<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/NCT04407039</url>
  </required_header>
  <id_info>
    <org_study_id>YXLL-KY-2020 (009)</org_study_id>
    <nct_id>NCT04407039</nct_id>
  </id_info>
  <brief_title>Establishment and Evaluation of Multimodal Image Recognition System of Glioma Based on Deep Learning</brief_title>
  <official_title>Establishment and Evaluation of Multimodal Image Recognition System of Glioma Based on Deep Learning</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Tao Xin</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Qianfoshan Hospital</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>Yes</is_fda_regulated_device>
    <is_us_export>No</is_us_export>
  </oversight_info>
  <brief_summary>
    <textblock>
      Research purposes:&#xD;
&#xD;
        1. To obtain the metabolic characteristics of glioma molecular imaging through a multimodal&#xD;
           image recognition system.&#xD;
&#xD;
        2. To determine whether molecular imaging metabolic parameters can characterize the&#xD;
           molecular typing of glioma by analyzing the relationship between metabolic parameters&#xD;
           and tumor subtypes&#xD;
&#xD;
        3. To get metabolic classification based on metabolic parameters of glioma molecular&#xD;
           imaging, and to identify the relationship between metabolic subtypes and surgical&#xD;
           resection, radiotherapy and chemotherapy, and prognosis and further refine the molecular&#xD;
           classification of glioma.&#xD;
&#xD;
      Research Background:&#xD;
&#xD;
      Glioma is the most common primary intracranial malignant tumor, accounting for 80% of central&#xD;
      nervous system malignant tumors. It is highly invasive, with a surgical recurrence rate of up&#xD;
      to 90%. The prognosis is extremely poor, which has caused a great burden. There are different&#xD;
      molecular subtypes of glioma with distinct molecular biological characteristics, resulting in&#xD;
      various prognosis of patients. With the continuous development of basic and clinical research&#xD;
      of glioma and the advent of various new drugs and treatment technologies, molecular&#xD;
      pathological diagnosis based on the individual level of glioma patients is particularly&#xD;
      important. Clarifying the molecular pathology type before surgery will help the clinical&#xD;
      diagnosis and prognostic judgment of glioma, and is of great significance for the&#xD;
      optimization of treatment options.&#xD;
&#xD;
      Based on the establishment of glioma molecular typing system, the project team use&#xD;
      noninvasive molecular imaging technology to clarify the characteristics of molecular subsets&#xD;
      of glioma based on the tumor metabolic parameters. Through combining deep learning-based&#xD;
      target detection and image recognition with big data analysis, it has great potential in the&#xD;
      clinical research of glioma diagnosis, prognosis and treatment options, which could provide a&#xD;
      scientific basis for the establishment and promotion of glioma molecular analysis and&#xD;
      recognition system.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      1. Multimodal Image Recognition System of Glioma Molecular imaging is the use of imaging&#xD;
           methods to display specific molecules at the tissue level, cellular level, and&#xD;
           subcellular level, reflecting changes in the molecular level in the living state, and&#xD;
           performing qualitative and quantitative researches on the biological behavior of the&#xD;
           imaging aspect. Multimodal Image Recognition System includes Positron Emission Computed&#xD;
           Tomography (PET), Magnetic Resonance Imaging (MRI) and Magnetic Resonance Hydrogen&#xD;
           Proton Spectroscopy (1H-MRS). 1H-MRS quantifies the choline (Cho) and creatine (Cr) in&#xD;
           the spectrum, N-acetylaspartate acid (NAA), qualification (Lip) and lactic acid (Lac)&#xD;
           and other substances to reflect changes in cell metabolites, and then speculate on the&#xD;
           occurrence and development of tumors. Imaging of the glucose analog 18F-labeled&#xD;
           fluorodeoxyglucose (18FDG) by positron emission tomography (PET) is the gold standard&#xD;
           for the assessment of cerebral glucose metabolism in vivo. However, single imaging&#xD;
           method often has limitations, and it is difficult to meet the requirements for&#xD;
           sensitivity, specificity, and target at the same time. Combing PET / CT with 1H- MRS of&#xD;
           new generation imaging technologies can overcome the shortcomings of a single imaging&#xD;
           method and achieve complementary advantages and provide effective informations&#xD;
&#xD;
        2. Risks and Benefits of Participants (1) Risks: In addition to collecting information&#xD;
           above, the main risk is discomfort that may occur during the conversation. Since this&#xD;
           study will not intervene in routine diagnosis and treatment, there are no other special&#xD;
           risks other than the possible discomfort and adverse reactions mentioned above expected&#xD;
           during treatment. (2) Benefits: No direct benefits, but may provide useful informations&#xD;
           for disease research.&#xD;
&#xD;
        3. Statistical Methods All data (clinical data, imaging data) will be established in a&#xD;
           special database. In order to control the imbalance of many different confounding&#xD;
           factors in the formed population, the test will use the propensity score method to&#xD;
