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/NCT06041555</url>
    </required_header>
    <id_info>
      <org_study_id>69HCL23_0298</org_study_id>
      <secondary_id>2023-A01124-41</secondary_id>
      <nct_id>NCT06041555</nct_id>
    </id_info>
    <brief_title>MRI Guided Radiotherapy and Radiobiological Data: the ISRAR Database (Irm Sequences for Radiobiological Adaptative Radiotherapy)</brief_title>
    <acronym>ISRAR</acronym>
    <official_title>MRI Guided Radiotherapy and Radiobiological Data: the ISRAR Database (Irm Sequences for Radiobiological Adaptative Radiotherapy)</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Hospices Civils de Lyon</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Hospices Civils de Lyon</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 MRI linac Unity is a major technological evolution in radiotherapy combining a linear&#xD;
        accelerator with a 1.5T MRI (radiological quality). It allows to target the target volume&#xD;
        more precisely and to adapt the daily dose distribution according to variations in the&#xD;
        position and volume of the tumor, critical organs and the tumor response. In many studies&#xD;
        conducted in radiology, the analysis of specific MRI sequences, particularly in radiomics,&#xD;
        aims to characterize tumors and their sensitivity to treatment. Initial data show that in&#xD;
        radiotherapy, it would eventually be possible to characterize the radiosensitivity of healthy&#xD;
        and tumorous tissues. With linac 1.5T MRI, the performance of selected MRI sequences, at each&#xD;
        session, could make it possible to identify different levels of radiosensitivity within the&#xD;
        tumour. The reproduction of these sequences on a daily basis could make it possible to follow&#xD;
        the variations in radiosensitivity during the treatment. The final objectives would be: 1- to&#xD;
        adapt the doses of radiotherapy to each session with a modulation of the dose according to&#xD;
        the daily level of intra-tumor radiosensitivity, 2- to develop Artificial Intelligence (AI)&#xD;
        tools allowing an analysis sequences and the generation of 3D maps of intra-tumor&#xD;
        radiosensitivity, fast and suitable for carrying out a radiotherapy session.&#xD;
  &#xD;
        A first work carried out in collaboration with the CREATIS lab of the University Claude&#xD;
        Bernard Lyon 1 (UCBL1) made it possible to generate maps of tissue oxygenation from sequences&#xD;
        produced on the MRI linac Unity of the Hospices Civils de Lyon (T2* , IVIM, Carto T2 Multi&#xD;
        Echo-Gradient). Hypoxia is known to be the first factor of tumor resistance to irradiation. A&#xD;
        research program is structured in collaboration with UCBL1 in order to develop&#xD;
        radiobiological adaptive radiotherapy approaches, based on 3D maps of intra-tumoral hypoxia&#xD;
        and their variation during treatment. Several tumor locations were selected because of the&#xD;
        preponderant place of MRI in tumor characterization: prostate, cervix, kidney, ENT and&#xD;
        glioblastoma. Hypoxia is not the only factor of radioresistance. Changes in the&#xD;
        microenvironment could also impact the sensitivity of tumor cells. The program will therefore&#xD;
        also aim to optimize the maps initially based on hypoxia, by identifying other relevant&#xD;
        factors to be taken into account to define intra-tumor sensitivity.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        The MRI linac Unity is a major technological evolution in radiotherapy combining a linear&#xD;
        accelerator with a 1.5T MRI (radiological quality). It allows to target the target volume&#xD;
        more precisely and to adapt the daily dose distribution according to variations in the&#xD;
        position and volume of the tumor, critical organs and the tumor response. In many studies&#xD;
        conducted in radiology, the analysis of specific MRI sequences, particularly in radiomics,&#xD;
        aims to characterize tumors and their sensitivity to treatment. Initial data show that in&#xD;
        radiotherapy, it would eventually be possible to characterize the radiosensitivity of healthy&#xD;
        and tumorous tissues. With linac 1.5T MRI, the performance of selected MRI sequences, at each&#xD;
        session, could make it possible to identify different levels of radiosensitivity within the&#xD;
        tumour. The reproduction of these sequences on a daily basis could make it possible to follow&#xD;
        the variations in radiosensitivity during the treatment. The final objectives would be: 1- to&#xD;
        adapt the doses of radiotherapy to each session with a modulation of the dose according to&#xD;
        the daily level of intra-tumor radiosensitivity, 2- to develop Artificial Intelligence (AI)&#xD;
        tools allowing an analysis sequences and the generation of 3D maps of intra-tumor&#xD;
        radiosensitivity, fast and suitable for carrying out a radiotherapy session.&#xD;
  &#xD;
        A first work carried out in collaboration with the CREATIS lab of the University Claude&#xD;
        Bernard Lyon 1 (UCBL1) made it possible to generate maps of tissue oxygenation from sequences&#xD;
        produced on the MRI linac Unity of the Hospices Civils de Lyon (T2* , IVIM, Carto T2 Multi&#xD;
        Echo-Gradient). Hypoxia is known to be the first factor of tumor resistance to irradiation. A&#xD;
        research program is structured in collaboration with UCBL1 in order to develop&#xD;
        radiobiological adaptive radiotherapy approaches, based on 3D maps of intra-tumoral hypoxia&#xD;
