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/NCT06002425</url>
    </required_header>
    <id_info>
      <org_study_id>CASMI006</org_study_id>
      <nct_id>NCT06002425</nct_id>
    </id_info>
    <brief_title>Treatment Recommendations for Gastrointestinal Cancers Via Large Language Models</brief_title>
    <official_title>Application of Large Language Models in the Recommendation of Treatment Plans for Gastrointestinal Cancers</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Chinese Academy of Sciences</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>ZhuHai Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Fifth Affiliated Hospital, Sun Yat-Sen University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Jiangmen Central Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Peking University Cancer Hospital (Inner Mongolia Campus)</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>San Raffaele University Hospital, Italy</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>University Hospital Magdeburg, Germany</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>City of Hope Medical Center</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Chinese Academy of Sciences</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>
        This study will evaluate the utility of ChatGPT in recommending treatment plans for patients&#xD;
        with gastrointestinal cancers, using both retrospective and prospective data.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        The medical records of over 1,200 patients with gastrointestinal cancers will be collected&#xD;
        retrospectively from participating hospitals. This data will be split into an exploratory&#xD;
        dataset (n=200) and a validation dataset (n&gt;=1,000). Within the exploratory dataset, various&#xD;
        prompt methods will be used to determine the treatment plans suggested by ChatGPT.&#xD;
        Additionally, several clinicians of varied seniority levels will provide their treatment&#xD;
        recommendations. For the validation dataset, ChatGPT's suggestions for treatment plans will&#xD;
        undergo both qualitative and quantitative assessments by a multidisciplinary consultation&#xD;
        (MDT) team. The recommendations from ChatGPT will then be compared with those from the&#xD;
        clinicians.&#xD;
  &#xD;
        Furthermore, this study will incorporate a prospective dataset comprising 400 participants&#xD;
        with gastrointestinal cancers. The participants will be randomly allocated to either a&#xD;
        control group (n=200) or a ChatGPT-Assisted group (n=200). In the control group, treatment&#xD;
        plan recommendations will solely be provided by the clinicians and will guide subsequent&#xD;
        treatments. In the ChatGPT-Assisted group, initial treatment plan recommendations will be&#xD;
        independently proposed by both ChatGPT and the clinicians. Based on ChatGPT's suggestions,&#xD;
        clinicians might selectively adjust their initial plans. Participants will then receive&#xD;
        treatments as per these refined plans. Within the ChatGPT-Assisted group, the treatment plans&#xD;
        of the initial 100 participants will be evaluated to determine the percentage of patients&#xD;
        whose treatment plans are influenced by ChatGPT. Subsequently, the proportion of participants&#xD;
        in the entire ChatGPT-Assisted group with treatment plans modified by ChatGPT will be&#xD;
        calculated. The study will further monitor the 3-year progression-free survival (PFS) and the&#xD;
        5-year overall survival (OS) rates, contrasting the outcomes between the control and&#xD;
        ChatGPT-assisted groups.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">August 29, 2023</start_date>
    <completion_date type="Anticipated">December 31, 2028</completion_date>
    <primary_completion_date type="Anticipated">September 30, 2023</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Randomized</allocation>
      <intervention_model>Parallel Assignment</intervention_model>
      <primary_purpose>Treatment</primary_purpose>
      <masking>Single (Participant)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Influence Rate of ChatGPT on Treatment Plans</measure>
      <time_frame>Within 24 hours after the treatment plan is determined from the onset of study participation.</time_frame>
      <description>The percentage of the initial 100 participants and the overall participants in the ChatGPT-Assisted group whose treatment plans are adjusted by clinicians after consulting with ChatGPT.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>3-year Progression-Free Survival (PFS) Rate</measure>
      <time_frame>3 years</time_frame>
      <description>Percentage of participants without disease progression over a period of 3 years from the start of treatment.</description>
    </secondary_outcome>
    <other_outcome>
      <measure>5-year Overall Survival (OS) Rate</measure>
      <time_frame>5 years</time_frame>
      <description>Percentage of participants who are still alive over a period of 5 years from the start of treatment.</description>
    </other_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">400</enrollment>
    <condition>Gastrointestinal Neoplasm Malignant</condition>
    <arm_group>
      <arm_group_label>Control group</arm_group_label>
      <arm_group_type>Active Comparator</arm_group_type>
      <description>In this arm, participants receive treatment plans directly from clinicians without the assistance of ChatGPT.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>GPT-Assisted Group</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>In this arm, participants receive treatment plans from clinicians with the assistance of ChatGPT.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Clinician-Directed Treatment Plan</intervention_name>
      <description>In this approach, clinicians do not employ any technological assistance and rely solely on their professional expertise and experience to formulate treatment plans for participants.</description>
