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/NCT06000046</url>
    </required_header>
    <id_info>
      <org_study_id>479272</org_study_id>
      <secondary_id>CIV-NO-23-04-042915</secondary_id>
      <secondary_id>REK KULMU A # 479272</secondary_id>
      <nct_id>NCT06000046</nct_id>
    </id_info>
    <brief_title>PROVIZ - a Machine Learning Software, to Support Targeting of Prostate Biopsies on MR Images in Biopsy-naive Patients</brief_title>
    <official_title>A Proof-of-technology, Pilot, Prospective Clinical Study to Investigate the Feasibility and Performance of PROVIZ-a Radiomics-based Machine Learning Software, to Support Targeting of Prostate Biopsies on MRI Images in Biopsy-naive Patients</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Norwegian University of Science and Technology</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>St. Olavs Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Norwegian University of Science and Technology</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>
      <is_us_export>No</is_us_export>
    </oversight_info>
    <brief_summary>
      <textblock>
        To perform a traditional feasibility clinical investigation, as defined in ISO 14155:2020, to&#xD;
        investigate preliminary feasibility, safety, and clinical performance information of a&#xD;
        near-final design of the investigational software. This will be performed through a&#xD;
        prospective clinical study on biopsy naïve men with suspected prostate cancer examined with&#xD;
        MRI at St. Olavs Hospital, Trondheim, Norway, in order to adequately plan an appropriate&#xD;
        pivotal clinical investigation.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        In this prospective study, after referral for suspected prostate cancer, the patient is&#xD;
        scanned with magnetic resonance imaging (MRI) in accordance with guidelines of the&#xD;
        standardized healthcare pathway. For consenting patients, the images are interpreted in two&#xD;
        ways: first, in the conventional manner, i.e., manually by a radiologist, to determine&#xD;
        whether clinically significant cancer is suspected. If so, the radiologist will delineate the&#xD;
        suspicious lesions. In the second approach, the software will perform the same task as the&#xD;
        radiologist, but automatically. If either or both interpretations point to significant&#xD;
        cancer, the patient will be sent for targeted biopsy sampling. Histopathologic evaluation of&#xD;
        the samples will then determine whether significant cancer is present in each of the targeted&#xD;
        lesions (delineated by the radiologist, software, or both).&#xD;
  &#xD;
        Feasibility is evaluated by measuring the overall failure rate of the software. This is&#xD;
        measured by the technical performance log automatically generated by the software, which&#xD;
        records all errors and failures during the study. If the record showed an overall failure&#xD;
        rate less than 10% across all subjects, the software will be considered feasible.&#xD;
  &#xD;
        The safety of the software is evaluated by the records of the serious adverse device effects&#xD;
        (SADEs) of the software during the study. The software will be considered safe with no&#xD;
        occurrence of death or serious injury.&#xD;
  &#xD;
        The results of the histopathological evaluation will be used to evaluate the performance of&#xD;
        the investigational software. This allows comparisons to be made between results obtained&#xD;
        with the traditional manual approach alone, with the software alone, and with the manual&#xD;
        approach assisted by the software. Statistical analysis will be performed to determine if&#xD;
        there are significant differences and if the software adds value.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">October 2023</start_date>
    <completion_date type="Anticipated">February 2024</completion_date>
    <primary_completion_date type="Anticipated">January 2024</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>N/A</allocation>
      <intervention_model>Single Group Assignment</intervention_model>
      <primary_purpose>Diagnostic</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Feasibility of the software in prospective study in a relevant clinical environment.</measure>
      <time_frame>Approximately 45 days. Actual time in clinic is approximately 1.5 hours.</time_frame>
      <description>The percentage of occurrence of a technical problem in the investigational software that hinders its use is less than 10% across all subjects.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Safety of the software in prospective study in a relevant clinical environment.</measure>
      <time_frame>Approximately 45 days. Actual time in clinic is approximately 1.5 hours.</time_frame>