        and their variation during treatment. Several tumor locations were selected because of the&#xD;
        preponderant place of MRI in tumor characterization: prostate, cervix, kidney, ENT and&#xD;
        glioblastoma. Hypoxia is not the only factor of radioresistance. Changes in the&#xD;
        microenvironment could also impact the sensitivity of tumor cells. The program will therefore&#xD;
        also aim to optimize the maps initially based on hypoxia, by identifying other relevant&#xD;
        factors to be taken into account to define intra-tumor sensitivity.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">October 10, 2023</start_date>
    <completion_date type="Anticipated">October 10, 2028</completion_date>
    <primary_completion_date type="Anticipated">October 10, 2028</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>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Hypoxia mapping</measure>
      <time_frame>At the end of the sequence of treatment of each patient, that is 13 weeks after the beginning of the treatment</time_frame>
      <description>Generate 3D maps of intra-tumoral hypoxia and characterize their evolution during treatment thanks to the establishment of a prospective database of MRI sequences</description>
    </primary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">600</enrollment>
    <condition>Prostate Cancer, Glioblastoma, Head and Neck Cancer, Kidney Cancer, Cervix Cancer</condition>
    <arm_group>
      <arm_group_label>ISRAR Cohort</arm_group_label>
      <description>All patient treated with the LINAC UNITY MRI radioguided radiotherapy for a prostate, kidney cancer, cervix, head and neck cancer or glioblastoma.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>3 MRI sequences (T2*, IVIM and Multi Echo-Gradient), without injection, are performed</intervention_name>
      <description>Generate 3D maps of intra-tumoral hypoxia and characterize their evolution during treatment thanks to the establishment of a prospective database of MRI sequences</description>
      <arm_group_label>ISRAR Cohort</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          All patient treated with the LINAC UNITY MRI radioguided radiotherapy for a prostate,&#xD;
          kidney cancer, cervix, head and neck cancer or glioblastoma.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  18years old or older&#xD;
  &#xD;
            -  diagnosed with prostate, kidney, cervix, head and neck cancer or glioblastoma&#xD;
  &#xD;
            -  indication of external radiotherapy&#xD;
  &#xD;
            -  treated with the Linac UNITY MRI guided radiotherapy at the centre hospitalier Lyon&#xD;
               Sud des Hospices Civils de Lyon&#xD;
  &#xD;
            -  agreement of the patient to participate&#xD;
  &#xD;
            -  affiliated to a social security system&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
          patient unable to keep a lying position during all the procedure&#xD;
  &#xD;
            -  patient under law restriction&#xD;
  &#xD;
            -  pregnant women or breastfeeding&#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_contact>
      <last_name>Olivier CHAPET, MD</last_name>
      <phone>4 78 86 42 51</phone>
      <phone_ext>+33</phone_ext>
      <email>Olivier.chapet@chu-lyon.fr</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Julien BERTILLET, Mr</last_name>
      <phone>4 27 85 63 01</phone>
      <phone_ext>+33</phone_ext>
      <email>Julien.berthiller@chu-lyon.fr</email>
    </overall_contact_backup>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>September 11, 2023</study_first_submitted>
    <study_first_submitted_qc>September 11, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 18, 2023</study_first_posted>
    <last_update_submitted>September 11, 2023</last_update_submitted>
    <last_update_submitted_qc>September 11, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 18, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Glioblastoma</mesh_term>
      <mesh_term>Kidney Neoplasms</mesh_term>
      <mesh_term>Uterine Cervical Neoplasms</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</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/NCT06041555
  org study id: 69HCL23_0298
  secondary id: 2023-A01124-41
  nct id: NCT06041555
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The MRI linac Unity is a major technological evolution in radiotherapy combining a linear
        accelerator with a 1.5T MRI (radiological quality). It allows to target the target volume
        more precisely and to adapt the daily dose distribution according to variations in the
        position and volume of the tumor, critical organs and the tumor response. In many studies
        conducted in radiology, the analysis of specific MRI sequences, particularly in radiomics,
        aims to characterize tumors and their sensitivity to treatment. Initial data show that in
        radiotherapy, it would eventually be possible to characterize the radiosensitivity of healthy
        and tumorous tissues. With linac 1.5T MRI, the performance of selected MRI sequences, at each
        session, could make it possible to identify different levels of radiosensitivity within the
        tumour. The reproduction of these sequences on a daily basis could make it possible to follow
        the variations in radiosensitivity during the treatment. The final objectives would be: 1- to
        adapt the doses of radiotherapy to each session with a modulation of the dose according to
        the daily level of intra-tumor radiosensitivity, 2- to develop Artificial Intelligence (AI)
        tools allowing an analysis sequences and the generation of 3D maps of intra-tumor
        radiosensitivity, fast and suitable for carrying out a radiotherapy session.
