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/NCT06023173</url>
    </required_header>
    <id_info>
      <org_study_id>DERBY</org_study_id>
      <nct_id>NCT06023173</nct_id>
    </id_info>
    <brief_title>Deep Radiomics-based Fusion Model Predicting Bevacizumab Treatment Response and Outcome in Patients With Colorectal Liver Metastases</brief_title>
    <official_title>Deep Radiomics-based Fusion Model Predicting Bevacizumab Treatment Response and Outcome in Patients With Colorectal Liver Metastases: a Multicenter Cohort Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Fudan University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Fudan University</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>
        This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was&#xD;
        able to identify patients with bevacizumab-sensitive unresectable colorectal cancer liver&#xD;
        metastases, providing a favorable approach for precise patient treatment.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Accurately predicting tumor response to targeted therapies is essential for guiding&#xD;
        personalized conversion therapy in patients with unresectable colorectal cancer liver&#xD;
        metastases (CRLM). Currently, tumor response evaluation criteria are based on assessments&#xD;
        made after at least 2-months treatment. Consequently, there is a compelling need to develop&#xD;
        baseline tools that can be used to guide therapy selection. Herein, the investigators&#xD;
        proposed a deep radiomics-based fusion model which demonstrates high accuracy in predicting&#xD;
        the efficacy of bevacizumab in CRLM patients. Further, the investigators observed a&#xD;
        significant and positive association between the predicted-responders and longer&#xD;
        progression-free survival as well as longer overall survival in CRLM patients treated with&#xD;
        bevacizumab. Moreover, the model exhibits high negative prediction value, indicating its&#xD;
        potential to accurately identify individuals who are unresponsive to bevacizumab. Thus, our&#xD;
        model provides a valuable baseline method for specifically identifying bevacizumab-sensitive&#xD;
        CRLM patients, which is offering a clinically convenient approach to guide precise patient&#xD;
        treatment.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Completed</overall_status>
    <start_date type="Actual">October 1, 2013</start_date>
    <completion_date type="Actual">January 1, 2023</completion_date>
    <primary_completion_date type="Actual">January 1, 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>ORR</measure>
      <time_frame>2013.10.1-2023.1.1</time_frame>
      <description>Objective response rate of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX</description>
    </primary_outcome>
    <primary_outcome>
      <measure>PFS</measure>
      <time_frame>2013.10.1-2023.1.1</time_frame>
      <description>Progression-free survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>OS</measure>
      <time_frame>2013.10.1-2023.1.1</time_frame>
      <description>Overall survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX</description>
    </secondary_outcome>
    <number_of_groups>4</number_of_groups>
    <enrollment type="Actual">307</enrollment>
    <condition>The Patients With CRLM Who Benefit More From Bevacizumab</condition>
    <arm_group>
      <arm_group_label>Training Cohort</arm_group_label>
      <description>This cohort was derived from Arm A (treated with FOLFOX + bevacizumab) of the BECOME studyand was used for model construction.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Negative Validation Cohort</arm_group_label>
      <description>The cohort was derived from Arm B (treated with FOLFOX) of the BECOME study , which demonstrated that the model specifically predicted the efficacy of bevacizumab.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Internal Validation Cohort</arm_group_label>
      <description>The cohort was derived from an independent Zhongshan Hospital cohort with the same treatment team and imaging instrumentation as the BECOME study, differing only in patient period, and was used for internal validation of the model.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>External Validation Cohort</arm_group_label>
      <description>The cohort was obtained from the Zhongshan Hospital - Xiamenand the First Affiliated Hospital of Wenzhou Medical University for external validation of the model.</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>Deep radiomics-based fusion model</intervention_name>
      <description>This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive CRLM, providing a favorable approach for precise patient treatment.</description>
      <arm_group_label>External Validation Cohort</arm_group_label>
      <arm_group_label>Internal Validation Cohort</arm_group_label>
      <arm_group_label>Negative Validation Cohort</arm_group_label>
      <arm_group_label>Training Cohort</arm_group_label>
      <other_name>deep learning model</other_name>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          In this multicenter cohort study, the investigators collected 307 patients with colorectal&#xD;
          cancer liver metastases. The training cohort and negative validation cohort were derived&#xD;
          from the BECOME study (NCT01972490), for whom baseline PET/CT images were available. The&#xD;
          internal validation cohort was derived from consecutive metastastic colorectal cancer&#xD;
          patients of the multi-disciplinary team (MDT) at Zhongshan Hospital (ZSH), share the same&#xD;
          MDT, surgical team, and PET/CT imaging equipment with training cohort, from 01 January 2018&#xD;
