Flatiron Unveils Six AI-Driven Hematology Datasets to Enhance Blood Cancer Real-World Evidence Research

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Health unveils expansive new hematology datasets powered by AI, promising to revolutionize blood cancer research and patient care.">

October 30, 2025

New York, NY – A Significant advancement in teh fight against blood cancers has been announced as Flatiron Health, a prominent health technology firm, launched six new hematology Panoramic datasets. This initiative represents a major step towards improving cancer care through the utilization of real-world data and artificial intelligence.

Expanding the Scope of Hematology Research

The newly released Panoramic datasets encompass records from over 505,000 patients diagnosed with five different subtypes of B-cell lymphoma and multiple myeloma.This substantially expands upon Flatiron’s previous hematology collections, marking a six-fold increase in cohort size. The extensive data, totaling 1.5 billion data points, reflects real-world clinical practices across various healthcare settings.

the data’s depth includes critical details such as measurable residual disease (MRD) testing and CAR-T therapy utilization – both key indicators in modern hematology.This comprehensive information empowers researchers to study treatment patterns, patient adherence, and overall outcomes with increased precision.

AI and Machine Learning at the Forefront

nathan Hubbard, Chief Executive Officer of Flatiron Health, emphasized that these datasets are the result of a decade-long commitment to establishing a robust oncology evidence infrastructure. He stated that combining the company’s AI and machine learning expertise with rigorous scientific validation will enhance personalized care for individuals battling blood cancers. The datasets also feature improved interoperability, streamlining analyses across different lymphoma types and tracking disease progression over time.

Investigating Complex Subgroups

Flatiron’s hematology program facilitates in-depth investigations into complex cases, including high-grade B-cell lymphomas with specific genetic rearrangements and multiple myeloma cases presenting with high-risk genetic profiles. Crucially, the data aids in assessing the real-world effectiveness of treatments, as well as monitoring molecular responses and adverse events – providing complimentary insight to customary clinical trials.

Did You Know? The use of Real-World Evidence (RWE) in pharmaceutical development is increasing,with the FDA actively promoting its use to supplement clinical trial data.

Shaping the Future of Digital Oncology

With over 250 publications already and another 275 research presentations planned for global conferences in 2025, Flatiron Health is actively shaping the standards for digital oncology evidence. the company’s presence at prominent events like ISPOR Europe and ASH 2025 highlights its commitment to responsibly leveraging artificial intelligence and real-world data to improve access to scientific insights, address data gaps, and drive innovation in hematology.

Key Dataset Feature	Details
Total Patient Records	Over 505,000
Data Points	1.5 Billion
Disease Focus	B-cell Lymphomas (5 subtypes) & Multiple Myeloma
Key Data Elements	MRD testing,CAR-T therapy utilization,treatment patterns,genetic profiles

pro Tip: Researchers can use these datasets to identify underserved patient populations and design more inclusive clinical trials.

This advancement could lead to earlier diagnoses, personalized treatment plans, and ultimately, improved outcomes for individuals impacted by blood cancers. What impact do you believe this increased access to comprehensive data will have on the development of novel therapies? And how will these datasets influence the future of clinical trial designs?

Understanding Real-World evidence (RWE)

Real-world evidence, gathered from sources like electronic health records, claims databases, and patient registries, is becoming increasingly vital in medical research. Unlike data from tightly controlled clinical trials, RWE reflects real-life clinical practice and provides a more holistic picture of a patient’s experience. According to a 2024 report by the FDA,RWE can be used to support regulatory decisions,complement clinical trial findings,and accelerate the development of new treatments. The growing prominence of RWE is transforming the landscape of healthcare research and delivery.

Frequently Asked Questions About Hematology Datasets

• What is a hematology dataset? A hematology dataset is a collection of data related to blood cancers, including patient characteristics, diagnoses, treatments, and outcomes.
• How does AI improve hematology research? AI algorithms can analyze large datasets to identify patterns and insights that would be difficult or unachievable for humans to detect.
• What is the significance of MRD testing in hematology? Measurable residual disease (MRD) testing helps determine how much cancer remains in the body after treatment, which is crucial for predicting relapse.
• What is CAR-T therapy? CAR-T therapy is a type of immunotherapy that uses genetically engineered immune cells to fight cancer.
• how does Flatiron Health ensure data quality? Flatiron Health utilizes a validated data quality framework to ensure the accuracy and reliability of its datasets.
• What are the ethical considerations of using patient data for research? Patient privacy and data security are paramount. Flatiron Health employs robust measures to protect sensitive information and adheres to all relevant regulations.
• What is “real-world evidence”? Real-world evidence (RWE) is data collected outside of traditional clinical trials,reflecting routine clinical practice and patient experiences.

