Better Leukemia Diagnostics Through AI (BELUGA)

NCT04466059 | Recruiting DMC

• 1 other identifier: MLL_001
• Study Type: observational
• Target: 25,000
• Locations: 1 country
• Sites: 1

Brief Summary

To the best of our knowledge, BELUGA will be the first prospective trial investigating the usefulness of deep learning-based hematologic diagnostic algorithms. Taking advantage of an unprecedented collection of diagnostic samples consisting of flow cytometry datapoints and digitalized blood-smears, categorization of yet undiagnosed patient samples will prospectively be compared to current state-of-the-art diagnosis at the Munich Leukemia Laboratory (hereafter MLL). In total, a collection of 25,000 digitalized blood smears and 25,000 flow cytometry datapoints will be prospectively used to train an AI-based deep neuronal network for correct categorization. Subsequently, the superiority will be challenged for the primary endpoints: sensitivity and specificity of diagnosis, most probable diagnosis, and time to diagnose. The secondary endpoints will compare the consequences regarding further diagnostic work-up and, thus, clinical decision making between routine diagnosis and AI guided diagnostics. BELUGA will set the stage for the introduction of AI-based hematologic diagnostics in a real-world setting.

Conditions

Hematologic Malignancy; Leukemia; Minimal Residual Disease; Lymphoma; Blood Cancer

Keywords

hematology; laboratory medicine; AI-based diagnostics; artificial intelligence; deep neuronal networks

Outcome Measures

Primary Outcomes (1)

• Sensitivity and Specificity of AI Guided diagnostics in Hematology

  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 Outcomes (4)

• comparison of clinical consequences

  08-01-2020 until 07-31-2021

• predictive diagnostic value

  08-01-2020 until 07-31-2021

• turn-around-time

  08-01-2020 until 07-31-2021

• enumerate entity-specific benchmarks (e.g., blast count in leukemia) count)

  08-01-2020 until 07-31-2021

Interventions

Automated AI-Guided Diagnosis of Hematological Malignancies DIAGNOSTIC_TEST

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

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The training cohort of BELUGA consists of 50,000 annotated samples for which cytomorphological smears (25,000 samples) and immunophenotyping (25,000 samples) data points have been collected. This cohort serves as a foundation for the DNN to perform training. Our test cohort will consist of all samples for which cytomorphology and immunophenotyping will be performed for one year.

You may qualify if:

• 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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Munich Leukemia Laboratory: lead

Study Sites (1)

MLL Munich Leukemia Laboratory

Munich, Bavaria, 81377, Germany

RECRUITING

Related Publications (1)

• 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.

  PMID: 32519455 RESULT

Related Links

• Related Info
• WHO Classification of hematological malignancies according to Swerdlow et al.
• Related Info
• Related Info

Biospecimen

Retention: SAMPLES WITH DNA

All samples used consist of bone marrow aspirates or peripheral blood sent to our diagnostic laboratory for routine hematological testing.

MeSH Terms

Conditions

Hematologic Neoplasms; Leukemia; Neoplasm, Residual; Lymphoma

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Hematologic Diseases; Hemic and Lymphatic Diseases; Neoplasms by Histologic Type; Neoplastic Processes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Lymphoproliferative Disorders; Lymphatic Diseases; Immunoproliferative Disorders; Immune System Diseases

Study Officials

• Wolfgang Kern, Prof. Dr.

  MLL Munich Leukemia Laboratory

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Adam Wahida, MD

CONTACT

+49 (0)89 99017 338; adam.wahida@mll.com

Torsten Haferlach, Prof. Dr.Dr.

CONTACT

+49 (0)89 99017 100; torsten.haferlach@mll.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof. Dr. Dr.

Study Record Dates

• First Submitted: July 6, 2020
• First Posted: July 10, 2020
• Study Start: January 5, 2020
• Primary Completion: July 31, 2025
• Study Completion: July 31, 2025
• Last Updated: December 17, 2024

Record last verified: 2024-12

Locations

DE (1)