Clinical trial NCT04466059: Better Leukemia Diagnostics Through AI (BELUGA)
Official title: A Case-Control Study To Determine The Suitability Of Artificial Intelligence For Leukemia Diagnostics
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Hematologic Malignancy
Leukemia
Minimal Residual Disease
Lymphoma
Blood Cancer
Keywords: hematology
laboratory medicine
AI-based diagnostics
artificial intelligence
deep neuronal networks
Lead sponsor: Munich Leukemia Laboratory
Sponsor class: INDUSTRY
First posted: 2020-07-10
Enrollment: 25000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
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.

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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Sensitivity and Specificity of AI Guided diagnostics in Hematology
timeFrame: 08-01-2020 until 07-31-2021
Secondary outcomes:
measure: comparison of clinical consequences
timeFrame: 08-01-2020 until 07-31-2021
measure: predictive diagnostic value
timeFrame: 08-01-2020 until 07-31-2021
measure: turn-around-time
timeFrame: 08-01-2020 until 07-31-2021
measure: enumerate entity-specific benchmarks (e.g., blast count in leukemia) count)
timeFrame: 08-01-2020 until 07-31-2021
Other outcomes:
Locations:
city: Munich
country: Germany
facility: MLL Munich Leukemia Laboratory
state: Bavaria
Study officials:
affiliation: MLL Munich Leukemia Laboratory
name: Wolfgang Kern, Prof. Dr.
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.