      <arm_group_label>Control group</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>ChatGPT-Assisted Treatment Plan</intervention_name>
      <description>In this approach, clinicians utilize the ChatGPT technological tool, formulating treatment plans for participants based on its suggestions and their own professional expertise.</description>
      <arm_group_label>GPT-Assisted Group</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Age ≥18 years, both male and female.&#xD;
  &#xD;
            -  Pathologically confirmed diagnosis of gastrointestinal cancer (gastric cancer or&#xD;
               colorectal Cancer).&#xD;
  &#xD;
            -  Detailed medical records available prior to treatment (including chief complaint,&#xD;
               history of present illness, radiological examinations, pathological examinations,&#xD;
               laboratory tests, etc.).&#xD;
  &#xD;
            -  Participants will receive complete treatment in the participating hospitals.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Participants with cancers other than gastrointestinal cancers.&#xD;
  &#xD;
            -  Participants who receive treatment in multiple hospitals.&#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>Di Dong, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Institute of Automation, Chinese Academy of Sciences</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Di Dong, PhD</last_name>
      <phone>+86 13811833760</phone>
      <email>di.dong@ia.ac.cn</email>
    </overall_contact>
    <location>
      <facility>
        <name>City of Hope</name>
        <address>
          <city>Duarte</city>
          <state>California</state>
          <zip>91010</zip>
          <country>United States</country>
        </address>
      </facility>
      <status>Not yet recruiting</status>
      <contact>
        <last_name>Syed Rahmanuddin</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Jiangmen Central Hospital</name>
        <address>
          <city>Jiangmen</city>
          <state>Guangdong</state>
          <zip>529000</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Xiaobei Duan</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>The Fifth Affiliated Hospital of Sun Yat-sen University</name>
        <address>
          <city>Zhuhai</city>
          <state>Guangdong</state>
          <zip>519000</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Jing Pang</last_name>
      </contact>
      <contact_backup>
        <last_name>Guojie Wang</last_name>
      </contact_backup>
    </location>
    <location>
      <facility>
        <name>Zhuhai People's Hospital</name>
        <address>
          <city>Zhuhai</city>
          <state>Guangdong</state>
          <zip>519000</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Jie Zhang, Ph.D.</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Peking University Cancer Hospital (Inner Mongolia Campus)</name>
        <address>
          <city>Hohhot</city>
          <state>Inner Mongolia</state>
          <zip>010010</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Xiaotian Zhang</last_name>
      </contact>
      <contact_backup>
        <last_name>Zhenghang Wang</last_name>
      </contact_backup>
    </location>
    <location>
      <facility>
        <name>University Hospital Magdeburg</name>
        <address>
          <city>Magdeburg</city>
          <state>Saxony-Anhalt</state>
          <zip>39120</zip>
          <country>Germany</country>
        </address>
      </facility>
      <status>Not yet recruiting</status>
      <contact>
        <last_name>Michael Kreissl</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>San Raffaele University Hospital, Italy</name>
        <address>
          <city>Milan</city>
          <zip>20132</zip>
          <country>Italy</country>
        </address>
      </facility>
      <status>Not yet recruiting</status>
      <contact>
        <last_name>Diego Palumbo</last_name>
      </contact>
    </location>
    <location_countries>
      <country>China</country>
      <country>Germany</country>
      <country>Italy</country>
      <country>United States</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 13, 2023</study_first_submitted>
    <study_first_submitted_qc>August 13, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 21, 2023</study_first_posted>
    <last_update_submitted>September 6, 2023</last_update_submitted>
    <last_update_submitted_qc>September 6, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Chinese Academy of Sciences</investigator_affiliation>
      <investigator_full_name>Di Dong</investigator_full_name>
      <investigator_title>Professor</investigator_title>
    </responsible_party>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Large Language Model</keyword>
    <keyword>Gastrointestinal Cancers</keyword>
    <keyword>Treatment Recommendation</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Gastrointestinal Neoplasms</mesh_term>
      <mesh_term>Digestive System Neoplasms</mesh_term>
      <mesh_term>Neoplasms</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Yes</sharing_ipd>
      <ipd_description>Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.</ipd_description>
      <ipd_info_type>Study Protocol</ipd_info_type>
      <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
      <ipd_info_type>Analytic Code</ipd_info_type>
      <ipd_time_frame>Data will become available 6 months after study completion and will remain available for a period of 5 years.</ipd_time_frame>
      <ipd_access_criteria>Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.</ipd_access_criteria>
      <ipd_url>http://www.radiomics.net.cn/</ipd_url>
    </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/NCT06002425
  org study id: CASMI006
  nct id: NCT06002425
  lead sponsor: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  This study will evaluate the utility of ChatGPT in recommending treatment plans for patients
        with gastrointestinal cancers, using both retrospective and prospective data.
