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/NCT06036394</url>
    </required_header>
    <id_info>
      <org_study_id>4147</org_study_id>
      <nct_id>NCT06036394</nct_id>
    </id_info>
    <brief_title>Artificial Intelligence in CNS Radiation Oncology</brief_title>
    <acronym>AI-RAD</acronym>
    <official_title>Artificial Intelligence in Radiation Oncology for CNS Tumors</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Tata Memorial Centre</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Bhabha Atomic Research Centre (BARC)</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>
        Radiotherapy involves the use of high-energy X-rays, which can be used to stop the growth of&#xD;
        tumor cells. Radiotherapy constitutes an essential avenue in the treatment of brain tumors.&#xD;
        The modern techniques of radiotherapy involve radiation planning techniques guided by&#xD;
        computer algorithms aimed to deliver high doses of radiation to the areas of brain with&#xD;
        tumors and limit the doses to surrounding normal structures. Artificial intelligence uses&#xD;
        advanced analytical processes aided by computational analysis, which can be undertaken on the&#xD;
        medical images, and radiation planning process. We plan to use artificial intelligence&#xD;
        techniques to automatically delineate areas of the brain with tumor and other normal&#xD;
        structures as identified from images. Also, we will use artificial intelligence on the&#xD;
        radiation dose images and other images done for radiation treatment to classify tumors with&#xD;
        good or bad prognoses, identify patients developing radiation complications, and detect&#xD;
        responses after treatment.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        In the proposed retrospective study, patients treated with Radiotherapy (RT) for Central&#xD;
        Nervous System(CNS) tumors will be included. The DMG database maintaining records of patients&#xD;
        registered and treated in TMC (Tata Memorial Centre) neuro oncology DMG (Disease Management&#xD;
        Group) will be screened to identify the patients eligible for the study. With approximately&#xD;
        500-600 patients with CNS tumors treated with RT in TMC every year, we expect a ceiling of&#xD;
        6000 patients during 2010-2022, which will be the maximum number of patients used for the&#xD;
        analysis. The images (CT, MRI, PET) used for RT planning, mid-treatment imaging as part of&#xD;
        IGRT (Image-guided radiation therapy) or disease evaluation, and response assessment/&#xD;
        surveillance post-RT will be analyzed. The radiation plans and dose-volume histogram will be&#xD;
        obtained from TPS (Treatment Planning System). All the images and radiation-related data will&#xD;
        be downloaded from the PACS (Picture Archiving and Communication System) and TPS, applying&#xD;
        anonymization filters. Clinical features (patient, disease, treatment-related&#xD;
        characteristics, and outcomes) will be extracted by review of electronic medical records.&#xD;
        Imaging pre-processing will be done, which will include skull stripping and registration&#xD;
        across different modalities (e.g., MRI and CT) or different sequences (e.g., T1C, T2W, ADC)&#xD;
        will be done using rigid or deformable algorithms as suited best for the modality. The target&#xD;
        volumes, i.e., gross tumor volume (GTV), clinical target volume (CTV), and planning target&#xD;
        volume (PTV), and OARs will be individually reviewed by radiation oncologists with&#xD;
        modifications applied as appropriate (e.g., exclusion of OARs (Organs at risk) distorted by&#xD;
        disease or surgery) and will be used to train the machine learning models for supervised&#xD;
        learning. The contours and the images will be resampled to a uniform resolution for different&#xD;
        sequences or modalities (e.g., T2W/ ADC/ PET) to match either with the 3D sequence (e.g.,&#xD;
        FSPGR sequence) or available images with the least slice thickness. Subsequently,&#xD;
        normalization techniques (e.g., histogram normalization/ Z-score normalization) will be&#xD;
        undertaken within the individual images and across the entire dataset to account for image&#xD;
        heterogeneity, including field strength for MRI and different image acquisition parameters.&#xD;
        For autosegmentation, both supervised and unsupervised machine learning algorithms will be&#xD;
        applied. For the supervised model, the entire database will be split into training and test&#xD;
        cohorts for the model and application development, respectively. Since the OARs are uniformly&#xD;
        applicable for different histology or tumor sites, autosegmentation training will be applied&#xD;
        to the entire dataset. However, given there are variations in target volume delineations&#xD;
        (e.g., for circumscribed vs. diffuse tumors, low grade vs. high grade), the training/ testing&#xD;
        for TVs will be applicable for individual disease entities. The effectiveness of the&#xD;
        automated model will be tested using the dice similarity coefficient between manually&#xD;
        segmentation regions and AI-based segments. For outcome prediction (e.g., survival and&#xD;
