<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04466059</url>
  </required_header>
  <id_info>
    <org_study_id>MLL_001</org_study_id>
    <nct_id>NCT04466059</nct_id>
  </id_info>
  <brief_title>Better Leukemia Diagnostics Through AI (BELUGA)</brief_title>
  <acronym>BELUGA</acronym>
  <official_title>A Case-Control Study To Determine The Suitability Of Artificial Intelligence For Leukemia Diagnostics</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Munich Leukemia Laboratory</agency>
      <agency_class>Industry</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Munich Leukemia Laboratory</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      To the best of our knowledge, BELUGA will be the first prospective trial investigating the&#xD;
      usefulness of deep learning-based hematologic diagnostic algorithms. Taking advantage of an&#xD;
      unprecedented collection of diagnostic samples consisting of flow cytometry datapoints and&#xD;
      digitalized blood-smears, categorization of yet undiagnosed patient samples will&#xD;
      prospectively be compared to current state-of-the-art diagnosis at the Munich Leukemia&#xD;
      Laboratory (hereafter MLL). In total, a collection of 25,000 digitalized blood smears and&#xD;
      25,000 flow cytometry datapoints will be prospectively used to train an AI-based deep&#xD;
      neuronal network for correct categorization. Subsequently, the superiority will be challenged&#xD;
      for the primary endpoints: sensitivity and specificity of diagnosis, most probable diagnosis,&#xD;
      and time to diagnose. The secondary endpoints will compare the consequences regarding further&#xD;
      diagnostic work-up and, thus, clinical decision making between routine diagnosis and AI&#xD;
      guided diagnostics. BELUGA will set the stage for the introduction of AI-based hematologic&#xD;
      diagnostics in a real-world setting.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      In numerous recent studies, deep neuronal networks (DNN) have been leveraged to examine the&#xD;
      usefulness of artificial intelligence (AI)-based DNN for diagnostic purposes. In essence,&#xD;
      they have successfully proved to recapitulate state-of-the-art diagnoses currently performed&#xD;
      by humans.&#xD;
&#xD;
      Specifically, the use of artificial intelligence for pattern recognition showed that DNN&#xD;
      could categorize complex and composite data points, chiefly images, with high fidelity to a&#xD;
      specific pathogenic condition or disease. The majority of these studies are primarily based&#xD;
      on extensive training sample collections that were categorized a priori. Subsequently, this&#xD;
      &quot;training&quot; provided the necessary input to classify newly delivered specimens into the&#xD;
      correct subgroups, frequently even outperforming independent human investigators. So far,&#xD;
      these studies have thus provided the rationale for the use of DNN in real-world diagnostics.&#xD;
      However, the prerequisite for using DNN in a real-world setting, where specimen sampling and&#xD;
      analysis would need to outperform human diagnosis prospectively, would be a blinded and&#xD;
      prospective trial. Currently, there is a lack of prospective data, therefore still&#xD;
      challenging the notion that DNN can outperform state-of-the-art human-based diagnostic&#xD;
      algorithms. Here we want to investigate the validity and usefulness of AI-based diagnostic&#xD;
      capabilities prospectively in a real-world setting.&#xD;
&#xD;
      Hematologic diagnostics heavily rely on multiple methodically distinct approaches, of which&#xD;
      phenotyping aberrant blood or bone marrow cells from affected patients represents a&#xD;
      cornerstone for all subsequent methods, such as chromosomal or molecular genetic analyses. At&#xD;
      the MLL, five different diagnostic pillars are required to provide diagnostic evidence for a&#xD;
      specific malignant blood disorder faithfully: cytomorphology and immunophenotyping first,&#xD;
      guiding more specific methods such as cytogenetics, FISH, and a diversity of molecular&#xD;
      genetic assays.&#xD;
&#xD;
      +++ Objectives +++&#xD;
&#xD;
      Phenotyping of blood cells is primarily based on two distinct challenges; (1) the&#xD;
      morphological appearance and abundance of specific cell types and (2) the presence of&#xD;
      particular lineage markers detected by flow cytometry. These two methods are critical for&#xD;
      each subsequent decision-making process and, thus ultimately, the final diagnosis.&#xD;
      Simultaneously, these two methods are ideally suited for automated analysis by DNN due to&#xD;
      their inherent image-based nature. This has been recently illustrated by a publication by&#xD;
      Marr and colleagues (Matek et al., 2019; https://doi.org/10.1038/s42256-019-0101-9)&#xD;
&#xD;
      In BELUGA, we want to investigate whether the automated analysis of blood (from peripheral&#xD;
