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/NCT06002711</url>
    </required_header>
    <id_info>
      <org_study_id>RenJiH-Rad-IIT-2023-0141</org_study_id>
      <secondary_id>IIT-2023-0141</secondary_id>
      <nct_id>NCT06002711</nct_id>
    </id_info>
    <brief_title>Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas: A Multi-Center Study</brief_title>
    <official_title>Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas: A Multi-Center Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>RenJi Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>RenJi Hospital</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>
        1. To retrospectively explore the feasibility of multi-dimensional heterogeneity imaging&#xD;
             features of MRI in predicting the status of key gene mutations in high-grade gliomas;&#xD;
  &#xD;
          2. To prospectively explore the correlation between multi-dimensional heterogeneous MRI&#xD;
             image features and prognosis of high-grade glioma patients.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Glioblastoma, the most prevalent primary intracranial tumor, is characterized by its&#xD;
        formidable therapeutic resistance, primarily attributed to its intrinsic heterogeneity. This&#xD;
        heightened heterogeneity is not solely confined to inter-tumoral variations across different&#xD;
        individuals but also encompasses considerable intratumoral diversity. The pervasive notion&#xD;
        among the scientific community posits that this intratumoral heterogeneity substantiates an&#xD;
        endogenous mechanism for drug resistance, thereby exerting substantial influence upon the&#xD;
        design of clinical trials, prognostic prediction, and patient outcomes. Preceding&#xD;
        methodologies for assessment are beleaguered by a constellation of challenges, impeding&#xD;
        precise evaluation of global tumor heterogeneity and necessitating innovative modalities to&#xD;
        surmount this impasse. MRI imaging, endowed with non-invasiveness and user-friendliness,&#xD;
        surmounts the biases of single-point sampling, enabling comprehensive and dynamic appraisal&#xD;
        of glioblastomas. Notably, high-grade gliomas exhibit pronounced microenvironmental pressure&#xD;
        selectivity and adaptability, akin to species occupation within distinct ecological niches.&#xD;
        This phenomenon, termed &quot;habitat,&quot; manifests as a visual representation of the tumor's&#xD;
        spatial distribution and temporal evolution, thus facilitating real-time, longitudinal&#xD;
        monitoring. Given the substantial imaging heterogeneity inherent to glioblastomas, they stand&#xD;
        as an opportune subject for habitat imaging techniques compared to their neoplastic&#xD;
        counterparts.&#xD;
  &#xD;
        The present investigation endeavors to leverage multi-center, multi-dimensional MRI spatial&#xD;
        heterogeneity analysis to predict pivotal genes germane to prognosis and therapy in&#xD;
        high-grade gliomas, ultimately constructing a stratified prognostic model for afflicted&#xD;
        patients.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 1, 2023</start_date>
    <completion_date type="Anticipated">December 31, 2027</completion_date>
    <primary_completion_date type="Anticipated">September 30, 2026</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>Other</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Survival prediction model</measure>
      <time_frame>2025.06-2026.09</time_frame>
      <description>Survival prediction efficiency of the included samples</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Time-depended ROC curve</measure>
      <time_frame>2025.06-2026.09</time_frame>
      <description>A time-dependent ROC curve which will be drawn according to the survival analysis.</description>
    </primary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">500</enrollment>
    <condition>High-grade Glioma</condition>
    <arm_group>
      <arm_group_label>retrospective study cohort</arm_group_label>
      <description>In the retrospective study, patient cases will be gathered from multi-center repositories, where surgical cases will be confirmed to be high-grade gliomas and will undergo preoperative contrast-enhanced MRI examinations. These patients will possess comprehensive clinical, pathological, and genetic data.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Prospective study cohort</arm_group_label>
      <description>The prospective study will encompass a cohort of individuals who are clinically suspected to have high-grade gliomas and will undergo multimodal MRI imaging. Subsequent to surgery, their postoperative pathology will confirm the diagnosis of high-grade gliomas. Following the surgical intervention, these patients will undergo standard procedures for radiotherapy and chemotherapy, as well as regular follow-up assessments.</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>MR scanning; Clinical data collection</intervention_name>
      <description>Multi-dimensional spatial heterogeneity analysis of MRI</description>
      <arm_group_label>Prospective study cohort</arm_group_label>
      <arm_group_label>retrospective study cohort</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          High-grade glioma patients were recruited from multiple research centers, including Renji&#xD;
