<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/NCT04489368</url>
  </required_header>
  <id_info>
    <org_study_id>RGCIRC/IRB/80/2020</org_study_id>
    <nct_id>NCT04489368</nct_id>
  </id_info>
  <brief_title>Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence &amp; Machine Learning</brief_title>
  <acronym>QARC</acronym>
  <official_title>Pathological Response Prediction to Neo-adjuvant Chemoradiotherapy in Esophageal Carcinoma and Comparison of Engineered Features Versus Deep Learning Models</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Dr Kundan Singh Chufal</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Rajiv Gandhi Cancer Institute &amp; Research Center, India</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>
      In esophageal carcinoma, neoadjuvant concurrent chemo-radiotherapy (NA-CCRT) followed by&#xD;
      surgery is the current standard of care and ample evidence has accumulated supporting the&#xD;
      view that complete pathological response (pCR) is a positive prognostic marker for improved&#xD;
      outcomes. Predicting the probability of achieving pCR prior to neoadjuvant treatment could&#xD;
      permit modification of treatment protocols for those patients unlikely to achieve pCR.&#xD;
&#xD;
      Radiomics is a new entrant in the field of imaging where specific features are derived from&#xD;
      the intensity and distribution pattern of pixels based on a region-of-interest (ROI). The&#xD;
      features thus extracted can then be used for prediction modelling similar to other -omics&#xD;
      datasets. Preliminary investigations examining its utility have been performed and its&#xD;
      applications have thus far focused on screening and survival prediction after treatment. Due&#xD;
      to the multi-dimensional nature of data extracted using radiomics, Artificial Intelligence&#xD;
      (AI) methods are ideally suited for analysing and modelling radiomic features.&#xD;
&#xD;
      Machine Learning (ML) and Deep Learning (DL)[utilising Convolutional Neural Networks (CNN)]&#xD;
      are both part of the AI framework. In contrast to ML, DL is a new entrant and has been&#xD;
      utilised by some medical researchers for modelling using prediction-type algorithms. Besides&#xD;
      significantly reducing the workflow associated with Radiomics-based research, feature&#xD;
      engineering and modelling using DL are immune to the effects of incorrect ROI delineation.&#xD;
      However, the main limitation of DL is the 'blackbox' effect, in which the underlying basis of&#xD;
      a CNN is not known. This has been mitigated in part by the visualisation of activation maps&#xD;
      directly on the image dataset to prove biological plausibility of predictions. The&#xD;
      comparative performance of both types of modelling is also not known.&#xD;
&#xD;
      Our objective is to investigate pCR probability in our study population using radiomics-based&#xD;
      ML and AI-based modelling. We will also investigate the comparative performance of both&#xD;
      modelling techniques. For DL based prediction modelling, we will attempt to provide&#xD;
      biological plausibility on the basis of activation maps.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Active, not recruiting</overall_status>
  <start_date type="Actual">January 16, 2020</start_date>
  <completion_date type="Anticipated">July 2023</completion_date>
  <primary_completion_date type="Anticipated">July 2023</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>Develop models to predict pCR based on pre-neoadjuvant imaging modalities</measure>
    <time_frame>August 2021</time_frame>
  </primary_outcome>
  <primary_outcome>
    <measure>Perform a clinical audit of patient outcomes (OS, RFS, pCR rate) after new-adjuvant chemoradiation and esophagectomy</measure>
    <time_frame>January 2020</time_frame>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">150</enrollment>
  <condition>Esophageal Neoplasm</condition>
  <arm_group>
    <arm_group_label>Study Group</arm_group_label>
    <description>Patients undergoing NA-CCRT followed by Surgery</description>
  </arm_group>
  <intervention>
    <intervention_type>Radiation</intervention_type>
    <intervention_name>Neo-Adjuvant Radiotherapy</intervention_name>
    <description>Neo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy.</description>
    <arm_group_label>Study Group</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Drug</intervention_type>
    <intervention_name>Neo-Adjuvant Chemotherapy</intervention_name>
    <description>Neo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy.</description>
    <arm_group_label>Study Group</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Procedure</intervention_type>
    <intervention_name>Esophagectomy</intervention_name>
    <description>Esophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation</description>