  
        A first work carried out in collaboration with the CREATIS lab of the University Claude
        Bernard Lyon 1 (UCBL1) made it possible to generate maps of tissue oxygenation from sequences
        produced on the MRI linac Unity of the Hospices Civils de Lyon (T2* , IVIM, Carto T2 Multi
        Echo-Gradient). Hypoxia is known to be the first factor of tumor resistance to irradiation. A
        research program is structured in collaboration with UCBL1 in order to develop
        radiobiological adaptive radiotherapy approaches, based on 3D maps of intra-tumoral hypoxia
        and their variation during treatment. Several tumor locations were selected because of the
        preponderant place of MRI in tumor characterization: prostate, cervix, kidney, ENT and
        glioblastoma. Hypoxia is not the only factor of radioresistance. Changes in the
        microenvironment could also impact the sensitivity of tumor cells. The program will therefore
        also aim to optimize the maps initially based on hypoxia, by identifying other relevant
        factors to be taken into account to define intra-tumor sensitivity.
  The MRI linac Unity is a major technological evolution in radiotherapy combining a linear
        accelerator with a 1.5T MRI (radiological quality). It allows to target the target volume
        more precisely and to adapt the daily dose distribution according to variations in the
        position and volume of the tumor, critical organs and the tumor response. In many studies
        conducted in radiology, the analysis of specific MRI sequences, particularly in radiomics,
        aims to characterize tumors and their sensitivity to treatment. Initial data show that in
        radiotherapy, it would eventually be possible to characterize the radiosensitivity of healthy
        and tumorous tissues. With linac 1.5T MRI, the performance of selected MRI sequences, at each
        session, could make it possible to identify different levels of radiosensitivity within the
        tumour. The reproduction of these sequences on a daily basis could make it possible to follow
        the variations in radiosensitivity during the treatment. The final objectives would be: 1- to
        adapt the doses of radiotherapy to each session with a modulation of the dose according to
        the daily level of intra-tumor radiosensitivity, 2- to develop Artificial Intelligence (AI)
        tools allowing an analysis sequences and the generation of 3D maps of intra-tumor
        radiosensitivity, fast and suitable for carrying out a radiotherapy session.
  
        A first work carried out in collaboration with the CREATIS lab of the University Claude
        Bernard Lyon 1 (UCBL1) made it possible to generate maps of tissue oxygenation from sequences
        produced on the MRI linac Unity of the Hospices Civils de Lyon (T2* , IVIM, Carto T2 Multi
        Echo-Gradient). Hypoxia is known to be the first factor of tumor resistance to irradiation. A
        research program is structured in collaboration with UCBL1 in order to develop
        radiobiological adaptive radiotherapy approaches, based on 3D maps of intra-tumoral hypoxia
        and their variation during treatment. Several tumor locations were selected because of the
        preponderant place of MRI in tumor characterization: prostate, cervix, kidney, ENT and
        glioblastoma. Hypoxia is not the only factor of radioresistance. Changes in the
        microenvironment could also impact the sensitivity of tumor cells. The program will therefore
        also aim to optimize the maps initially based on hypoxia, by identifying other relevant
        factors to be taken into account to define intra-tumor sensitivity.
  observational model: Cohort
  time perspective: Prospective
  measure: Hypoxia mapping
  time frame: At the end of the sequence of treatment of each patient, that is 13 weeks after the beginning of the treatment
  description: Generate 3D maps of intra-tumoral hypoxia and characterize their evolution during treatment thanks to the establishment of a prospective database of MRI sequences
  arm group label: ISRAR Cohort
  description: All patient treated with the LINAC UNITY MRI radioguided radiotherapy for a prostate, kidney cancer, cervix, head and neck cancer or glioblastoma.
  intervention type: Other
  intervention name: 3 MRI sequences (T2*, IVIM and Multi Echo-Gradient), without injection, are performed
  description: Generate 3D maps of intra-tumoral hypoxia and characterize their evolution during treatment thanks to the establishment of a prospective database of MRI sequences
  arm group label: ISRAR Cohort
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Olivier CHAPET, MD
  phone: 4 78 86 42 51
  phone ext: +33
  email: Olivier.chapet@chu-lyon.fr
  last name: Julien BERTILLET, Mr
  phone: 4 27 85 63 01
  phone ext: +33
  email: Julien.berthiller@chu-lyon.fr
  responsible party type: Sponsor
  mesh term: Glioblastoma
  mesh term: Kidney Neoplasms
  mesh term: Uterine Cervical Neoplasms
  sharing ipd: Undecided