        toxicities), the next step will include feature extraction from images (CT, MRI, PET)&#xD;
        corresponding to different TV and OARs and RT dose distribution data converted to volumetric&#xD;
        image/ number data (dosiomics), which will consist of first-order (including shape,&#xD;
        histogram), second-order or higher-order (e.g., different texture features like GLCM, GLDM,&#xD;
        GLSZM, etc.), or deep learning techniques will be employed. Delta-radiomics will include&#xD;
        temporal changes in the radiomic features from different time points for the same patient&#xD;
        within the entire volume and individual regions. Subsequently, feature reduction and&#xD;
        selection techniques like LASSO, recursive feature elimination will be used to shortlist the&#xD;
        number of features depending on the sample size. The outputs will be decided based on the&#xD;
        modeling defined for specific class problems (e.g., tumor vs. edema, recurrence vs.&#xD;
        pseudoprogression, outcomes, tumor region of interest vs. non-tumoral area) as obtained from&#xD;
        the clinical information. Any class imbalance will be addressed using methods like random&#xD;
        subset sampling or SMOTE analysis for data augmentation of the minority class. Machine&#xD;
        learning algorithms like LDA, k-NN, SVM, random forest, AdaBoost, etc., will be applied&#xD;
        singularly or in combination as an ensembled classifier to find the model with the best&#xD;
        performance. Deep learning classifiers will be used along with feature-based modeling and&#xD;
        compared to test the classifier's applicability. Validation techniques like leave-one-out&#xD;
        validation, k-fold validation, and split (into training and test cohort) will be used to&#xD;
        assess the stability of the machine learning model. Radiomic analysis will be done by data&#xD;
        scientist/ study investigators with expertise in data analytics. All segmentations will be&#xD;
        done on open source software like ITK snap (itksnap.org) or 3D Slicer (slicer.org). Feature&#xD;
        extraction and modeling will be done using opensource software like Python (python.org). As a&#xD;
        tertiary objective in the project, we will develop a protocol for anonymized data storage&#xD;
        (clinical information, radiation planning and response assessment images, radiation planning&#xD;
        data, intra-treatment images like cone beam computed tomography) in a secured image biobank&#xD;
        repository with protected cloud space. Also, natural language processing (NLP) algorithms&#xD;
        will be developed to train and validate model for extraction, and documentation of clinical&#xD;
        variables extracted for the study. With continuous advancements in computational science,&#xD;
        available newer analytical techniques and platforms will be applied as appropriate by&#xD;
        collaborators from Bhabha Atomic Research Centre, Mumbai, by sharing 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 2028</completion_date>
    <primary_completion_date type="Anticipated">September 2028</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 organs at risk and target volumes.</measure>
      <time_frame>5 years</time_frame>
      <description>The agreement between manual segmentation and automated segmentation using an artificial intelligence-based model will be assessed using Dice coefficient of similarity.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Survival Analysis and Toxicity Estimation</measure>
      <time_frame>5 years</time_frame>
      <description>Quantitative image analysis from target volumes and organs at risk and correlation with survival and normal tissue complications. Survival analysis will be done using Kaplan Meier method and nomograms will be constructed from quantitative imaging features. Toxicity assessment will include the incidence of radionecrosis and the correlation of quantitative imaging markers will be done using regression models or machine learning algorithms.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Dosiomic analysis</measure>
      <time_frame>5 years</time_frame>
      <description>Quantitative analysis of radiation dosimetric maps and correlation with survival and toxicity. The performance metrics of the radiomics model in the prediction of recurrence/ death or toxicity (necrosis) will be compared with ground truth using sensitivity, specificity, accuracy, area under curve.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Response assessment following treatment</measure>
      <time_frame>5 years</time_frame>
      <description>Development of an automated response assessment algorithm using the images (MRI/ CT/ PET scan) following radiation. The predicted response using machine learning algorithms will be compared with actual outcomes (response/ stable disease/ progressive disease) and performance measured in the form of sensitivity, specificity, accuracy, area under curve.</description>
    </secondary_outcome>
    <other_outcome>
      <measure>Automated radiation planning and evaluation</measure>
      <time_frame>5 years</time_frame>
      <description>Application of artificial intelligence algorithms in the treatment planning system for automated radiation plan generation and evaluation. The plans generated using artificial intelligence algorithms will be manually evaluated by radiation oncologists/ physicists and reported with proportions/ratios.</description>