      <description>There are no death or serious harm associated with an adverse device effect (ADE) of the investigational software.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Performance of the software in prospective study in a relevant clinical environment.</measure>
      <time_frame>Approximately 45 days. Actual time in clinic is approximately 1.5 hours.</time_frame>
      <description>Comparable preliminary detection rate of clinically significant prostate cancer lesions from the software to the radiologist.</description>
    </secondary_outcome>
    <number_of_arms>1</number_of_arms>
    <enrollment type="Anticipated">80</enrollment>
    <condition>Prostate Cancer Diagnosis</condition>
    <arm_group>
      <arm_group_label>MRI for prostate cancer</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>Biopsy-naive men undergoing MRI for suspected prostate cancer via the Norwegian standardized care pathway. The MRI images will then be evaluated by the radiologist and a machine-learning based diagnosis and detection system.</description>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>Automatic image analysis</intervention_name>
      <description>After referral for suspected prostate cancer, the patient is scanned with magnetic resonance imaging (MRI) in accordance with guidelines of the standardized healthcare pathway. For consenting patients, the images are interpreted in two ways: first, in the conventional manner, i.e., manually by a radiologist, to determine whether clinically significant cancer is suspected. If so, the radiologist will delineate the suspicious lesions. In the second approach, the software will perform the same task as the radiologist, but automatically. If either or both interpretations point to significant cancer, the patient will be sent for targeted biopsy sampling. Histopathologic evaluation of the samples will then determine whether significant cancer is present in each of the targeted lesions (delineated by the radiologist, software, or both).</description>
      <arm_group_label>MRI for prostate cancer</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Biopsy-naive men undergoing MRI examination for suspected prostate cancer via the&#xD;
               Norwegian standardized care pathway.&#xD;
  &#xD;
            -  Patients who give consent to participate during the enrollment period.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients who have undergone a biopsy for prostate cancer in the past 3 years.&#xD;
  &#xD;
            -  Patients who currently enrolled in an active surveillance program for prostate cancer.&#xD;
  &#xD;
            -  Patients who have had hip replacements that may affect the quality of the image.&#xD;
  &#xD;
            -  Patients with claustrophobia.&#xD;
  &#xD;
            -  Patients who intolerance to glucagon or buscopan.&#xD;
  &#xD;
            -  Patients who unable to sign the informed consent themselves.&#xD;
        </textblock>
      </criteria>
      <gender>Male</gender>
      <minimum_age>N/A</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Tone Frost Bathen, Prof.</last_name>
      <role>Principal Investigator</role>
      <affiliation>Norwegian University of Science and Technology (NTNU)</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Tone Frost Bathen, Prof.</last_name>
      <phone>+4773551355</phone>
      <email>tone.f.bathen@ntnu.no</email>
    </overall_contact>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 14, 2023</study_first_submitted>
    <study_first_submitted_qc>August 14, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 21, 2023</study_first_posted>
    <last_update_submitted>September 5, 2023</last_update_submitted>
    <last_update_submitted_qc>September 5, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Prostate</keyword>
    <keyword>Magnetic Resonance Imaging</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Machine Learning</keyword>
    <keyword>Computer-Aided Diagnosis</keyword>
    <keyword>Radiomics</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Disease</mesh_term>
    </condition_browse>
    <!-- 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/NCT06000046
  org study id: 479272
  secondary id: CIV-NO-23-04-042915
  secondary id: REK KULMU A # 479272
  nct id: NCT06000046
  lead sponsor: 
  collaborator: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  is us export: No
  To perform a traditional feasibility clinical investigation, as defined in ISO 14155:2020, to
        investigate preliminary feasibility, safety, and clinical performance information of a
        near-final design of the investigational software. This will be performed through a
        prospective clinical study on biopsy naïve men with suspected prostate cancer examined with
        MRI at St. Olavs Hospital, Trondheim, Norway, in order to adequately plan an appropriate
        pivotal clinical investigation.