          to 31 December 2018. The external validation cohort came from the MDT of Zhongshan Hospital&#xD;
          - Xiamen and the First Hospital of Wenzhou Medical University, from 01 January 2020 to 31&#xD;
          December 2020&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. Age ≥ 18 years and ≤75 years;&#xD;
  &#xD;
            2. Patients were histologically confirmed for colorectal adenocarcinoma with unresectable&#xD;
               liver-limited or liver-dominant metastases&#xD;
  &#xD;
            3. PET/CT at baseline were available&#xD;
  &#xD;
            4. First line treated with FOLFOX+ bevacizumab.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. Resectable liver metastases;&#xD;
  &#xD;
            2. Wide-type KRAS/NRAS;&#xD;
  &#xD;
            3. No measurable liver metastasis;&#xD;
  &#xD;
            4. No efficacy assessment;&#xD;
  &#xD;
            5. No follow-up information.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>75 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Jianmin Xu, MD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Fudan University</affiliation>
    </overall_official>
    <location>
      <facility>
        <name>Department of General Surgery, Zhongshan Hospital, Fudan University</name>
        <address>
          <city>Shanghai</city>
          <country>China</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 29, 2023</study_first_submitted>
    <study_first_submitted_qc>August 29, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 5, 2023</study_first_posted>
    <last_update_submitted>September 13, 2023</last_update_submitted>
    <last_update_submitted_qc>September 13, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 14, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Fudan University</investigator_affiliation>
      <investigator_full_name>Xu jianmin</investigator_full_name>
      <investigator_title>Head of Colorectal Surgery</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Neoplasm Metastasis</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/NCT06023173
  org study id: DERBY
  nct id: NCT06023173
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was
        able to identify patients with bevacizumab-sensitive unresectable colorectal cancer liver
        metastases, providing a favorable approach for precise patient treatment.
  Accurately predicting tumor response to targeted therapies is essential for guiding
        personalized conversion therapy in patients with unresectable colorectal cancer liver
        metastases (CRLM). Currently, tumor response evaluation criteria are based on assessments
        made after at least 2-months treatment. Consequently, there is a compelling need to develop
        baseline tools that can be used to guide therapy selection. Herein, the investigators
        proposed a deep radiomics-based fusion model which demonstrates high accuracy in predicting
        the efficacy of bevacizumab in CRLM patients. Further, the investigators observed a
        significant and positive association between the predicted-responders and longer
        progression-free survival as well as longer overall survival in CRLM patients treated with
        bevacizumab. Moreover, the model exhibits high negative prediction value, indicating its
        potential to accurately identify individuals who are unresponsive to bevacizumab. Thus, our
        model provides a valuable baseline method for specifically identifying bevacizumab-sensitive
        CRLM patients, which is offering a clinically convenient approach to guide precise patient
        treatment.
  observational model: Cohort
  time perspective: Retrospective
  measure: ORR
  time frame: 2013.10.1-2023.1.1
  description: Objective response rate of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX
  measure: PFS
  time frame: 2013.10.1-2023.1.1
  description: Progression-free survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX
  measure: OS
  time frame: 2013.10.1-2023.1.1
  description: Overall survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX
  arm group label: Training Cohort
  description: This cohort was derived from Arm A (treated with FOLFOX + bevacizumab) of the BECOME studyand was used for model construction.
  arm group label: Negative Validation Cohort
  description: The cohort was derived from Arm B (treated with FOLFOX) of the BECOME study , which demonstrated that the model specifically predicted the efficacy of bevacizumab.
  arm group label: Internal Validation Cohort
  description: The cohort was derived from an independent Zhongshan Hospital cohort with the same treatment team and imaging instrumentation as the BECOME study, differing only in patient period, and was used for internal validation of the model.
  arm group label: External Validation Cohort
  description: The cohort was obtained from the Zhongshan Hospital - Xiamenand the First Affiliated Hospital of Wenzhou Medical University for external validation of the model.
  intervention type: Diagnostic Test
  intervention name: Deep radiomics-based fusion model
  description: This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive CRLM, providing a favorable approach for precise patient treatment.
  arm group label: External Validation Cohort
  arm group label: Internal Validation Cohort
  arm group label: Negative Validation Cohort
  arm group label: Training Cohort
  other name: deep learning model
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 75 Years
  healthy volunteers: No
  last name: Jianmin Xu, MD
  role: Principal Investigator
  affiliation: Fudan University
  facility: 
  country: China
  responsible party type: Principal Investigator
  investigator affiliation: Fudan University
  investigator full name: Xu jianmin
  investigator title: Head of Colorectal Surgery
  mesh term: Neoplasm Metastasis
  sharing ipd: No