      blood and bone marrow aspirates) smears and flow-cytometry-based analyses can provide a&#xD;
      benefit for diagnostic quality and, ultimately, patient care. Moreover, BELUGA will provide&#xD;
      evidence for the cooperative nature of image-based diagnostic tools for other pillars of&#xD;
      hematologic diagnostic decision making such as genetic and molecular genetic&#xD;
      characterization.&#xD;
&#xD;
      BELUGA, therefore, consists of three parts (A-C) (See Figure in the attached File). In A, we&#xD;
      want to train a DNN with an unprecedented collection of blood smears and flow-cytometry-based&#xD;
      data points collected during the course of 15 years. These samples consist of all&#xD;
      hematological malignancies currently identified and recognized by the current WHO&#xD;
      classification for hematologic malignancies. Due to the varying incidences of these entities,&#xD;
      the total number of training items varies from 1,000 to 20,000 for 15 years. However, we deem&#xD;
      this discrepancy a benefit to this trial's overall aims, because this diverse spectrum will&#xD;
      inform us on the number of training items needed for outperforming the state-of-the-art&#xD;
      diagnostics in cytomorphology or flow cytometry.&#xD;
&#xD;
      In part B, we will compare the overall performance of our trained DNN prospectively to new&#xD;
      yet undiagnosed samples arriving at our laboratory (see the main section for details). The&#xD;
      superiority of DNN based categorization will be challenged based on the pre-defined outcome&#xD;
      parameters accuracy with respect to state-of-the-art diagnostics, mismatch-rate, and time&#xD;
      needed to provide a diagnostic probability.&#xD;
&#xD;
      Lastly, in C, we will investigate the effects on faster and more accurate diagnostic power by&#xD;
      leveraging our trained DNN to aid downstream diagnostic methodologies such as chromosomal&#xD;
      analysis or panel sequencing of patient samples.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">January 5, 2020</start_date>
  <completion_date type="Anticipated">July 31, 2023</completion_date>
  <primary_completion_date type="Anticipated">July 31, 2023</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Control</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Sensitivity and Specificity of AI Guided diagnostics in Hematology</measure>
    <time_frame>08-01-2020 until 07-31-2021</time_frame>
    <description>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.&#xD;
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.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>comparison of clinical consequences</measure>
    <time_frame>08-01-2020 until 07-31-2021</time_frame>
    <description>We will compare the clinical recommendation obtained after routine gold-standard diagnostics and after AI-guided categorization of all samples enrolled in this study</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>predictive diagnostic value</measure>
    <time_frame>08-01-2020 until 07-31-2021</time_frame>
    <description>We will assess the predictive value of unsupervised categorization and diagnosis in comparison to gold-standard routine testing.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>turn-around-time</measure>
    <time_frame>08-01-2020 until 07-31-2021</time_frame>
    <description>We will measure the turn-around-time of gold-standard diagnostics in comparison to AI-guided diagnosis.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>enumerate entity-specific benchmarks (e.g., blast count in leukemia) count)</measure>
    <time_frame>08-01-2020 until 07-31-2021</time_frame>
    <description>We will assess secondary disease specific values determined by AI/DNN based unsupervised diagnosis versus routine testing.</description>
  </secondary_outcome>
  <enrollment type="Anticipated">25000</enrollment>
  <condition>Hematologic Malignancy</condition>
  <condition>Leukemia</condition>
  <condition>Minimal Residual Disease</condition>
  <condition>Lymphoma</condition>
  <condition>Blood Cancer</condition>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Automated AI-Guided Diagnosis of Hematological Malignancies</intervention_name>
    <description>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.</description>
  </intervention>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      All samples used consist of bone marrow aspirates or peripheral blood sent to our diagnostic&#xD;
      laboratory for routine hematological testing.&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        The training cohort of BELUGA consists of 50,000 annotated samples for which&#xD;
        cytomorphological smears (25,000 samples) and immunophenotyping (25,000 samples) data&#xD;
        points have been collected. This cohort serves as a foundation for the DNN to perform&#xD;
        training. Our test cohort will consist of all samples for which cytomorphology and&#xD;