           achieve the purpose of controlling confounding and improve the quality of real-world&#xD;
           evidence, close to the effect of randomization.&#xD;
&#xD;
        4. Criteria for aborting trials and ending clinical trials&#xD;
&#xD;
      1. Criteria for aborting trials The investigator judges that continuing the trial is not good&#xD;
      for the patient; During the course of the study, the patient is unwilling to continue the&#xD;
      clinical study, and makes a withdrawal request to the competent doctor and requests to cancel&#xD;
      the informed consent.&#xD;
&#xD;
      2. Criteria for ending clinical research: If serious safety problems occur during the study,&#xD;
      the study should be terminated in time; The ethics committee requested to terminate the trial&#xD;
      after the study; The health administrative department ordered the termination of clinical&#xD;
      research for some reason; Significant deviations have occurred in the design or&#xD;
      implementation of the study protocol and it is difficult to evaluate the results.&#xD;
&#xD;
      5. Participant's Code, Storage Procedure of Case Report, Data Management and Guarantee of&#xD;
      Test Reliability&#xD;
&#xD;
        1. After the participants enter the cohort after signing the informed consent, they will be&#xD;
           sorted and assigned a serial number by the research secretary according to the time when&#xD;
           the informed consent is signed. Therefore, the first patient enrolled is coded 001.&#xD;
&#xD;
        2. The patient's case report form will be made into a special patient manual, which will be&#xD;
           kept by the research secretary and clinical staff for data collection and follow-up. Any&#xD;
           observations and inspection results during the test should be entered into the form and&#xD;
           database in a timely, accurate, complete, clear, standardized, and true manner. After&#xD;
           the subject's follow-up is over, hand over and perform data collection and analysis. The&#xD;
           subject's imaging data will be regularly uploaded by the research secretary to the&#xD;
           intelligent IT virtual storage system based on the cluster. The system divides&#xD;
           neurosurgery and subjects into specific storage partitions.&#xD;
&#xD;
        3. Each researcher has the authority to consult and modify the subject's storage partition.&#xD;
           The storage system automatically records the changes of the stored data, thus ensuring&#xD;
           the traceability of the data. The investigator should agree to keep all research data&#xD;
           (including original records, informed consent of all patients, all case report forms,&#xD;
           etc.), and the special counter shall be locked and stored until 5 years after the end of&#xD;
           the trial.&#xD;
&#xD;
      6. Safety&#xD;
&#xD;
        1. Serious adverse event: death; medical personnel believe what is life-threatening; events&#xD;
           requiring hospitalization or extension of hospital stay; persistent or severe disability&#xD;
           or disability.&#xD;
&#xD;
        2. Reporting methods and measures after the occurrence of the adverse events above, the&#xD;
           medical staff should first report to the main investigator and fill out the adverse&#xD;
           event report form. The adverse reaction report includes the diagnosis and treatment of&#xD;
           the patient, the time of the event, the cause of the event and the treatment after the&#xD;
           event. Provide timely diagnosis and treatment after serious adverse events, and report&#xD;
           to the ethics committee within 24 hours. Report regularly according to the requirements&#xD;
           of the ethics committee.&#xD;
&#xD;
      7. Ethical requirements&#xD;
&#xD;
        1. Follow the Declaration of Helsinki.&#xD;
&#xD;
        2. Recruitment and informed process: The main investigator and his team members will&#xD;
           recruit the subjects in the clinics or wards, inform the research content and related&#xD;
           matters in detail, and read and sign the informed consent form before enrollment.&#xD;
&#xD;
        3. All trials will be conducted after obtaining the approval of the Ethics Committee of&#xD;
           Qian Foshan Hospital in Shandong Province as well as follow-up review.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Not yet recruiting</last_known_status>
  <start_date type="Anticipated">December 30, 2021</start_date>
  <completion_date type="Anticipated">December 30, 2022</completion_date>
  <primary_completion_date type="Anticipated">August 30, 2022</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>The data of multimodal image recognition system</measure>
    <time_frame>one week</time_frame>
    <description>Cho (mmol/kg), Cr (mmol/kg), NAA (mmol/kg), Cho/Cr, Cho/NAA and NAA/Cr values Record the Cho (mmol/kg), Cr (mmol/kg), NAA (mmol/kg), Cho/Cr, Cho/NAA and NAA/Cr values in the lesion area, and calculate the ratio (relative reference value) to the metabolites of the healthy brain tissue area, namely r Cho, r Cr, r NAA, r Cho/Cr, r Cho/NAA and r NAA/Cr.&#xD;
18 F-FDG uptake values The uptake of 18 F-FDG was analyzed based on the anatomical morphology of the lesions provided by MRI images. Finally, according to the 18F-FDG uptake of the lesion, it is divided into 5 levels: level 1 is no intake, level 2 is slightly lower than the contralateral normal brain tissue, level 3 is similar to the contralateral normal brain tissue, and level 4 is the intake The degree is slightly higher than that of the contralateral normal brain tissue. Level 5 is the level of uptake significantly higher than that of the contralateral normal brain tissue.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Molecular subsets in in diffuse gliomas</measure>
