xml: <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/NCT05983172</url>
    </required_header>
    <id_info>
      <org_study_id>202306138RINB</org_study_id>
      <nct_id>NCT05983172</nct_id>
    </id_info>
    <brief_title>Multi-omics Approach of Risk Stratification for Patients With de Novo Acute Myeloid Leukemia</brief_title>
    <official_title>Multi-omics Approach of Risk Stratification for Patients With de Novo Acute Myeloid Leukemia</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>National Taiwan University Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>National Taiwan University Hospital</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The investigators will use machine learning to identify features on bone marrow smears and&#xD;
        select features that are related to gene mutations, gene expression, or prognosis. The&#xD;
        investigators will then use genome-wide transcriptomic profiling to investigate gene&#xD;
        expression that is associated with patients' outcomes. The investigators will design a&#xD;
        next-generation sequencing panel with unique molecular index and assess its feasibility and&#xD;
        robustness in detecting measurable residual disease and optimize the&#xD;
        panel/platform/bioinformatic pipeline. Finally, The investigators will use machine learning&#xD;
        to integrate bone marrow smear features, gene mutations, gene expression, and measurable&#xD;
        residual disease to construct a comprehensive risk assessment system that is based on&#xD;
        multi-omics data. The investigators believe that such a platform will help physicians to&#xD;
        design the most appropriate treatment strategies for individual patients, not only advancing&#xD;
        the concept of precision medicine but also improving patients' prognoses.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Enrolling by invitation</overall_status>
    <start_date type="Anticipated">August 2023</start_date>
    <completion_date type="Anticipated">July 2026</completion_date>
    <primary_completion_date type="Anticipated">July 2024</primary_completion_date>
    <study_type>Observational [Patient Registry]</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Other</time_perspective>
    </study_design_info>
    <target_duration>10 Years</target_duration>
    <primary_outcome>
      <measure>Overall survival</measure>
      <time_frame>From date of diagnosis until the date of death from any cause, assessed up to 30 years</time_frame>
    </primary_outcome>
    <enrollment type="Anticipated">1500</enrollment>
    <condition>Acute Myeloid Leukemia</condition>
    <eligibility>
      <study_pop>
        <textblock>
          Adult patients with acute myeloid leukemia&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. Diagnosed with acute myeloid leukemia at the National Taiwan University Hospital&#xD;
  &#xD;
            2. Ever enrolled in 201802021RINC、202109078RINB、201709072RINC&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
          Nil&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Xavier Cheng-Hong Tsai, MD, MSc, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>National Taiwan University Hospital</affiliation>
    </overall_official>
    <location>
      <facility>
        <name>National Taiwan University Hospital</name>
        <address>
          <city>Taipei</city>
          <zip>100</zip>
          <country>Taiwan</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>Taiwan</country>
    </location_countries>
    <verification_date>July 2023</verification_date>
    <study_first_submitted>July 30, 2023</study_first_submitted>
    <study_first_submitted_qc>August 7, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 9, 2023</study_first_posted>
    <last_update_submitted>August 9, 2023</last_update_submitted>
    <last_update_submitted_qc>August 9, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 14, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>National Taiwan University Hospital</investigator_affiliation>
      <investigator_full_name>National Taiwan University Clinical Trial Center</investigator_full_name>
      <investigator_title>Assistant Professor and attending physician</investigator_title>
    </responsible_party>
    <keyword>Acute myeloid leukemia</keyword>
    <keyword>Machine learning</keyword>
    <keyword>Artificial intelligence</keyword>
    <keyword>Risk stratification</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Leukemia</mesh_term>
      <mesh_term>Leukemia, Myeloid</mesh_term>
      <mesh_term>Leukemia, Myeloid, Acute</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT05983172
  org study id: 202306138RINB
  nct id: NCT05983172
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  The investigators will use machine learning to identify features on bone marrow smears and
        select features that are related to gene mutations, gene expression, or prognosis. The
        investigators will then use genome-wide transcriptomic profiling to investigate gene
        expression that is associated with patients' outcomes. The investigators will design a
        next-generation sequencing panel with unique molecular index and assess its feasibility and
        robustness in detecting measurable residual disease and optimize the
        panel/platform/bioinformatic pipeline. Finally, The investigators will use machine learning
        to integrate bone marrow smear features, gene mutations, gene expression, and measurable
        residual disease to construct a comprehensive risk assessment system that is based on
        multi-omics data. The investigators believe that such a platform will help physicians to
        design the most appropriate treatment strategies for individual patients, not only advancing
        the concept of precision medicine but also improving patients' prognoses.
  observational model: Cohort
  time perspective: Other
  measure: Overall survival
  time frame: From date of diagnosis until the date of death from any cause, assessed up to 30 years
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Xavier Cheng-Hong Tsai, MD, MSc, PhD
  role: Principal Investigator
  affiliation: National Taiwan University Hospital
  facility: 
  country: Taiwan
  responsible party type: Principal Investigator
  investigator affiliation: National Taiwan University Hospital
  investigator full name: National Taiwan University Clinical Trial Center
  investigator title: Assistant Professor and attending physician
  mesh term: Leukemia
  mesh term: Leukemia, Myeloid
  mesh term: Leukemia, Myeloid, Acute
  sharing ipd: No