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/NCT06036381</url>
    </required_header>
    <id_info>
      <org_study_id>4146</org_study_id>
      <nct_id>NCT06036381</nct_id>
    </id_info>
    <brief_title>Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis</brief_title>
    <acronym>GLIO-RAD</acronym>
    <official_title>Spatial and Temporal Characterization of Gliomas Using Radiomic Analysis</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Tata Memorial Centre</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Indian Statistical Institute, Kolkata</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Tata Memorial Centre</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>
    </oversight_info>
    <brief_summary>
      <textblock>
        Glioma are type of primary brain tumors arising within the substance of brain. Different type&#xD;
        of gliomas are seen which are classified depending upon pathological examination and advanced&#xD;
        molecular techniques, which help to determine the aggressiveness of the tumor and outcomes.&#xD;
        Artificial intelligence uses advanced analytical process aided by computer which can be&#xD;
        undertaken on the medical images. We plan to use artificial intelligence techniques to&#xD;
        identify the abnormal areas within the brain representing tumor from the radiological images.&#xD;
        Also, similar approach will be undertaken to classify gliomas with good or bad prognosis, to&#xD;
        differentiate glioma from other type of brain tumors, and to detect response after treatment.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        In the proposed retrospective study, images (MRI, CT, or PET) undertaken as part of standard&#xD;
        of care (pre-treatment, post-operative, response assessment, and surveillance) will be&#xD;
        analyzed. The DMG database maintaining records of patients registered in TMC neuro-oncology&#xD;
        DMG will be screened to identify the patients eligible for the study. With approximately&#xD;
        500-600 gliomas seen annually and approximately 80-100 patients/year having pre-treatment&#xD;
        imaging, we expect a ceiling of 1000 patients during 2010-2022, which will be the maximum&#xD;
        number of patients used for the analysis. All the images will be downloaded from the PACS&#xD;
        applying anonymization filters, with clinical records extracted by review of electronic&#xD;
        medical records and radiation plans. Imaging pre-processing will be done, which will include&#xD;
        skull stripping and registration across different modalities (e.g., MRI and CT) or different&#xD;
        sequences (e.g., T1C, T2W, ADC) will be done using rigid or deformable algorithms as suited&#xD;
        best for the modality. Image segmentation to classify the region of interest will be done and&#xD;
        verified individually by a neuro-radiologist or nuclear medicine physician as appropriate.&#xD;
        The segmentations will be done to identify T1-contrast enhancing region (CE), non-enhancing&#xD;
        regions (NE), and necrosis (NEC) guided by T1-C, T2W, and T2-FLAIR areas. The contours and&#xD;
        the images will be resampled to a uniform resolution for different sequences or modalities&#xD;
        (e.g., T2W/ ADC/ PET) to match either with the 3D sequence (e.g., FSPGR sequence) or&#xD;
        available images with the least slice thickness. Subsequently, normalization techniques&#xD;
        (e.g., histogram normalization/ Z-score normalization) will be undertaken within the&#xD;
        individual images and across the entire dataset to account or image heterogeneity, including&#xD;
        field strength for MRI and different image acquisition parameters. For auto segmentation,&#xD;
        both supervised and unsupervised machine learning algorithms will be applied. For the&#xD;
        supervised model, the entire database will be split into training and test cohorts for the&#xD;
        model and application development, respectively. The effectiveness of the automated model&#xD;
        will be tested using the dice similarity coefficient between manually segmentation regions&#xD;
        and AI-based segments. For prognostication of gliomas, the next step will include feature&#xD;
        extraction, which will consist of first-order (including shape, histogram), second-order or&#xD;
        higher-order (e.g., different texture features like GLCM, GLDM, GLSZM, etc.), or deep&#xD;
        learning techniques will be employed. Delta-radiomics will include temporal changes in the&#xD;
        radiomic features from different time points for the same patient within the entire volume&#xD;
        and individual regions. Subsequently, feature reduction and selection techniques like LASSO,&#xD;
        recursive feature elimination will be used to shortlist the number of features depending on&#xD;
        the sample size. The outputs will be decided based on the modeling defined for specific class&#xD;
        problems (e.g., tumor vs. edema, recurrence vs. pseudo progression, outcomes, tumor region of&#xD;
        interest vs. non-tumoral area) as obtained from the clinical information. Any class imbalance&#xD;
        will be addressed using methods like random subset sampling or SMOTE analysis for data&#xD;
        augmentation of the minority class. Machine learning algorithms like LDA, k-NN, SVM, random&#xD;
        forest, AdaBoost, etc., will be applied singularly or in combination as an ensembled&#xD;
        classifier to find the model with best performance. Deep learning classifiers will be used&#xD;
        along with feature-based modeling and compared to test the classifier's applicability.&#xD;
        Validation techniques like leave-one-out validation, k-fold validation, and split (into&#xD;
        training and test cohort) will be used to assess the stability of the machine learning model.&#xD;
        Radiomic analysis will be done by data scientist/ study investigators with expertise in data&#xD;
        analytics. All segmentations will be done on open-source software like ITK snap (itksnap.org)&#xD;
        or 3D Slicer (slicer.org). Feature extraction and modeling will be done using open-source&#xD;
        software like Python (python.org). With continuous advancements in computational science,&#xD;
        available newer analytical techniques and platforms will be applied as appropriate by&#xD;