    <time_frame>five months</time_frame>
    <description>(1) G-CIMP-low; (2) G-CIMP-high; (3) codel; (4) classic-like; (5) mesenchymal-like; (6) LGM6-GBM; (7) PA-like</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Overall survival</measure>
    <time_frame>two years</time_frame>
    <description>Overall survival of glioma patients</description>
  </secondary_outcome>
  <number_of_groups>7</number_of_groups>
  <enrollment type="Anticipated">350</enrollment>
  <condition>Glioma</condition>
  <arm_group>
    <arm_group_label>Glioma molecular subtype: G-CIMP-low</arm_group_label>
    <description>one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Glioma molecular subtype: G-CIMP-high</arm_group_label>
    <description>one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Glioma molecular subtype: codel</arm_group_label>
    <description>one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Glioma molecular subtype: classic-like</arm_group_label>
    <description>one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Glioma molecular subtype: mesenchymal-like</arm_group_label>
    <description>one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Glioma molecular subtype: LGM6-GBM</arm_group_label>
    <description>one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Glioma molecular subtype: PA-like</arm_group_label>
    <description>one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>PET/CT, H-MRS and MRI</intervention_name>
    <description>Use imaging methods to get metabolic parameters.</description>
    <arm_group_label>Glioma molecular subtype: G-CIMP-high</arm_group_label>
    <arm_group_label>Glioma molecular subtype: G-CIMP-low</arm_group_label>
    <arm_group_label>Glioma molecular subtype: LGM6-GBM</arm_group_label>
    <arm_group_label>Glioma molecular subtype: PA-like</arm_group_label>
    <arm_group_label>Glioma molecular subtype: classic-like</arm_group_label>
    <arm_group_label>Glioma molecular subtype: codel</arm_group_label>
    <arm_group_label>Glioma molecular subtype: mesenchymal-like</arm_group_label>
  </intervention>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      DNA is extracted from frozen glioma tissues&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        outpatients from Shandong Province Qianfoshan Hospital&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. glioma patients confirmed by postoperatively pathology ;&#xD;
&#xD;
          2. the lesion is non-diffuse, and the tumor body, edema and surrounding normal tissue are&#xD;
             clearly delimited;&#xD;
&#xD;
          3. capacity to give informed consent and follow study procedures.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. patients with previous treatment of glioma;&#xD;
&#xD;
          2. lack of clinical and image data or data inability to meet research needs;&#xD;
&#xD;
          3. severe cardiac dysfunction: acute decompensated heart failure and/or chronic heart&#xD;
             failure functional class III or IV (New York Heart Association classification);&#xD;
&#xD;
          4. patients who gave up halfway&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>70 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <reference>
    <citation>Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016 Mar-Apr;66(2):115-32. doi: 10.3322/caac.21338. Epub 2016 Jan 25.</citation>
    <PMID>26808342</PMID>
  </reference>
  <reference>
    <citation>Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA, Morozova O, Newton Y, Radenbaugh A, Pagnotta SM, Anjum S, Wang J, Manyam G, Zoppoli P, Ling S, Rao AA, Grifford M, Cherniack AD, Zhang H, Poisson L, Carlotti CG Jr, Tirapelli DP, Rao A, Mikkelsen T, Lau CC, Yung WK, Rabadan R, Huse J, Brat DJ, Lehman NL, Barnholtz-Sloan JS, Zheng S, Hess K, Rao G, Meyerson M, Beroukhim R, Cooper L, Akbani R, Wrensch M, Haussler D, Aldape KD, Laird PW, Gutmann DH; TCGA Research Network; Noushmehr H, Iavarone A, Verhaak RG. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell. 2016 Jan 28;164(3):550-63. doi: 10.1016/j.cell.2015.12.028.</citation>
    <PMID>26824661</PMID>
  </reference>
  <reference>
    <citation>Chaumeil MM, Lupo JM, Ronen SM. Magnetic Resonance (MR) Metabolic Imaging in Glioma. Brain Pathol. 2015 Nov;25(6):769-80. doi: 10.1111/bpa.12310.</citation>
    <PMID>26526945</PMID>
  </reference>
  <reference>
    <citation>la Fougere C, Suchorska B, Bartenstein P, Kreth FW, Tonn JC. Molecular imaging of gliomas with PET: opportunities and limitations. Neuro Oncol. 2011 Aug;13(8):806-19. doi: 10.1093/neuonc/nor054. Epub 2011 Jul 13.</citation>
    <PMID>21757446</PMID>
  </reference>
  <verification_date>September 2021</verification_date>
  <study_first_submitted>May 25, 2020</study_first_submitted>
  <study_first_submitted_qc>May 25, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">May 29, 2020</study_first_posted>
  <last_update_submitted>September 7, 2021</last_update_submitted>
  <last_update_submitted_qc>September 7, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">September 9, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor-Investigator</responsible_party_type>
    <investigator_affiliation>Qianfoshan Hospital</investigator_affiliation>
    <investigator_full_name>Tao Xin</investigator_full_name>
    <investigator_title>Director of Neurosurgery</investigator_title>
  </responsible_party>
  <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>