Share your thoughts on this groundbreaking development in the comments below. Don’t forget to share this article with your network to raise awareness of the potential benefits of AI-powered hematology research!

how can these datasets be used to develop predictive models for treatment response in AML patients, considering genetic mutations?

Accelerating Blood Cancer Research with Real-World Data (RWD)

Flatiron Health has recently announced the release of six novel, AI-driven datasets focused on hematological malignancies – cancers of the blood, bone marrow, and lymphatic system. This initiative represents a notable leap forward in real-world evidence (RWD) research, offering researchers unprecedented access to thorough, longitudinal data to improve understanding, diagnosis, and treatment of blood cancers like leukemia, lymphoma, and myeloma. These datasets are designed to overcome conventional barriers in hematology research, such as data fragmentation and limited access to patient-level data.

Understanding the New Hematology Datasets

The six datasets cover a range of hematologic malignancies and are built upon FlatironS robust oncoemr® platform, a leading electronic health record (EHR) system used by a large network of community and academic oncology practices. Key features include:

* Acute Myeloid Leukemia (AML): Detailed data on diagnostic workup, genetic mutations, treatment regimens (including intensive chemotherapy and targeted therapies), and outcomes.

* Chronic Lymphocytic Leukemia (CLL): Longitudinal data tracking disease progression, treatment response, and minimal residual disease (MRD) status.

* Diffuse Large B-Cell Lymphoma (DLBCL): Comprehensive information on staging, prognostic indices (like IPI), treatment approaches (R-CHOP, novel agents), and patterns of relapse.

* Follicular Lymphoma (FL): Data capturing treatment sequences, response to therapy, and transformation to more aggressive lymphomas.

* Multiple Myeloma: Detailed records of disease stage, cytogenetic abnormalities, treatment lines (including proteasome inhibitors, immunomodulatory drugs, and stem cell transplant), and survival outcomes.

* Myelodysplastic Syndromes (MDS): Information on risk stratification (IPSS-R), treatment strategies, and progression to acute leukemia.

These datasets aren’t simply raw data dumps. Flatiron leverages artificial intelligence (AI) and machine learning (ML) to standardize and harmonize the data, addressing inconsistencies inherent in EHR systems. This process enhances data quality and usability for researchers.

The power of AI in Hematology Data Analysis

The integration of AI is crucial. Here’s how it’s being applied:

1. Natural language Processing (NLP): Extracting key clinical information from unstructured text within physician notes, pathology reports, and radiology reports.This unlocks valuable insights that would or else be inaccessible.
2. Data Harmonization: Standardizing terminology and coding across different institutions to ensure data comparability. such as, converting various ways of documenting “disease stage” into a unified format.
3. Phenotype Definition: Using AI to identify patient subgroups based on complex combinations of clinical and genomic characteristics. this allows for more targeted research and personalized medicine approaches.
4. Predictive Modeling: Developing algorithms to predict treatment response, risk of relapse, and overall survival based on patient data.

Benefits for Researchers and Patients

These datasets offer significant benefits across the cancer research landscape:

* Accelerated Drug Growth: Identifying potential drug targets and biomarkers, and optimizing clinical trial design. Clinical trial enrichment becomes more feasible with refined patient stratification.

* improved Treatment Strategies: Understanding which treatments are most effective for specific patient populations, leading to more personalized care.

* Enhanced Understanding of Disease Progression: Tracking disease evolution over time and identifying factors that influence outcomes.

* Reduced Healthcare Costs: Optimizing treatment pathways and reducing unnecessary interventions.

* Real-World Outcomes Research: evaluating the effectiveness of treatments in routine clinical practice, complementing data from randomized controlled trials. This is notably crucial for rare cancers where large-scale trials are challenging.

Practical Applications & Case Studies (Illustrative)

While specific published case studies directly utilizing these new datasets are still emerging (as of late 2025),the potential is clear. Consider these illustrative examples based on Flatiron’s previous RWD work:

* Identifying Biomarkers for Relapsed/Refractory DLBCL: Researchers could use the DLBCL dataset,combined with genomic data,to identify biomarkers that predict response to CAR-T cell therapy.

* Optimizing Treatment Sequencing in Multiple Myeloma: Analyzing the myeloma dataset could reveal optimal sequences of treatment lines to maximize patient survival and quality of life.

* Predicting Risk of Transformation in Follicular Lymphoma: Using AI to identify patients at high risk of transformation to aggressive lymphoma, allowing for more proactive monitoring and intervention.

Data Access and Security

Flatiron emphasizes responsible data stewardship. Access to these datasets is granted through a rigorous review process, ensuring compliance with privacy regulations (HIPAA) and ethical guidelines. Data is de-identified to protect patient confidentiality. Researchers typically access the data through Flatiron’s secure cloud-based platform,utilizing tools for data analysis and visualization. data use agreements are standard practice.

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