        collaborators from Indian Statistical Institute, Kolkata, Machine Intelligence Unit by&#xD;
        sharing of the anonymized data.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 2023</start_date>
    <completion_date type="Anticipated">September 2026</completion_date>
    <primary_completion_date type="Anticipated">September 2026</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Only</observational_model>
      <time_perspective>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Autosegmentation of tumor</measure>
      <time_frame>3 years</time_frame>
      <description>The correlation of tumor region between manual segmentation and artificial intelligence-based autosegmentation model will be assessed using the Dice coefficient of similarity.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Prognostication of gliomas</measure>
      <time_frame>3 years</time_frame>
      <description>Radiomic signature in prognostication of gliomas with estimation of progression-free survival and overall survival using Kaplan Meier plots and radiomics score-based nomograms.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Response assessment in gliomas</measure>
      <time_frame>3 years</time_frame>
      <description>Response assessment of gliomas using artificial intelligence model-based prediction and comparison with actual response (like radionecrosis, progression) using confusion matrices and estimation of parameters like sensitivity, specificity, accuracy, area under curve.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Differentiation of glioma from non-glioma histology</measure>
      <time_frame>3 years</time_frame>
      <description>Use of radiomics model to differentiate gliomas from other brain tumors, with performance indices calculated using sensitivity, specificity, accuracy, area under curve.</description>
    </secondary_outcome>
    <enrollment type="Anticipated">1000</enrollment>
    <condition>Glioma</condition>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>Radiomic analysis of imaging - MRI, PET, CT</intervention_name>
      <description>Radiomic analysis of imaging will be undertaken as a standard of care to develop computational algorithms for patients treated in our institution.</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Study population will be according to the Inclusion and Exclusion Criteria . Study Includes&#xD;
          vulnerable participants also. Minors (up to 18 years),Elderly&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients with glioma or glioma-mimicking pathology with imaging available in TMC&#xD;
               between January 2010 and December 2022.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Imaging done outside TMC.&#xD;
  &#xD;
            -  Motion artifacts or other artifacts causing image degradation.&#xD;
  &#xD;
            -  Size of tumor or region of interest &lt; 1 cm in the largest dimension&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>1 Year</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Dr. ARCHYA DASGUPTA, MD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Tata Memorial Hospital</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Dr. ARCHYA DASGUPTA, MD</last_name>
      <phone>022-24177000</phone>
      <phone_ext>6861</phone_ext>
      <email>archya1010@gmail.com</email>
    </overall_contact>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 28, 2023</study_first_submitted>
    <study_first_submitted_qc>September 6, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 13, 2023</study_first_posted>
    <last_update_submitted>September 6, 2023</last_update_submitted>
    <last_update_submitted_qc>September 6, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 13, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Tata Memorial Centre</investigator_affiliation>
      <investigator_full_name>Dr Archya Dasgupta</investigator_full_name>
      <investigator_title>Assistant Professor, Radiation Oncology</investigator_title>
    </responsible_party>
    <keyword>Glioma</keyword>
    <keyword>Radiomics</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Machine learning</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>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/NCT06036381
  org study id: 4146
  nct id: NCT06036381
  lead sponsor: 
  collaborator: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  Glioma are type of primary brain tumors arising within the substance of brain. Different type
        of gliomas are seen which are classified depending upon pathological examination and advanced
        molecular techniques, which help to determine the aggressiveness of the tumor and outcomes.
        Artificial intelligence uses advanced analytical process aided by computer which can be
        undertaken on the medical images. We plan to use artificial intelligence techniques to
        identify the abnormal areas within the brain representing tumor from the radiological images.
        Also, similar approach will be undertaken to classify gliomas with good or bad prognosis, to
        differentiate glioma from other type of brain tumors, and to detect response after treatment.
  In the proposed retrospective study, images (MRI, CT, or PET) undertaken as part of standard
        of care (pre-treatment, post-operative, response assessment, and surveillance) will be
        analyzed. The DMG database maintaining records of patients registered in TMC neuro-oncology
        DMG will be screened to identify the patients eligible for the study. With approximately
        500-600 gliomas seen annually and approximately 80-100 patients/year having pre-treatment
        imaging, we expect a ceiling of 1000 patients during 2010-2022, which will be the maximum
        number of patients used for the analysis. All the images will be downloaded from the PACS
        applying anonymization filters, with clinical records extracted by review of electronic
        medical records and radiation plans. Imaging pre-processing will be done, which will include
        skull stripping and registration across different modalities (e.g., MRI and CT) or different
        sequences (e.g., T1C, T2W, ADC) will be done using rigid or deformable algorithms as suited
        best for the modality. Image segmentation to classify the region of interest will be done and
        verified individually by a neuro-radiologist or nuclear medicine physician as appropriate.
