Validating Artificial Intelligencein Hematology
Our focus areas
ENHANCE
Diagnostic
capacity
EVALUATE
Treatment
alternatives
PREDICT
Disease outcomes and
treatment response
ESTIMATE
Drug
repurposing
Our use cases in detail
Myelodysplastic Syndromes
MDS
The disease
Myelodysplastic syndromes (MDS) are a group of bone marrow failure disorders that typically affect the elderly. Patients suffer from blood cytopenia (low blood cell counts), since their bone marrow is no longer able to produce enough healthy blood cells. The disease is also known as a form of blood cancer, and in some patients can evolve into acute myeloid leukemia (AML), which is usually fatal if not treated.
Clinical aims
Prevention based on genomic screening
Investigate factors that influence the development of MDS, enabling early-stage identification of individuals at risk
Omics-based classification and prognosis
Personalized predictive models through integration of comprehensive genomic and clinical information.
Omics-based clinical decision making
AI-based algorithms to stratify the individual probability of response to specific treatments
Drug repurposing
Build a rationale for drug repurposing in specific subsets of MDS
Multiple Myeloma
MM
The disease
Multiple Myeloma (MM) is a type of bone marrow cancer originating in plasma cells, a type of white blood cell responsible for producing antibodies to fight off infections. In patients with MM, cancerous plasma cells accumulate in the bone marrow and produce abnormal proteins instead, which can lead to decreased blood cell numbers, bone and kidney damage.
Clinical aims
Understand disease complexity
Describe the different layers of MM heterogeneity integrating baseline genomic and imaging data
Identify evolution dynamics
Define the quantitative and qualitative dynamics of the disease in time
Study risk progression
Develop a prognostic risk score for the baseline and the disease remaining after therapy
Integrate radiomics and radiogenomics
Develop and validate a model to predict treatment response and determine progression free survival
Sickle Cell Disease
SCD
The disease
Sickle Cell Disease (SCD) is a group of hereditary red blood cell disorders. It is a rare, chronic and life-threatening disease. In patients with SCD, red blood cells become C-shaped in resemblance to a sickle, the farming tool the disease is named after. Sickle cells die early and tend to clog the blood flow when going through small blood vessels, so patients usually suffer from low red blood cell counts, infections, acute chest syndrome and strokes.
Clinical aims
Identify gene mutations linked to inflammation markers
Correlations between genetic inflammatory risk profiles CRP level to develop high inflammation prediction models
AI allocation of patients to a sickling risk profile
Understand which genetic loci (GWAS) are associated with patient-specific blood rheology and the point of sickling
Develop a combined model to predict clinical outcome
Expressing renal damage with microalbuminuria as gold standard and other known genetic modifiers
AI-based radiomics
Build a probability score using AI-based brain MRI image analysis to predict silent infarction in young patients
Validation
Prevention based on Genomic ScreeningInvestigate factors that influence the development of MDS, enabling early-stage identification of individuals at risk. Omics-based Classification and PrognosisPersonalized predictive models through integration of comprehensive genomic and clinical information. Omics-based Clinical Decision MakingAI-based algorithms to stratify the individual probability of response to specific treatments. Drug RepurposingBuild a rationale for drug repurposing in specific subsets of MDS.
Baseline clinical data and whole genome Copy Number Alterations (CNAs) landscape data (as detected by SNPs array) from 1,000 patients. Baseline clinical and genomic data (coming from both BM clone and liquid biopsy) and imaging data (PET-CT and WB-MRI) at baseline, as well as molecular (NGS) and imaging (PET-CT and WB-MRI) minimal residual disease (MRD) data from 200 patients. Clinical and genomic data from 750 patients available from public repositories (1A12 release CoMMpass database). Data from 300 newly diagnosed patients who will receive induction therapy followed by high dose melphalan and lenalidomide maintenance.
MRI data file (DICOM, CTI, SIGNA etc.), Lorrca Oxygenscan raw data file (RTS,TXT), GWAS data file, laboratory parameters (RTS, XLS etc.), clinical parameters and baseline characteristics (RTS, XLS etc.)