  The medical records of over 1,200 patients with gastrointestinal cancers will be collected
        retrospectively from participating hospitals. This data will be split into an exploratory
        dataset (n=200) and a validation dataset (n>=1,000). Within the exploratory dataset, various
        prompt methods will be used to determine the treatment plans suggested by ChatGPT.
        Additionally, several clinicians of varied seniority levels will provide their treatment
        recommendations. For the validation dataset, ChatGPT's suggestions for treatment plans will
        undergo both qualitative and quantitative assessments by a multidisciplinary consultation
        (MDT) team. The recommendations from ChatGPT will then be compared with those from the
        clinicians.
  
        Furthermore, this study will incorporate a prospective dataset comprising 400 participants
        with gastrointestinal cancers. The participants will be randomly allocated to either a
        control group (n=200) or a ChatGPT-Assisted group (n=200). In the control group, treatment
        plan recommendations will solely be provided by the clinicians and will guide subsequent
        treatments. In the ChatGPT-Assisted group, initial treatment plan recommendations will be
        independently proposed by both ChatGPT and the clinicians. Based on ChatGPT's suggestions,
        clinicians might selectively adjust their initial plans. Participants will then receive
        treatments as per these refined plans. Within the ChatGPT-Assisted group, the treatment plans
        of the initial 100 participants will be evaluated to determine the percentage of patients
        whose treatment plans are influenced by ChatGPT. Subsequently, the proportion of participants
        in the entire ChatGPT-Assisted group with treatment plans modified by ChatGPT will be
        calculated. The study will further monitor the 3-year progression-free survival (PFS) and the
        5-year overall survival (OS) rates, contrasting the outcomes between the control and
        ChatGPT-assisted groups.
  allocation: Randomized
  intervention model: Parallel Assignment
  primary purpose: Treatment
  masking: Single (Participant)
  measure: Influence Rate of ChatGPT on Treatment Plans
  time frame: Within 24 hours after the treatment plan is determined from the onset of study participation.
  description: The percentage of the initial 100 participants and the overall participants in the ChatGPT-Assisted group whose treatment plans are adjusted by clinicians after consulting with ChatGPT.
  measure: 3-year Progression-Free Survival (PFS) Rate
  time frame: 3 years
  description: Percentage of participants without disease progression over a period of 3 years from the start of treatment.
  measure: 5-year Overall Survival (OS) Rate
  time frame: 5 years
  description: Percentage of participants who are still alive over a period of 5 years from the start of treatment.
  arm group label: Control group
  arm group type: Active Comparator
  description: In this arm, participants receive treatment plans directly from clinicians without the assistance of ChatGPT.
  arm group label: GPT-Assisted Group
  arm group type: Experimental
  description: In this arm, participants receive treatment plans from clinicians with the assistance of ChatGPT.
  intervention type: Other
  intervention name: Clinician-Directed Treatment Plan
  description: In this approach, clinicians do not employ any technological assistance and rely solely on their professional expertise and experience to formulate treatment plans for participants.
  arm group label: Control group
  intervention type: Other
  intervention name: ChatGPT-Assisted Treatment Plan
  description: In this approach, clinicians utilize the ChatGPT technological tool, formulating treatment plans for participants based on its suggestions and their own professional expertise.
  arm group label: GPT-Assisted Group
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Di Dong, PhD
  role: Principal Investigator
  affiliation: Institute of Automation, Chinese Academy of Sciences
  last name: Di Dong, PhD
  phone: +86 13811833760
  email: di.dong@ia.ac.cn
  facility: 
  status: Not yet recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  contact backup: 
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  contact backup: 
  facility: 
  status: Not yet recruiting
  contact: 
  facility: 
  status: Not yet recruiting
  contact: 
  country: China
  country: Germany
  country: Italy
  country: United States
  responsible party type: Principal Investigator
  investigator affiliation: Chinese Academy of Sciences
  investigator full name: Di Dong
  investigator title: Professor
  mesh term: Gastrointestinal Neoplasms
  mesh term: Digestive System Neoplasms
  mesh term: Neoplasms
  sharing ipd: Yes
  ipd description: Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.
  ipd info type: Study Protocol
  ipd info type: Statistical Analysis Plan (SAP)
  ipd info type: Analytic Code
  ipd time frame: Data will become available 6 months after study completion and will remain available for a period of 5 years.
  ipd access criteria: Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.
  ipd url: http://www.radiomics.net.cn/