        The segmentations will be done to identify T1-contrast enhancing region (CE), non-enhancing
        regions (NE), and necrosis (NEC) guided by T1-C, T2W, and T2-FLAIR areas. The contours and
        the images will be resampled to a uniform resolution for different sequences or modalities
        (e.g., T2W/ ADC/ PET) to match either with the 3D sequence (e.g., FSPGR sequence) or
        available images with the least slice thickness. Subsequently, normalization techniques
        (e.g., histogram normalization/ Z-score normalization) will be undertaken within the
        individual images and across the entire dataset to account or image heterogeneity, including
        field strength for MRI and different image acquisition parameters. For auto segmentation,
        both supervised and unsupervised machine learning algorithms will be applied. For the
        supervised model, the entire database will be split into training and test cohorts for the
        model and application development, respectively. The effectiveness of the automated model
        will be tested using the dice similarity coefficient between manually segmentation regions
        and AI-based segments. For prognostication of gliomas, the next step will include feature
        extraction, which will consist of first-order (including shape, histogram), second-order or
        higher-order (e.g., different texture features like GLCM, GLDM, GLSZM, etc.), or deep
        learning techniques will be employed. Delta-radiomics will include temporal changes in the
        radiomic features from different time points for the same patient within the entire volume
        and individual regions. Subsequently, feature reduction and selection techniques like LASSO,
        recursive feature elimination will be used to shortlist the number of features depending on
        the sample size. The outputs will be decided based on the modeling defined for specific class
        problems (e.g., tumor vs. edema, recurrence vs. pseudo progression, outcomes, tumor region of
        interest vs. non-tumoral area) as obtained from the clinical information. Any class imbalance
        will be addressed using methods like random subset sampling or SMOTE analysis for data
        augmentation of the minority class. Machine learning algorithms like LDA, k-NN, SVM, random
        forest, AdaBoost, etc., will be applied singularly or in combination as an ensembled
        classifier to find the model with best performance. Deep learning classifiers will be used
        along with feature-based modeling and compared to test the classifier's applicability.
        Validation techniques like leave-one-out validation, k-fold validation, and split (into
        training and test cohort) will be used to assess the stability of the machine learning model.
        Radiomic analysis will be done by data scientist/ study investigators with expertise in data
        analytics. All segmentations will be done on open-source software like ITK snap (itksnap.org)
        or 3D Slicer (slicer.org). Feature extraction and modeling will be done using open-source
        software like Python (python.org). With continuous advancements in computational science,
        available newer analytical techniques and platforms will be applied as appropriate by
        collaborators from Indian Statistical Institute, Kolkata, Machine Intelligence Unit by
        sharing of the anonymized data.
  observational model: Case-Only
  time perspective: Retrospective
  measure: Autosegmentation of tumor
  time frame: 3 years
  description: The correlation of tumor region between manual segmentation and artificial intelligence-based autosegmentation model will be assessed using the Dice coefficient of similarity.
  measure: Prognostication of gliomas
  time frame: 3 years
  description: Radiomic signature in prognostication of gliomas with estimation of progression-free survival and overall survival using Kaplan Meier plots and radiomics score-based nomograms.
  measure: Response assessment in gliomas
  time frame: 3 years
  description: Response assessment of gliomas using artificial intelligence model-based prediction and comparison with actual response (like radionecrosis, progression) using confusion matrices and estimation of parameters like sensitivity, specificity, accuracy, area under curve.
  measure: Differentiation of glioma from non-glioma histology
  time frame: 3 years
  description: Use of radiomics model to differentiate gliomas from other brain tumors, with performance indices calculated using sensitivity, specificity, accuracy, area under curve.
  intervention type: Diagnostic Test
  intervention name: Radiomic analysis of imaging - MRI, PET, CT
  description: Radiomic analysis of imaging will be undertaken as a standard of care to develop computational algorithms for patients treated in our institution.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 1 Year
  maximum age: N/A
  healthy volunteers: No
  last name: Dr. ARCHYA DASGUPTA, MD
  role: Principal Investigator
  affiliation: Tata Memorial Hospital
  last name: Dr. ARCHYA DASGUPTA, MD
  phone: 022-24177000
  phone ext: 6861
  email: archya1010@gmail.com
  responsible party type: Principal Investigator
  investigator affiliation: Tata Memorial Centre
  investigator full name: Dr Archya Dasgupta
  investigator title: Assistant Professor, Radiation Oncology
  mesh term: Glioma
  sharing ipd: No