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Better Leukemia Diagnostics Through AI (BELUGA)



Januar 2020

Letztes Update:


Indikation (Clinical Trials):
Leukemia, Hematologic Neoplasms, Neoplasm, Residual


Erwachsene (18+)


Munich Leukemia Laboratory



Wolfgang Kern, Prof. Dr.
Principal Investigator
MLL Munich Leukemia Laboratory


Torsten Haferlach, Prof. Dr.Dr.
Phone: +49 (0)89 99017 100
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MLL Munich Leukemia Laboratory
81377 Munich
GermanyRekrutierend» Google-Maps
Adam Wahida, MD

Torsten Haferlach, Prof.Dr.Dr.
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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;

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


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.


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


- Bone marrow aspirates without sufficient material to assess malignant or healthy



Primary outcome:

1. 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.

Secondary outcome:

1. 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

2. 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.

3. 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.

4. 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.

Geprüfte Regime

  • 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.


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