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/NCT04407039
org study id: YXLL-KY-2020 (009)
nct id: NCT04407039
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: Yes
is us export: No
Research purposes:

        1. To obtain the metabolic characteristics of glioma molecular imaging through a multimodal
           image recognition system.

        2. To determine whether molecular imaging metabolic parameters can characterize the
           molecular typing of glioma by analyzing the relationship between metabolic parameters
           and tumor subtypes

        3. To get metabolic classification based on metabolic parameters of glioma molecular
           imaging, and to identify the relationship between metabolic subtypes and surgical
           resection, radiotherapy and chemotherapy, and prognosis and further refine the molecular
           classification of glioma.

      Research Background:

      Glioma is the most common primary intracranial malignant tumor, accounting for 80% of central
      nervous system malignant tumors. It is highly invasive, with a surgical recurrence rate of up
      to 90%. The prognosis is extremely poor, which has caused a great burden. There are different
      molecular subtypes of glioma with distinct molecular biological characteristics, resulting in
      various prognosis of patients. With the continuous development of basic and clinical research
      of glioma and the advent of various new drugs and treatment technologies, molecular
      pathological diagnosis based on the individual level of glioma patients is particularly
      important. Clarifying the molecular pathology type before surgery will help the clinical
      diagnosis and prognostic judgment of glioma, and is of great significance for the
      optimization of treatment options.

      Based on the establishment of glioma molecular typing system, the project team use
      noninvasive molecular imaging technology to clarify the characteristics of molecular subsets
      of glioma based on the tumor metabolic parameters. Through combining deep learning-based
      target detection and image recognition with big data analysis, it has great potential in the
      clinical research of glioma diagnosis, prognosis and treatment options, which could provide a
      scientific basis for the establishment and promotion of glioma molecular analysis and
      recognition system.
1. Multimodal Image Recognition System of Glioma Molecular imaging is the use of imaging
           methods to display specific molecules at the tissue level, cellular level, and
           subcellular level, reflecting changes in the molecular level in the living state, and
           performing qualitative and quantitative researches on the biological behavior of the
           imaging aspect. Multimodal Image Recognition System includes Positron Emission Computed
           Tomography (PET), Magnetic Resonance Imaging (MRI) and Magnetic Resonance Hydrogen
           Proton Spectroscopy (1H-MRS). 1H-MRS quantifies the choline (Cho) and creatine (Cr) in
           the spectrum, N-acetylaspartate acid (NAA), qualification (Lip) and lactic acid (Lac)
           and other substances to reflect changes in cell metabolites, and then speculate on the
           occurrence and development of tumors. Imaging of the glucose analog 18F-labeled
           fluorodeoxyglucose (18FDG) by positron emission tomography (PET) is the gold standard
           for the assessment of cerebral glucose metabolism in vivo. However, single imaging
           method often has limitations, and it is difficult to meet the requirements for
           sensitivity, specificity, and target at the same time. Combing PET / CT with 1H- MRS of
           new generation imaging technologies can overcome the shortcomings of a single imaging
           method and achieve complementary advantages and provide effective informations

