- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04466059
Better Leukemia Diagnostics Through AI (BELUGA) (BELUGA)
A Case-Control Study To Determine The Suitability Of Artificial Intelligence For Leukemia Diagnostics
Study Overview
Status
Intervention / Treatment
Detailed Description
In numerous recent studies, deep neuronal networks (DNN) have been leveraged to examine the usefulness of artificial intelligence (AI)-based DNN for diagnostic purposes. In essence, they have successfully proved to recapitulate state-of-the-art diagnoses currently performed by humans.
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.
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Adam Wahida, MD
- Phone Number: +49 (0)89 99017 338
- Email: adam.wahida@mll.com
Study Contact Backup
- Name: Torsten Haferlach, Prof. Dr.Dr.
- Phone Number: +49 (0)89 99017 100
- Email: torsten.haferlach@mll.com
Study Locations
-
-
Bavaria
-
Munich, Bavaria, Germany, 81377
- Recruiting
- MLL Munich Leukemia Laboratory
-
Contact:
- Adam Wahida, MD
- Email: adam.wahida@mll.com
-
Contact:
- Torsten Haferlach, Prof.Dr.Dr.
- Email: torsten.haferlach@mll.com
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients having been diagnosed with a suspected hematological disorder
- The suspected diagnoses constitute a primary diagnosis
- Only samples of patients min.18 years of age will be used
- Samples must suffice quality attributes which are denoted in "Exclusion Criteria"
Exclusion Criteria:
- The sample is not fit for state-of-the-art diagnosis or fails initial quality control. For quality insurance, we will exclude samples in heparin- instead of EDTA. Samples with damage due to atmospheric reasons (freeze-thaw damage or elevated temperature) will be excluded.
- 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.
Study Plan
How is the study designed?
Design Details
- Observational Models: Case-Control
- Time Perspectives: Prospective
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Sensitivity and Specificity of AI Guided diagnostics in Hematology
Time Frame: 08-01-2020 until 07-31-2021
|
As a primary endpoint, we will examine the ability of DNN to classify disorders according to (after initial assessment disease/healthy) to the gold-standard diagnosis. The gold-standard diagnosis is defined as an integrated diagnosis, including cytomorphology, flow cytometry, cytogenetics, FISH, and molecular genetics. DNN will independently provide a bi-directional (probabilistic) diagnosis, with the most probable diagnosis. The primary analysis will include a direct comparison between the human cytomorphological examination and the pattern recognition software. Secondly, this result will be provided to downstream diagnostic departments to assess phenotypic diagnosis's usefulness for genetic characterization. We hypothesize that the turn-around time will be significantly enhanced, further providing quality at sooner timepoint. |
08-01-2020 until 07-31-2021
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
comparison of clinical consequences
Time Frame: 08-01-2020 until 07-31-2021
|
We will compare the clinical recommendation obtained after routine gold-standard diagnostics and after AI-guided categorization of all samples enrolled in this study
|
08-01-2020 until 07-31-2021
|
predictive diagnostic value
Time Frame: 08-01-2020 until 07-31-2021
|
We will assess the predictive value of unsupervised categorization and diagnosis in comparison to gold-standard routine testing.
|
08-01-2020 until 07-31-2021
|
turn-around-time
Time Frame: 08-01-2020 until 07-31-2021
|
We will measure the turn-around-time of gold-standard diagnostics in comparison to AI-guided diagnosis.
|
08-01-2020 until 07-31-2021
|
enumerate entity-specific benchmarks (e.g., blast count in leukemia) count)
Time Frame: 08-01-2020 until 07-31-2021
|
We will assess secondary disease specific values determined by AI/DNN based unsupervised diagnosis versus routine testing.
|
08-01-2020 until 07-31-2021
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Wolfgang Kern, Prof. Dr., MLL Munich Leukemia Laboratory
Publications and helpful links
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- MLL_001
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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