    </other_outcome>
    <other_outcome>
      <measure>Data Banking</measure>
      <time_frame>5 years</time_frame>
      <description>Development of a data bank to store clinical details, images, and radiation plans. The annotated clinical, imaging, and radiation data will be secured in a database (no formal assessment is planned for the efficacy of data storage). Standardization of clinical variables with automated data extraction from the medical records and PACS will be established.</description>
    </other_outcome>
    <other_outcome>
      <measure>Natural language processing for data interpretation</measure>
      <time_frame>5 years</time_frame>
      <description>Development of NLP algorithms for automated recording of data and interpretation. The performance of NLP algorithms in interpretation will be assessed manually (with actual data/ records) and reported with confusion matrices e.g., sensitivity, specificity</description>
    </other_outcome>
    <enrollment type="Anticipated">6000</enrollment>
    <condition>CNS Tumor</condition>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>The images (CT, MRI, PET) used for RT planning, mid-treatment imaging as part of IGRT or disease evaluation, and response assessment/ surveillance post-RT</intervention_name>
      <description>The radiation plans and dose-volume histogram will be obtained from TPS. All the images and radiation-related data will be downloaded from the PACS and TPS, applying anonymization filters. Clinical features (patient, disease, treatment-related characteristics, and outcomes) will be extracted by review of electronic medical records. 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</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          With approximately 500-600 patients with CNS tumors treated with RT in TMC every year, we&#xD;
          expect a ceiling of 6000 patients during 2010-2022, which will be the maximum number of&#xD;
          patients used for the analysis.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
          • Patients with CNS tumors treated with radiation in TMC between January 2010 and December&#xD;
          2022.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  RT treatment outside TMC.&#xD;
  &#xD;
            -  Radiation planning not done in the treatment planning system (treated using clinical&#xD;
               marking/ conventional simulator).&#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>91-22-24177000</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>Brain tumor</keyword>
    <keyword>Glioma</keyword>
    <keyword>Radiation Oncology</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Machine learning</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Central Nervous System Neoplasms</mesh_term>
    </condition_browse>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT06036394
  org study id: 4147
  nct id: NCT06036394
  lead sponsor: 
  collaborator: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  Radiotherapy involves the use of high-energy X-rays, which can be used to stop the growth of
        tumor cells. Radiotherapy constitutes an essential avenue in the treatment of brain tumors.
        The modern techniques of radiotherapy involve radiation planning techniques guided by
        computer algorithms aimed to deliver high doses of radiation to the areas of brain with
        tumors and limit the doses to surrounding normal structures. Artificial intelligence uses
        advanced analytical processes aided by computational analysis, which can be undertaken on the
        medical images, and radiation planning process. We plan to use artificial intelligence
        techniques to automatically delineate areas of the brain with tumor and other normal
        structures as identified from images. Also, we will use artificial intelligence on the
        radiation dose images and other images done for radiation treatment to classify tumors with
        good or bad prognoses, identify patients developing radiation complications, and detect
        responses after treatment.
  In the proposed retrospective study, patients treated with Radiotherapy (RT) for Central
        Nervous System(CNS) tumors will be included. The DMG database maintaining records of patients
        registered and treated in TMC (Tata Memorial Centre) neuro oncology DMG (Disease Management
        Group) will be screened to identify the patients eligible for the study. With approximately
        500-600 patients with CNS tumors treated with RT in TMC every year, we expect a ceiling of
        6000 patients during 2010-2022, which will be the maximum number of patients used for the
        analysis. The images (CT, MRI, PET) used for RT planning, mid-treatment imaging as part of
        IGRT (Image-guided radiation therapy) or disease evaluation, and response assessment/
        surveillance post-RT will be analyzed. The radiation plans and dose-volume histogram will be
        obtained from TPS (Treatment Planning System). All the images and radiation-related data will
        be downloaded from the PACS (Picture Archiving and Communication System) and TPS, applying
        anonymization filters. Clinical features (patient, disease, treatment-related
        characteristics, and outcomes) will be extracted by review of electronic medical records.
