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/NCT06031818</url>
    </required_header>
    <id_info>
      <org_study_id>Interpretable DL</org_study_id>
      <secondary_id>92059201</secondary_id>
      <nct_id>NCT06031818</nct_id>
    </id_info>
    <brief_title>Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction</brief_title>
    <official_title>Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework (VAE-MILP) Using Counterfactual Explanations and Layerwise Relevance Propagation Framework for Post-Hepatectomy Liver Failure Prediction</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Maastricht University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>First Affiliated Hospital, Sun Yat-Sen University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Maastricht University</source>
    <oversight_info>
      <has_dmc>No</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>
        The goal of this in-silico clinical trial is to learn about the usability and clinical&#xD;
        effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual&#xD;
        explanations and layerwise relevance propagation for prediction of post-hepatectomy liver&#xD;
        failure (PHLF) in patients with hepatocellular carcinoma (HCC). The main questions it aims to&#xD;
        answer are:&#xD;
  &#xD;
          -  To investigate the usability of the VAE-MLP framework for explanation of the deep&#xD;
             learning model.&#xD;
  &#xD;
          -  To investigate the clinical effectiveness of VAE-MLP framework for prediction of&#xD;
             post-hepatectomy liver failure in patients with hepatocellular carcinoma.&#xD;
  &#xD;
        In the usability trial the clinicians and radiologists will be shown the counterfactual&#xD;
        explanations and layerwise relevance propagation (LRP) plots to evaluate the usability of the&#xD;
        framework.&#xD;
  &#xD;
        In the clinical trial the clinicians and radiologists will make the prediction under two&#xD;
        different conditions: with model explanation and without model explanation with a washout&#xD;
        period of at least 14 days to evaluate the clinical effectiveness of the explanation&#xD;
        framework.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Post-hepatectomy liver failure (PHLF) is a severe complication after liver resection. It is&#xD;
        important to develop an interpretable model for predicting PHLF in order to facilitate&#xD;
        effective collaboration with clinicians for decision-making. Two-dimensional shear wave&#xD;
        elastography (2D-SWE) is a liver stiffness measurement (LSM) technology that was proven to be&#xD;
        useful in liver fibrosis staging. Therefore 2D-SWE shows the potential value for liver&#xD;
        function assessment and PHLF prediction. 2D-SWE images display color-coded tissue stiffness&#xD;
        map of liver parenchyma, with red representing a solid tissue (higher stiffness) and blue&#xD;
        representing a soft tissue (lower stiffness). Routine analysis of 2D-SWE fails to fully&#xD;
        utilize all information available in the images and also suffers from inter-observer variance&#xD;
        in choosing the optimal quantification region.&#xD;
  &#xD;
        Deep learning (DL) has demonstrated state-of-the-art performance on many medical imaging&#xD;
        tasks such as classification or segmentation. However, despite significant progress in DL,&#xD;
        the clinical translation of DL tools has so far been limited, partially due to a lack of&#xD;
        interpretability of models, the so-called &quot;black box&quot; problem. Interpretability of DL systems&#xD;
        is important for fostering clinical trust as well as timely correcting any faulty processes&#xD;
        in the algorithms.&#xD;
  &#xD;
        Here, we present a novel interpretable DL framework (VAE-MLP) which incorporates&#xD;
        counterfactual analysis for the explanation of 2D medical images and LRP for the explanation&#xD;
        of feature attributions of both medical images and clinical variables.&#xD;
  &#xD;
        The goal of this in-silico clinical trial is to learn about the usability and clinical&#xD;
        effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual&#xD;
        explanations and layerwise relevance propagation for prediction of post-hepatectomy liver&#xD;
        failure (PHLF) in patients with hepatocellular carcinoma. The main questions it aims to&#xD;
        answer are:&#xD;
  &#xD;
          -  To investigate the usability of the the interpretable deep learning framework (VAE-MLP)&#xD;
             for explanation of the deep learning model.&#xD;
  &#xD;
          -  To investigate the clinical effectiveness of the interpretable deep learning framework&#xD;
             (VAE-MLP) for prediction of post-hepatectomy liver failure in patients with&#xD;
             hepatocellular carcinoma.&#xD;
  &#xD;
        In the usability trial the clinicians and radiologists will be shown the counterfactual&#xD;
        explanations and layerwise relevance propagation plots of 6 examples. The score of the Likert&#xD;
        scale of a designed questionnaire is used to evaluate the usability of the framework.&#xD;
  &#xD;
        In the clinical trial the clinicians and radiologists will make the prediction under two&#xD;
        different conditions: with model explanation and without model explanation with a washout&#xD;
        period of at least 14 days. The accuracy, sensitivity and specificity is used to compare the&#xD;