        immunophenotyping will be performed for one year.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients having been diagnosed with a suspected hematological disorder&#xD;
&#xD;
          -  The suspected diagnoses constitute a primary diagnosis&#xD;
&#xD;
          -  Only samples of patients min.18 years of age will be used&#xD;
&#xD;
          -  Samples must suffice quality attributes which are denoted in &quot;Exclusion Criteria&quot;&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  The sample is not fit for state-of-the-art diagnosis or fails initial quality control.&#xD;
             For quality insurance, we will exclude samples in heparin- instead of EDTA. Samples&#xD;
             with damage due to atmospheric reasons (freeze-thaw damage or elevated temperature)&#xD;
             will be excluded.&#xD;
&#xD;
          -  Samples with too scarce material jeopardizing routine gold-standard diagnosis will be&#xD;
             excluded.&#xD;
&#xD;
          -  Bone marrow aspirates without sufficient material to assess malignant or healthy&#xD;
             hematopoiesis.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Wolfgang Kern, Prof. Dr.</last_name>
    <role>Principal Investigator</role>
    <affiliation>MLL Munich Leukemia Laboratory</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Adam Wahida, MD</last_name>
    <phone>+49 (0)89 99017 338</phone>
    <email>adam.wahida@mll.com</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Torsten Haferlach, Prof. Dr.Dr.</last_name>
    <phone>+49 (0)89 99017 100</phone>
    <email>torsten.haferlach@mll.com</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>MLL Munich Leukemia Laboratory</name>
      <address>
        <city>Munich</city>
        <state>Bavaria</state>
        <zip>81377</zip>
        <country>Germany</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Adam Wahida, MD</last_name>
      <email>adam.wahida@mll.com</email>
    </contact>
    <contact_backup>
      <last_name>Torsten Haferlach, Prof.Dr.Dr.</last_name>
      <email>torsten.haferlach@mll.com</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>Germany</country>
  </location_countries>
  <link>
    <url>https://doi.org/10.1038/s42256-019-0101-9</url>
    <description>Related Info</description>
  </link>
  <link>
    <url>https://publications.iarc.fr/Book-And-Report-Series/Who-Classification-Of-Tumours/WHO-Classification-Of-Tumours-Of-Haematopoietic-And-Lymphoid-Tissues-2017</url>
    <description>WHO Classification of hematological malignancies according to Swerdlow et al.</description>
  </link>
  <link>
    <url>https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9785/97853C/Automated-morphological-analysis-of-bone-marrow-cells-in-microscopic-images/10.1117/12.2216037.short</url>
    <description>Related Info</description>
  </link>
  <link>
    <url>https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9414/94143I/Automated-classification-of-bone-marrow-cells-in-microscopic-images-for/10.1117/12.2081946.short?webSyncID=9a28531a-b0d3-a6b0-3c97-c009b6bcd746&amp;sessionGUID=b80d8e8a-beea-ed6a-e05c-117aac5a9199</url>
    <description>Related Info</description>
  </link>
  <results_reference>
    <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.</citation>
    <PMID>32519455</PMID>
  </results_reference>
  <verification_date>March 2023</verification_date>
  <study_first_submitted>July 6, 2020</study_first_submitted>
  <study_first_submitted_qc>July 9, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 10, 2020</study_first_posted>
  <last_update_submitted>March 21, 2023</last_update_submitted>
  <last_update_submitted_qc>March 21, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">March 22, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Munich Leukemia Laboratory</investigator_affiliation>
    <investigator_full_name>Torsten Haferlach</investigator_full_name>
    <investigator_title>Prof. Dr. Dr.</investigator_title>
  </responsible_party>
  <keyword>hematology</keyword>
  <keyword>laboratory medicine</keyword>
  <keyword>AI-based diagnostics</keyword>
  <keyword>artificial intelligence</keyword>
  <keyword>deep neuronal networks</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Leukemia</mesh_term>
    <mesh_term>Hematologic Neoplasms</mesh_term>
    <mesh_term>Neoplasm, Residual</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
  </patient_data>
  <provided_document_section>
    <provided_document>
      <document_type>Study Protocol and Statistical Analysis Plan</document_type>
      <document_has_protocol>Yes</document_has_protocol>
      <document_has_icf>No</document_has_icf>
      <document_has_sap>Yes</document_has_sap>
      <document_date>July 9, 2020</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/59/NCT04466059/Prot_SAP_000.pdf</document_url>
    </provided_document>
  </provided_document_section>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04466059
org study id: MLL_001
nct id: NCT04466059
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
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.