          Hospital, Shanghai Jiao Tong University School of Medicine, Huashan Hospital, Fudan&#xD;
          University; Shanghai Jing'an District Central Hospital, and Nantong First People's&#xD;
          Hospital.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
          Retrospective Study:&#xD;
  &#xD;
            1. Participants aged 18 to 70 years, of any gender.&#xD;
  &#xD;
            2. Confirmed postoperative pathology of adult diffuse glioma (WHO Grade III-IV).&#xD;
  &#xD;
            3. Standard MR contrast-enhanced imaging performed within 10 days before surgery.&#xD;
  &#xD;
            4. No history of prior radiotherapy or chemotherapy before surgery.&#xD;
  &#xD;
            5. Absence of concurrent significant comorbidities or other tumors.&#xD;
  &#xD;
            6. Presence of molecular testing results (including IDH, MGMT, 1p19q, TERT, CDKN2A/B,&#xD;
               BRAF).&#xD;
  &#xD;
            7. Availability of comprehensive clinical and follow-up data.&#xD;
  &#xD;
          Prospective Study:&#xD;
  &#xD;
            1. Participants aged 18 to 70 years, of any gender.&#xD;
  &#xD;
            2. Clinically suspected to have high-grade gliomas preoperatively, with final pathology&#xD;
               confirming high-grade gliomas.&#xD;
  &#xD;
            3. Stable vital signs and capable of cooperating for a 40-minute MR scan.&#xD;
  &#xD;
            4. Absence of significant underlying medical conditions or history of other tumors.&#xD;
  &#xD;
            5. Documentation of informed consent through a signed consent form.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
          Retrospective Study:&#xD;
  &#xD;
            1. MRI images with artifacts or presence of intratumoral hemorrhage.&#xD;
  &#xD;
            2. Incomplete clinical data available.&#xD;
  &#xD;
          Prospective Study:&#xD;
  &#xD;
            1. Individuals with claustrophobia or other reasons unable to undergo MRI scans.&#xD;
  &#xD;
            2. History of allergic reactions to MRI contrast agents.&#xD;
  &#xD;
            3. Inappropriate for prolonged MRI scans due to other reasons.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>70 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Yan Zhou, MD,PhD</last_name>
      <phone>+86-021-68383086</phone>
      <email>clare1475@hotmail.com</email>
    </overall_contact>
    <location>
      <facility>
        <name>Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University</name>
        <address>
          <city>Shanghai</city>
          <state>Shanghai</state>
          <zip>200127</zip>
          <country>China</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <reference>
      <citation>Cao M, Wang X, Liu F, Xue K, Dai Y, Zhou Y. A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status. Eur Radiol. 2023 Apr;33(4):2871-2880. doi: 10.1007/s00330-022-09212-5. Epub 2022 Nov 8.</citation>
      <PMID>36346441</PMID>
    </reference>
    <reference>
      <citation>Cao M, Suo S, Zhang X, Wang X, Xu J, Yang W, Zhou Y. Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach. Biomed Res Int. 2021 Jan 22;2021:1235314. doi: 10.1155/2021/1235314. eCollection 2021.</citation>
      <PMID>33553421</PMID>
    </reference>
    <reference>
      <citation>Cao M, Ding W, Han X, Suo S, Sun Y, Wang Y, Qu J, Zhang X, Zhou Y. Brain T1rho mapping for grading and IDH1 gene mutation detection of gliomas: a preliminary study. J Neurooncol. 2019 Jan;141(1):245-252. doi: 10.1007/s11060-018-03033-7. Epub 2018 Nov 9.</citation>
      <PMID>30414094</PMID>
    </reference>
    <reference>
      <citation>Dextraze K, Saha A, Kim D, Narang S, Lehrer M, Rao A, Narang S, Rao D, Ahmed S, Madhugiri V, Fuller CD, Kim MM, Krishnan S, Rao G, Rao A. Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma. Oncotarget. 2017 Dec 5;8(68):112992-113001. doi: 10.18632/oncotarget.22947. eCollection 2017 Dec 22.</citation>
      <PMID>29348883</PMID>
    </reference>
    <reference>
      <citation>Park JE, Kim HS, Kim N, Park SY, Kim YH, Kim JH. Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Is Associated with Patient Outcomes in IDH-Wildtype Glioblastoma. Clin Cancer Res. 2021 Jan 1;27(1):237-245. doi: 10.1158/1078-0432.CCR-20-2156. Epub 2020 Oct 7.</citation>
      <PMID>33028594</PMID>
    </reference>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 16, 2023</study_first_submitted>
    <study_first_submitted_qc>August 16, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 21, 2023</study_first_posted>
    <last_update_submitted>August 16, 2023</last_update_submitted>
    <last_update_submitted_qc>August 16, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 21, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>glioma; MRI; spatial heterogeneity; habitat</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Glioma</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/NCT06002711
  org study id: RenJiH-Rad-IIT-2023-0141
  secondary id: IIT-2023-0141
  nct id: NCT06002711
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  1. To retrospectively explore the feasibility of multi-dimensional heterogeneity imaging
             features of MRI in predicting the status of key gene mutations in high-grade gliomas;
  
          2. To prospectively explore the correlation between multi-dimensional heterogeneous MRI
             image features and prognosis of high-grade glioma patients.
