Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
RECRUITING
25000 participants
OBSERVATIONAL
2020-01-05
2025-07-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Specifically, the use of artificial intelligence for pattern recognition showed that DNN could categorize complex and composite data points, chiefly images, with high fidelity to a specific pathogenic condition or disease. The majority of these studies are primarily based on extensive training sample collections that were categorized a priori. Subsequently, this "training" provided the necessary input to classify newly delivered specimens into the correct subgroups, frequently even outperforming independent human investigators. So far, these studies have thus provided the rationale for the use of DNN in real-world diagnostics. However, the prerequisite for using DNN in a real-world setting, where specimen sampling and analysis would need to outperform human diagnosis prospectively, would be a blinded and prospective trial. Currently, there is a lack of prospective data, therefore still challenging the notion that DNN can outperform state-of-the-art human-based diagnostic algorithms. Here we want to investigate the validity and usefulness of AI-based diagnostic capabilities prospectively in a real-world setting.
Hematologic diagnostics heavily rely on multiple methodically distinct approaches, of which phenotyping aberrant blood or bone marrow cells from affected patients represents a cornerstone for all subsequent methods, such as chromosomal or molecular genetic analyses. At the MLL, five different diagnostic pillars are required to provide diagnostic evidence for a specific malignant blood disorder faithfully: cytomorphology and immunophenotyping first, guiding more specific methods such as cytogenetics, FISH, and a diversity of molecular genetic assays.
+++ Objectives +++
Phenotyping of blood cells is primarily based on two distinct challenges; (1) the morphological appearance and abundance of specific cell types and (2) the presence of particular lineage markers detected by flow cytometry. These two methods are critical for each subsequent decision-making process and, thus ultimately, the final diagnosis. Simultaneously, these two methods are ideally suited for automated analysis by DNN due to their inherent image-based nature. This has been recently illustrated by a publication by Marr and colleagues (Matek et al., 2019; https://doi.org/10.1038/s42256-019-0101-9)
In BELUGA, we want to investigate whether the automated analysis of blood (from peripheral blood and bone marrow aspirates) smears and flow-cytometry-based analyses can provide a benefit for diagnostic quality and, ultimately, patient care. Moreover, BELUGA will provide evidence for the cooperative nature of image-based diagnostic tools for other pillars of hematologic diagnostic decision making such as genetic and molecular genetic characterization.
BELUGA, therefore, consists of three parts (A-C) (See Figure in the attached File). In A, we want to train a DNN with an unprecedented collection of blood smears and flow-cytometry-based data points collected during the course of 15 years. These samples consist of all hematological malignancies currently identified and recognized by the current WHO classification for hematologic malignancies. Due to the varying incidences of these entities, the total number of training items varies from 1,000 to 20,000 for 15 years. However, we deem this discrepancy a benefit to this trial's overall aims, because this diverse spectrum will inform us on the number of training items needed for outperforming the state-of-the-art diagnostics in cytomorphology or flow cytometry.
In part B, we will compare the overall performance of our trained DNN prospectively to new yet undiagnosed samples arriving at our laboratory (see the main section for details). The superiority of DNN based categorization will be challenged based on the pre-defined outcome parameters accuracy with respect to state-of-the-art diagnostics, mismatch-rate, and time needed to provide a diagnostic probability.
Lastly, in C, we will investigate the effects on faster and more accurate diagnostic power by leveraging our trained DNN to aid downstream diagnostic methodologies such as chromosomal analysis or panel sequencing of patient samples.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Keywords
Explore important study keywords that can help with search, categorization, and topic discovery.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
CASE_CONTROL
PROSPECTIVE
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
Automated AI-Guided Diagnosis of Hematological Malignancies
In BELUGA, we want to investigate whether the automated analysis of blood (from peripheral blood and bone marrow aspirates) smears and flow-cytometry-based analyses can provide a benefit for diagnostic quality and, ultimately, patient care.
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* The suspected diagnoses constitute a primary diagnosis
* Only samples of patients min.18 years of age will be used
Exclusion Criteria
* Samples with too scarce material jeopardizing routine gold-standard diagnosis will be excluded.
* Bone marrow aspirates without sufficient material to assess malignant or healthy hematopoiesis.
18 Years
ALL
Yes
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Munich Leukemia Laboratory
INDUSTRY
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Torsten Haferlach
Prof. Dr. Dr.
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Wolfgang Kern, Prof. Dr.
Role: PRINCIPAL_INVESTIGATOR
MLL Munich Leukemia Laboratory
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
MLL Munich Leukemia Laboratory
Munich, Bavaria, Germany
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
Adam Wahida, MD
Role: primary
Torsten Haferlach, Prof.Dr.Dr.
Role: backup
References
Explore related publications, articles, or registry entries linked to this study.
Zhao M, Mallesh N, Hollein A, Schabath R, Haferlach C, Haferlach T, Elsner F, Luling H, Krawitz P, Kern W. Hematologist-Level Classification of Mature B-Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data. Cytometry A. 2020 Oct;97(10):1073-1080. doi: 10.1002/cyto.a.24159. Epub 2020 Jun 9.
Provided Documents
Download supplemental materials such as informed consent forms, study protocols, or participant manuals.
Document Type: Study Protocol and Statistical Analysis Plan
Related Links
Access external resources that provide additional context or updates about the study.
Related Info
WHO Classification of hematological malignancies according to Swerdlow et al.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
MLL_001
Identifier Type: -
Identifier Source: org_study_id