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.
observational model: Case-Control
time perspective: Prospective
measure: Sensitivity and Specificity of AI Guided diagnostics in Hematology
time frame: 08-01-2020 until 07-31-2021
description: 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.
measure: comparison of clinical consequences
time frame: 08-01-2020 until 07-31-2021
description: We will compare the clinical recommendation obtained after routine gold-standard diagnostics and after AI-guided categorization of all samples enrolled in this study
measure: predictive diagnostic value
time frame: 08-01-2020 until 07-31-2021
description: We will assess the predictive value of unsupervised categorization and diagnosis in comparison to gold-standard routine testing.
measure: turn-around-time
time frame: 08-01-2020 until 07-31-2021
description: We will measure the turn-around-time of gold-standard diagnostics in comparison to AI-guided diagnosis.
measure: enumerate entity-specific benchmarks (e.g., blast count in leukemia) count)
time frame: 08-01-2020 until 07-31-2021
description: We will assess secondary disease specific values determined by AI/DNN based unsupervised diagnosis versus routine testing.
intervention type: Diagnostic Test
intervention name: Automated AI-Guided Diagnosis of Hematological Malignancies
description: 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.
All samples used consist of bone marrow aspirates or peripheral blood sent to our diagnostic
      laboratory for routine hematological testing.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Wolfgang Kern, Prof. Dr.
role: Principal Investigator
affiliation: MLL Munich Leukemia Laboratory
last name: Adam Wahida, MD
phone: +49 (0)89 99017 338
email: adam.wahida@mll.com
last name: Torsten Haferlach, Prof. Dr.Dr.
phone: +49 (0)89 99017 100
email: torsten.haferlach@mll.com
facility: 
status: Recruiting
contact: 
contact backup: 
country: Germany
url: https://doi.org/10.1038/s42256-019-0101-9
description: Related Info
url: https://publications.iarc.fr/Book-And-Report-Series/Who-Classification-Of-Tumours/WHO-Classification-Of-Tumours-Of-Haematopoietic-And-Lymphoid-Tissues-2017
description: WHO Classification of hematological malignancies according to Swerdlow et al.
url: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9785/97853C/Automated-morphological-analysis-of-bone-marrow-cells-in-microscopic-images/10.1117/12.2216037.short
description: Related Info
url: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9414/94143I/Automated-classification-of-bone-marrow-cells-in-microscopic-images-for/10.1117/12.2081946.short?webSyncID=9a28531a-b0d3-a6b0-3c97-c009b6bcd746&sessionGUID=b80d8e8a-beea-ed6a-e05c-117aac5a9199
description: Related Info
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.
PMID: 32519455
responsible party type: Principal Investigator
investigator affiliation: Munich Leukemia Laboratory
investigator full name: Torsten Haferlach
investigator title: Prof. Dr. Dr.
mesh term: Leukemia
mesh term: Hematologic Neoplasms
mesh term: Neoplasm, Residual
sharing ipd: Undecided
provided document: 