        clinical effectiveness of the explanation framework.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 1, 2023</start_date>
    <completion_date type="Anticipated">October 30, 2023</completion_date>
    <primary_completion_date type="Anticipated">September 30, 2023</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Clinical effectiveness of the explanation framework</measure>
      <time_frame>From enrollment to the end of trial at 8 weeks</time_frame>
      <description>The accuracy, sensitivity and specificity will be compared between the prediction made with and without the explanation of the DL model to determine the clinical effectiveness of the explanation framework.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Usability of the explanation framework</measure>
      <time_frame>From enrollment to the end of trial at 8 weeks</time_frame>
      <description>The score of the Likert scale of a designed questionnaire is used to evaluate the usability of the framework</description>
    </secondary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">80</enrollment>
    <condition>Post-hepatectomy Liver Failure</condition>
    <condition>Hepatocellular Carcinoma</condition>
    <condition>Artificial Intelligence</condition>
    <arm_group>
      <arm_group_label>Patients with HCC</arm_group_label>
      <description>Patients who underwent curative liver resection for HCC in the First Affiliated Hospital of Sun Yat-Sen University in China.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation</intervention_name>
      <description>The radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will fill in a questionnaire to evaluate the usability of the interpretable framework.</description>
      <arm_group_label>Patients with HCC</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>The model prediction</intervention_name>
      <description>The radiologist and clinicians will be provided the model prediction results without the explanation of the model and they will be asked to give their own prediction.</description>
      <arm_group_label>Patients with HCC</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation</intervention_name>
      <description>The radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will be asked to give their own prediction.</description>
      <arm_group_label>Patients with HCC</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Patients who underwent curative liver resection for HCC.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
          1) patients with treatment-naive and resectable HCC; 2) performance status Eastern&#xD;
          Cooperative Oncology Group (PS) score 0-1.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
          1) liver resection was not performed; 2) pathological diagnosis of non-HCC; 3) failure in&#xD;
          liver stiffness measurement defined as the elastography color map was less than 75% filled&#xD;
          or interquartile range (IQR)/median &gt; 30%; 4) immune-active chronic hepatitis indicated by&#xD;
          an elevation of alanine aminotransferase (ALT) levels ≥ 2×upper limit of normal (ULN); 5)&#xD;
          obstructive jaundice or dilated intrahepatic bile ducts with a diameter of &gt;3 mm; 6)&#xD;
          hypoalbuminemia, hyperbilirubinemia, or coagulopathy not related to the liver.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>N/A</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Philippe Lambin</last_name>
      <role>Principal Investigator</role>
      <affiliation>Maastricht University</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Xian Zhong</last_name>
      <phone>86-13632460144</phone>
      <email>x.zhong@maastrichtuniversity.nl</email>
    </overall_contact>
    <location>
      <facility>
        <name>The First Affiliated Hospital of Sun Yat-Sen University</name>
        <address>
          <city>Guangzhou</city>
          <state>Guangdong</state>
          <zip>510000</zip>
          <country>China</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>September 4, 2023</study_first_submitted>
    <study_first_submitted_qc>September 4, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 11, 2023</study_first_posted>
    <last_update_submitted>September 4, 2023</last_update_submitted>
    <last_update_submitted_qc>September 4, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 11, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>deep learning</keyword>
    <keyword>interpretability</keyword>
    <keyword>post-hepatectomy liver failure</keyword>
    <keyword>hepatocellular carcinoma</keyword>
    <keyword>shear wave elastography</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Carcinoma, Hepatocellular</mesh_term>
      <mesh_term>Liver Failure</mesh_term>
      <mesh_term>Hepatic Insufficiency</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
      <ipd_description>Individual participant data (IPD) can be shared if asked</ipd_description>
    </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/NCT06031818
  org study id: Interpretable DL
  secondary id: 92059201
  nct id: NCT06031818
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The goal of this in-silico clinical trial is to learn about the usability and clinical
        effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual
        explanations and layerwise relevance propagation for prediction of post-hepatectomy liver
        failure (PHLF) in patients with hepatocellular carcinoma (HCC). The main questions it aims to
        answer are:
  