  In this prospective study, after referral for suspected prostate cancer, the patient is
        scanned with magnetic resonance imaging (MRI) in accordance with guidelines of the
        standardized healthcare pathway. For consenting patients, the images are interpreted in two
        ways: first, in the conventional manner, i.e., manually by a radiologist, to determine
        whether clinically significant cancer is suspected. If so, the radiologist will delineate the
        suspicious lesions. In the second approach, the software will perform the same task as the
        radiologist, but automatically. If either or both interpretations point to significant
        cancer, the patient will be sent for targeted biopsy sampling. Histopathologic evaluation of
        the samples will then determine whether significant cancer is present in each of the targeted
        lesions (delineated by the radiologist, software, or both).
  
        Feasibility is evaluated by measuring the overall failure rate of the software. This is
        measured by the technical performance log automatically generated by the software, which
        records all errors and failures during the study. If the record showed an overall failure
        rate less than 10% across all subjects, the software will be considered feasible.
  
        The safety of the software is evaluated by the records of the serious adverse device effects
        (SADEs) of the software during the study. The software will be considered safe with no
        occurrence of death or serious injury.
  
        The results of the histopathological evaluation will be used to evaluate the performance of
        the investigational software. This allows comparisons to be made between results obtained
        with the traditional manual approach alone, with the software alone, and with the manual
        approach assisted by the software. Statistical analysis will be performed to determine if
        there are significant differences and if the software adds value.
  allocation: N/A
  intervention model: Single Group Assignment
  primary purpose: Diagnostic
  masking: None (Open Label)
  measure: Feasibility of the software in prospective study in a relevant clinical environment.
  time frame: Approximately 45 days. Actual time in clinic is approximately 1.5 hours.
  description: The percentage of occurrence of a technical problem in the investigational software that hinders its use is less than 10% across all subjects.
  measure: Safety of the software in prospective study in a relevant clinical environment.
  time frame: Approximately 45 days. Actual time in clinic is approximately 1.5 hours.
  description: There are no death or serious harm associated with an adverse device effect (ADE) of the investigational software.
  measure: Performance of the software in prospective study in a relevant clinical environment.
  time frame: Approximately 45 days. Actual time in clinic is approximately 1.5 hours.
  description: Comparable preliminary detection rate of clinically significant prostate cancer lesions from the software to the radiologist.
  arm group label: MRI for prostate cancer
  arm group type: Experimental
  description: Biopsy-naive men undergoing MRI for suspected prostate cancer via the Norwegian standardized care pathway. The MRI images will then be evaluated by the radiologist and a machine-learning based diagnosis and detection system.
  intervention type: Device
  intervention name: Automatic image analysis
  description: After referral for suspected prostate cancer, the patient is scanned with magnetic resonance imaging (MRI) in accordance with guidelines of the standardized healthcare pathway. For consenting patients, the images are interpreted in two ways: first, in the conventional manner, i.e., manually by a radiologist, to determine whether clinically significant cancer is suspected. If so, the radiologist will delineate the suspicious lesions. In the second approach, the software will perform the same task as the radiologist, but automatically. If either or both interpretations point to significant cancer, the patient will be sent for targeted biopsy sampling. Histopathologic evaluation of the samples will then determine whether significant cancer is present in each of the targeted lesions (delineated by the radiologist, software, or both).
  arm group label: MRI for prostate cancer
  criteria: 
  gender: Male
  minimum age: N/A
  maximum age: N/A
  healthy volunteers: No
  last name: Tone Frost Bathen, Prof.
  role: Principal Investigator
  affiliation: Norwegian University of Science and Technology (NTNU)
  last name: Tone Frost Bathen, Prof.
  phone: +4773551355
  email: tone.f.bathen@ntnu.no
  responsible party type: Sponsor
  mesh term: Disease