  Glioblastoma, the most prevalent primary intracranial tumor, is characterized by its
        formidable therapeutic resistance, primarily attributed to its intrinsic heterogeneity. This
        heightened heterogeneity is not solely confined to inter-tumoral variations across different
        individuals but also encompasses considerable intratumoral diversity. The pervasive notion
        among the scientific community posits that this intratumoral heterogeneity substantiates an
        endogenous mechanism for drug resistance, thereby exerting substantial influence upon the
        design of clinical trials, prognostic prediction, and patient outcomes. Preceding
        methodologies for assessment are beleaguered by a constellation of challenges, impeding
        precise evaluation of global tumor heterogeneity and necessitating innovative modalities to
        surmount this impasse. MRI imaging, endowed with non-invasiveness and user-friendliness,
        surmounts the biases of single-point sampling, enabling comprehensive and dynamic appraisal
        of glioblastomas. Notably, high-grade gliomas exhibit pronounced microenvironmental pressure
        selectivity and adaptability, akin to species occupation within distinct ecological niches.
        This phenomenon, termed "habitat," manifests as a visual representation of the tumor's
        spatial distribution and temporal evolution, thus facilitating real-time, longitudinal
        monitoring. Given the substantial imaging heterogeneity inherent to glioblastomas, they stand
        as an opportune subject for habitat imaging techniques compared to their neoplastic
        counterparts.
  
        The present investigation endeavors to leverage multi-center, multi-dimensional MRI spatial
        heterogeneity analysis to predict pivotal genes germane to prognosis and therapy in
        high-grade gliomas, ultimately constructing a stratified prognostic model for afflicted
        patients.
  observational model: Cohort
  time perspective: Other
  measure: Survival prediction model
  time frame: 2025.06-2026.09
  description: Survival prediction efficiency of the included samples
  measure: Time-depended ROC curve
  time frame: 2025.06-2026.09
  description: A time-dependent ROC curve which will be drawn according to the survival analysis.
  arm group label: retrospective study cohort
  description: In the retrospective study, patient cases will be gathered from multi-center repositories, where surgical cases will be confirmed to be high-grade gliomas and will undergo preoperative contrast-enhanced MRI examinations. These patients will possess comprehensive clinical, pathological, and genetic data.
  arm group label: Prospective study cohort
  description: The prospective study will encompass a cohort of individuals who are clinically suspected to have high-grade gliomas and will undergo multimodal MRI imaging. Subsequent to surgery, their postoperative pathology will confirm the diagnosis of high-grade gliomas. Following the surgical intervention, these patients will undergo standard procedures for radiotherapy and chemotherapy, as well as regular follow-up assessments.
  intervention type: Diagnostic Test
  intervention name: MR scanning; Clinical data collection
  description: Multi-dimensional spatial heterogeneity analysis of MRI
  arm group label: Prospective study cohort
  arm group label: retrospective study cohort
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 70 Years
  healthy volunteers: No
  last name: Yan Zhou, MD,PhD
  phone: +86-021-68383086
  email: clare1475@hotmail.com
  facility: 
  country: China
  citation: Cao M, Wang X, Liu F, Xue K, Dai Y, Zhou Y. A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status. Eur Radiol. 2023 Apr;33(4):2871-2880. doi: 10.1007/s00330-022-09212-5. Epub 2022 Nov 8.
  PMID: 36346441
  citation: Cao M, Suo S, Zhang X, Wang X, Xu J, Yang W, Zhou Y. Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach. Biomed Res Int. 2021 Jan 22;2021:1235314. doi: 10.1155/2021/1235314. eCollection 2021.
  PMID: 33553421
  citation: Cao M, Ding W, Han X, Suo S, Sun Y, Wang Y, Qu J, Zhang X, Zhou Y. Brain T1rho mapping for grading and IDH1 gene mutation detection of gliomas: a preliminary study. J Neurooncol. 2019 Jan;141(1):245-252. doi: 10.1007/s11060-018-03033-7. Epub 2018 Nov 9.
  PMID: 30414094
  citation: Dextraze K, Saha A, Kim D, Narang S, Lehrer M, Rao A, Narang S, Rao D, Ahmed S, Madhugiri V, Fuller CD, Kim MM, Krishnan S, Rao G, Rao A. Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma. Oncotarget. 2017 Dec 5;8(68):112992-113001. doi: 10.18632/oncotarget.22947. eCollection 2017 Dec 22.
  PMID: 29348883
  citation: Park JE, Kim HS, Kim N, Park SY, Kim YH, Kim JH. Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Is Associated with Patient Outcomes in IDH-Wildtype Glioblastoma. Clin Cancer Res. 2021 Jan 1;27(1):237-245. doi: 10.1158/1078-0432.CCR-20-2156. Epub 2020 Oct 7.
  PMID: 33028594
  responsible party type: Sponsor
  mesh term: Glioma
  sharing ipd: Undecided