          -  To investigate the usability of the VAE-MLP framework for explanation of the deep
             learning model.
  
          -  To investigate the clinical effectiveness of VAE-MLP framework for prediction of
             post-hepatectomy liver failure in patients with hepatocellular carcinoma.
  
        In the usability trial the clinicians and radiologists will be shown the counterfactual
        explanations and layerwise relevance propagation (LRP) plots to evaluate the usability of the
        framework.
  
        In the clinical trial the clinicians and radiologists will make the prediction under two
        different conditions: with model explanation and without model explanation with a washout
        period of at least 14 days to evaluate the clinical effectiveness of the explanation
        framework.
  Post-hepatectomy liver failure (PHLF) is a severe complication after liver resection. It is
        important to develop an interpretable model for predicting PHLF in order to facilitate
        effective collaboration with clinicians for decision-making. Two-dimensional shear wave
        elastography (2D-SWE) is a liver stiffness measurement (LSM) technology that was proven to be
        useful in liver fibrosis staging. Therefore 2D-SWE shows the potential value for liver
        function assessment and PHLF prediction. 2D-SWE images display color-coded tissue stiffness
        map of liver parenchyma, with red representing a solid tissue (higher stiffness) and blue
        representing a soft tissue (lower stiffness). Routine analysis of 2D-SWE fails to fully
        utilize all information available in the images and also suffers from inter-observer variance
        in choosing the optimal quantification region.
  
        Deep learning (DL) has demonstrated state-of-the-art performance on many medical imaging
        tasks such as classification or segmentation. However, despite significant progress in DL,
        the clinical translation of DL tools has so far been limited, partially due to a lack of
        interpretability of models, the so-called "black box" problem. Interpretability of DL systems
        is important for fostering clinical trust as well as timely correcting any faulty processes
        in the algorithms.
  
        Here, we present a novel interpretable DL framework (VAE-MLP) which incorporates
        counterfactual analysis for the explanation of 2D medical images and LRP for the explanation
        of feature attributions of both medical images and clinical variables.
  
        The goal of this in-silico clinical trial is to learn about the usability and clinical
        effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual
        explanations and layerwise relevance propagation for prediction of post-hepatectomy liver
        failure (PHLF) in patients with hepatocellular carcinoma. The main questions it aims to
        answer are:
  
          -  To investigate the usability of the the interpretable deep learning framework (VAE-MLP)
             for explanation of the deep learning model.
  
          -  To investigate the clinical effectiveness of the interpretable deep learning framework
             (VAE-MLP) for prediction of post-hepatectomy liver failure in patients with
             hepatocellular carcinoma.
  
        In the usability trial the clinicians and radiologists will be shown the counterfactual
        explanations and layerwise relevance propagation plots of 6 examples. The score of the Likert
        scale of a designed questionnaire is used to evaluate the usability of the framework.
  
        In the clinical trial the clinicians and radiologists will make the prediction under two
        different conditions: with model explanation and without model explanation with a washout
        period of at least 14 days. The accuracy, sensitivity and specificity is used to compare the
        clinical effectiveness of the explanation framework.
  observational model: Cohort
  time perspective: Retrospective
  measure: Clinical effectiveness of the explanation framework
  time frame: From enrollment to the end of trial at 8 weeks
  description: The accuracy, sensitivity and specificity will be compared between the prediction made with and without the explanation of the DL model to determine the clinical effectiveness of the explanation framework.
  measure: Usability of the explanation framework
  time frame: From enrollment to the end of trial at 8 weeks
  description: The score of the Likert scale of a designed questionnaire is used to evaluate the usability of the framework
  arm group label: Patients with HCC
  description: Patients who underwent curative liver resection for HCC in the First Affiliated Hospital of Sun Yat-Sen University in China.
  intervention type: Other
  intervention name: The explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation
  description: The radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will fill in a questionnaire to evaluate the usability of the interpretable framework.
  arm group label: Patients with HCC
  intervention type: Other
  intervention name: The model prediction
  description: The radiologist and clinicians will be provided the model prediction results without the explanation of the model and they will be asked to give their own prediction.
  arm group label: Patients with HCC
  intervention type: Other
  intervention name: The model prediction and the explanation of deep learning framework (VAE-MLP) , including counterfactual explanations and layerwise relevance propagation
  description: The radiologist and clinicians will be provided the model prediction results with the explanation of the model and they will be asked to give their own prediction.
  arm group label: Patients with HCC
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: N/A
  maximum age: N/A
  healthy volunteers: No
  last name: Philippe Lambin
  role: Principal Investigator
  affiliation: Maastricht University
  last name: Xian Zhong
  phone: 86-13632460144
  email: x.zhong@maastrichtuniversity.nl
  facility: 
  country: China
  responsible party type: Sponsor
  mesh term: Carcinoma, Hepatocellular
  mesh term: Liver Failure
  mesh term: Hepatic Insufficiency
  sharing ipd: Undecided
  ipd description: Individual participant data (IPD) can be shared if asked