Acute Myeloid Leukemia
Conditions
Keywords
Acute myeloid leukemia, Machine learning, Artificial intelligence, Risk stratification
Brief summary
The investigators will use machine learning to identify features on bone marrow smears and select features that are related to gene mutations, gene expression, or prognosis. The investigators will then use genome-wide transcriptomic profiling to investigate gene expression that is associated with patients' outcomes. The investigators will design a next-generation sequencing panel with unique molecular index and assess its feasibility and robustness in detecting measurable residual disease and optimize the panel/platform/bioinformatic pipeline. Finally, The investigators will use machine learning to integrate bone marrow smear features, gene mutations, gene expression, and measurable residual disease to construct a comprehensive risk assessment system that is based on multi-omics data. The investigators believe that such a platform will help physicians to design the most appropriate treatment strategies for individual patients, not only advancing the concept of precision medicine but also improving patients' prognoses.
Interventions
None listed
Sponsors
National Taiwan University Hospital
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Diagnosed with acute myeloid leukemia at the National Taiwan University Hospital 2. Ever enrolled in 201802021RINC、202109078RINB、201709072RINC
Exclusion criteria
Nil
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Overall survival | From date of diagnosis until the date of death from any cause, assessed up to 30 years |
Countries
Taiwan
Outcome results
None listed