        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. The target
        volumes, i.e., gross tumor volume (GTV), clinical target volume (CTV), and planning target
        volume (PTV), and OARs will be individually reviewed by radiation oncologists with
        modifications applied as appropriate (e.g., exclusion of OARs (Organs at risk) distorted by
        disease or surgery) and will be used to train the machine learning models for supervised
        learning. 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 for image
        heterogeneity, including field strength for MRI and different image acquisition parameters.
        For autosegmentation, 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. Since the OARs are uniformly
        applicable for different histology or tumor sites, autosegmentation training will be applied
        to the entire dataset. However, given there are variations in target volume delineations
        (e.g., for circumscribed vs. diffuse tumors, low grade vs. high grade), the training/ testing
        for TVs will be applicable for individual disease entities. The effectiveness of the
        automated model will be tested using the dice similarity coefficient between manually
        segmentation regions and AI-based segments. For outcome prediction (e.g., survival and
        toxicities), the next step will include feature extraction from images (CT, MRI, PET)
        corresponding to different TV and OARs and RT dose distribution data converted to volumetric
        image/ number data (dosiomics), 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.
        pseudoprogression, 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 the 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 opensource software like Python (python.org). As a
        tertiary objective in the project, we will develop a protocol for anonymized data storage
        (clinical information, radiation planning and response assessment images, radiation planning
        data, intra-treatment images like cone beam computed tomography) in a secured image biobank
        repository with protected cloud space. Also, natural language processing (NLP) algorithms
        will be developed to train and validate model for extraction, and documentation of clinical
        variables extracted for the study. With continuous advancements in computational science,
        available newer analytical techniques and platforms will be applied as appropriate by
        collaborators from Bhabha Atomic Research Centre, Mumbai, by sharing anonymized data.
  observational model: Case-Only
  time perspective: Retrospective
  measure: Autosegmentation of organs at risk and target volumes.
  time frame: 5 years
  description: The agreement between manual segmentation and automated segmentation using an artificial intelligence-based model will be assessed using Dice coefficient of similarity.
  measure: Survival Analysis and Toxicity Estimation
  time frame: 5 years
  description: Quantitative image analysis from target volumes and organs at risk and correlation with survival and normal tissue complications. Survival analysis will be done using Kaplan Meier method and nomograms will be constructed from quantitative imaging features. Toxicity assessment will include the incidence of radionecrosis and the correlation of quantitative imaging markers will be done using regression models or machine learning algorithms.
  measure: Dosiomic analysis
  time frame: 5 years
  description: Quantitative analysis of radiation dosimetric maps and correlation with survival and toxicity. The performance metrics of the radiomics model in the prediction of recurrence/ death or toxicity (necrosis) will be compared with ground truth using sensitivity, specificity, accuracy, area under curve.
  measure: Response assessment following treatment
  time frame: 5 years
  description: Development of an automated response assessment algorithm using the images (MRI/ CT/ PET scan) following radiation. The predicted response using machine learning algorithms will be compared with actual outcomes (response/ stable disease/ progressive disease) and performance measured in the form of sensitivity, specificity, accuracy, area under curve.
  measure: Automated radiation planning and evaluation
  time frame: 5 years
  description: Application of artificial intelligence algorithms in the treatment planning system for automated radiation plan generation and evaluation. The plans generated using artificial intelligence algorithms will be manually evaluated by radiation oncologists/ physicists and reported with proportions/ratios.
  measure: Data Banking
  time frame: 5 years
  description: Development of a data bank to store clinical details, images, and radiation plans. The annotated clinical, imaging, and radiation data will be secured in a database (no formal assessment is planned for the efficacy of data storage). Standardization of clinical variables with automated data extraction from the medical records and PACS will be established.
  measure: Natural language processing for data interpretation
  time frame: 5 years
  description: Development of NLP algorithms for automated recording of data and interpretation. The performance of NLP algorithms in interpretation will be assessed manually (with actual data/ records) and reported with confusion matrices e.g., sensitivity, specificity
  intervention type: Diagnostic Test
  intervention name: The images (CT, MRI, PET) used for RT planning, mid-treatment imaging as part of IGRT or disease evaluation, and response assessment/ surveillance post-RT
  description: The radiation plans and dose-volume histogram will be obtained from TPS. All the images and radiation-related data will be downloaded from the PACS and TPS, applying anonymization filters. Clinical features (patient, disease, treatment-related characteristics, and outcomes) will be extracted by review of electronic medical records. 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
  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: 91-22-24177000
  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: Central Nervous System Neoplasms