        2. Risks and Benefits of Participants (1) Risks: In addition to collecting information
           above, the main risk is discomfort that may occur during the conversation. Since this
           study will not intervene in routine diagnosis and treatment, there are no other special
           risks other than the possible discomfort and adverse reactions mentioned above expected
           during treatment. (2) Benefits: No direct benefits, but may provide useful informations
           for disease research.

        3. Statistical Methods All data (clinical data, imaging data) will be established in a
           special database. In order to control the imbalance of many different confounding
           factors in the formed population, the test will use the propensity score method to
           achieve the purpose of controlling confounding and improve the quality of real-world
           evidence, close to the effect of randomization.

        4. Criteria for aborting trials and ending clinical trials

      1. Criteria for aborting trials The investigator judges that continuing the trial is not good
      for the patient; During the course of the study, the patient is unwilling to continue the
      clinical study, and makes a withdrawal request to the competent doctor and requests to cancel
      the informed consent.

      2. Criteria for ending clinical research: If serious safety problems occur during the study,
      the study should be terminated in time; The ethics committee requested to terminate the trial
      after the study; The health administrative department ordered the termination of clinical
      research for some reason; Significant deviations have occurred in the design or
      implementation of the study protocol and it is difficult to evaluate the results.

      5. Participant's Code, Storage Procedure of Case Report, Data Management and Guarantee of
      Test Reliability

        1. After the participants enter the cohort after signing the informed consent, they will be
           sorted and assigned a serial number by the research secretary according to the time when
           the informed consent is signed. Therefore, the first patient enrolled is coded 001.

        2. The patient's case report form will be made into a special patient manual, which will be
           kept by the research secretary and clinical staff for data collection and follow-up. Any
           observations and inspection results during the test should be entered into the form and
           database in a timely, accurate, complete, clear, standardized, and true manner. After
           the subject's follow-up is over, hand over and perform data collection and analysis. The
           subject's imaging data will be regularly uploaded by the research secretary to the
           intelligent IT virtual storage system based on the cluster. The system divides
           neurosurgery and subjects into specific storage partitions.

        3. Each researcher has the authority to consult and modify the subject's storage partition.
           The storage system automatically records the changes of the stored data, thus ensuring
           the traceability of the data. The investigator should agree to keep all research data
           (including original records, informed consent of all patients, all case report forms,
           etc.), and the special counter shall be locked and stored until 5 years after the end of
           the trial.

      6. Safety

        1. Serious adverse event: death; medical personnel believe what is life-threatening; events
           requiring hospitalization or extension of hospital stay; persistent or severe disability
           or disability.

        2. Reporting methods and measures after the occurrence of the adverse events above, the
           medical staff should first report to the main investigator and fill out the adverse
           event report form. The adverse reaction report includes the diagnosis and treatment of
           the patient, the time of the event, the cause of the event and the treatment after the
           event. Provide timely diagnosis and treatment after serious adverse events, and report
           to the ethics committee within 24 hours. Report regularly according to the requirements
           of the ethics committee.

      7. Ethical requirements

        1. Follow the Declaration of Helsinki.

        2. Recruitment and informed process: The main investigator and his team members will
           recruit the subjects in the clinics or wards, inform the research content and related
           matters in detail, and read and sign the informed consent form before enrollment.

