxidative stress
- cell cycle arrest
- signaling pathway
- cell death