        3. All trials will be conducted after obtaining the approval of the Ethics Committee of
           Qian Foshan Hospital in Shandong Province as well as follow-up review.
observational model: Cohort
time perspective: Prospective
measure: The data of multimodal image recognition system
time frame: one week
description: Cho (mmol/kg), Cr (mmol/kg), NAA (mmol/kg), Cho/Cr, Cho/NAA and NAA/Cr values Record the Cho (mmol/kg), Cr (mmol/kg), NAA (mmol/kg), Cho/Cr, Cho/NAA and NAA/Cr values in the lesion area, and calculate the ratio (relative reference value) to the metabolites of the healthy brain tissue area, namely r Cho, r Cr, r NAA, r Cho/Cr, r Cho/NAA and r NAA/Cr.
18 F-FDG uptake values The uptake of 18 F-FDG was analyzed based on the anatomical morphology of the lesions provided by MRI images. Finally, according to the 18F-FDG uptake of the lesion, it is divided into 5 levels: level 1 is no intake, level 2 is slightly lower than the contralateral normal brain tissue, level 3 is similar to the contralateral normal brain tissue, and level 4 is the intake The degree is slightly higher than that of the contralateral normal brain tissue. Level 5 is the level of uptake significantly higher than that of the contralateral normal brain tissue.
measure: Molecular subsets in in diffuse gliomas
time frame: five months
description: (1) G-CIMP-low; (2) G-CIMP-high; (3) codel; (4) classic-like; (5) mesenchymal-like; (6) LGM6-GBM; (7) PA-like
measure: Overall survival
time frame: two years
description: Overall survival of glioma patients
arm group label: Glioma molecular subtype: G-CIMP-low
description: one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma
arm group label: Glioma molecular subtype: G-CIMP-high
description: one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma
arm group label: Glioma molecular subtype: codel
description: one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma
arm group label: Glioma molecular subtype: classic-like
description: one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma
arm group label: Glioma molecular subtype: mesenchymal-like
description: one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma
arm group label: Glioma molecular subtype: LGM6-GBM
description: one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma
arm group label: Glioma molecular subtype: PA-like
description: one of molecular subtypes according to gene expression, DNA copy number, DNA methylation, exome sequencing and protein expression of glioma
intervention type: Device
intervention name: PET/CT, H-MRS and MRI
description: Use imaging methods to get metabolic parameters.
arm group label: Glioma molecular subtype: G-CIMP-high
arm group label: Glioma molecular subtype: G-CIMP-low
arm group label: Glioma molecular subtype: LGM6-GBM
arm group label: Glioma molecular subtype: PA-like
arm group label: Glioma molecular subtype: classic-like
arm group label: Glioma molecular subtype: codel
arm group label: Glioma molecular subtype: mesenchymal-like
DNA is extracted from frozen glioma tissues
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 70 Years
healthy volunteers: No
citation: Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016 Mar-Apr;66(2):115-32. doi: 10.3322/caac.21338. Epub 2016 Jan 25.
PMID: 26808342
citation: Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA, Morozova O, Newton Y, Radenbaugh A, Pagnotta SM, Anjum S, Wang J, Manyam G, Zoppoli P, Ling S, Rao AA, Grifford M, Cherniack AD, Zhang H, Poisson L, Carlotti CG Jr, Tirapelli DP, Rao A, Mikkelsen T, Lau CC, Yung WK, Rabadan R, Huse J, Brat DJ, Lehman NL, Barnholtz-Sloan JS, Zheng S, Hess K, Rao G, Meyerson M, Beroukhim R, Cooper L, Akbani R, Wrensch M, Haussler D, Aldape KD, Laird PW, Gutmann DH; TCGA Research Network; Noushmehr H, Iavarone A, Verhaak RG. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell. 2016 Jan 28;164(3):550-63. doi: 10.1016/j.cell.2015.12.028.
PMID: 26824661
citation: Chaumeil MM, Lupo JM, Ronen SM. Magnetic Resonance (MR) Metabolic Imaging in Glioma. Brain Pathol. 2015 Nov;25(6):769-80. doi: 10.1111/bpa.12310.
PMID: 26526945
citation: la Fougere C, Suchorska B, Bartenstein P, Kreth FW, Tonn JC. Molecular imaging of gliomas with PET: opportunities and limitations. Neuro Oncol. 2011 Aug;13(8):806-19. doi: 10.1093/neuonc/nor054. Epub 2011 Jul 13.
PMID: 21757446
responsible party type: Sponsor-Investigator
investigator affiliation: Qianfoshan Hospital
investigator full name: Tao Xin
investigator title: Director of Neurosurgery
mesh term: Glioma
sharing ipd: Undecided