    <arm_group_label>Study Group</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patient records in each participating research center from January 2011 to 1st May 2020&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  ECOG Performance Status: 0-2&#xD;
&#xD;
          -  Patients with histopathological or cytopathological confirmed malignancy of the&#xD;
             esophagus&#xD;
&#xD;
          -  Histology: Squamous Cell Carcinoma and Adenocarcinoma&#xD;
&#xD;
          -  Patients should have received NeoAdjuvant Concurrent Chemoradiation (NACCRT) followed&#xD;
             by Surgery&#xD;
&#xD;
          -  All therapeutic interventions (Radiotherapy, Chemotherapy &amp; Surgery) delivered within&#xD;
             participating institutions&#xD;
&#xD;
          -  At least one pre-NACCRT DICOM imaging dataset (HRCT/ 18-FDG PET-CT/ Radiotherapy&#xD;
             planning CT) for each patient&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients with any metallic implants in the region of interest&#xD;
&#xD;
          -  Patient with locally advanced disease or metastatic disease (T4 disease, Fistula,&#xD;
             metastases)&#xD;
&#xD;
          -  Patients with prior history of radiotherapy in the same region&#xD;
&#xD;
          -  Patients developing a second malignancy in the esophagus&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Kundan S Chufal, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Rajiv Gandhi Cancer Institute &amp; Research Center</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Illawarra Cancer Care Centre</name>
      <address>
        <city>Wollongong</city>
        <zip>2500</zip>
        <country>Australia</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Rajiv Gandhi Cancer Institute &amp; Research Center</name>
      <address>
        <city>New Delhi</city>
        <state>Delhi</state>
        <zip>110019</zip>
        <country>India</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Australia</country>
    <country>India</country>
  </location_countries>
  <verification_date>December 2022</verification_date>
  <study_first_submitted>July 23, 2020</study_first_submitted>
  <study_first_submitted_qc>July 23, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 28, 2020</study_first_posted>
  <last_update_submitted>December 25, 2022</last_update_submitted>
  <last_update_submitted_qc>December 25, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">December 28, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor-Investigator</responsible_party_type>
    <investigator_affiliation>Rajiv Gandhi Cancer Institute &amp; Research Center, India</investigator_affiliation>
    <investigator_full_name>Dr Kundan Singh Chufal</investigator_full_name>
    <investigator_title>Senior Consultant &amp; Chief of Thoracic Radiation Oncology, Department of Radiation Oncology</investigator_title>
  </responsible_party>
  <keyword>Artificial Intelligence</keyword>
  <keyword>Deep Learning</keyword>
  <keyword>Neoadjuvant Chemoradiation</keyword>
  <keyword>Pathologic Response</keyword>
  <keyword>Radiomics</keyword>
  <keyword>Radiotherapy</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Esophageal Neoplasms</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</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/NCT04489368
org study id: RGCIRC/IRB/80/2020
nct id: NCT04489368
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
In esophageal carcinoma, neoadjuvant concurrent chemo-radiotherapy (NA-CCRT) followed by
      surgery is the current standard of care and ample evidence has accumulated supporting the
      view that complete pathological response (pCR) is a positive prognostic marker for improved
      outcomes. Predicting the probability of achieving pCR prior to neoadjuvant treatment could
      permit modification of treatment protocols for those patients unlikely to achieve pCR.

      Radiomics is a new entrant in the field of imaging where specific features are derived from
      the intensity and distribution pattern of pixels based on a region-of-interest (ROI). The
      features thus extracted can then be used for prediction modelling similar to other -omics
      datasets. Preliminary investigations examining its utility have been performed and its
      applications have thus far focused on screening and survival prediction after treatment. Due
      to the multi-dimensional nature of data extracted using radiomics, Artificial Intelligence
      (AI) methods are ideally suited for analysing and modelling radiomic features.

      Machine Learning (ML) and Deep Learning (DL)[utilising Convolutional Neural Networks (CNN)]
      are both part of the AI framework. In contrast to ML, DL is a new entrant and has been
      utilised by some medical researchers for modelling using prediction-type algorithms. Besides
      significantly reducing the workflow associated with Radiomics-based research, feature
      engineering and modelling using DL are immune to the effects of incorrect ROI delineation.
      However, the main limitation of DL is the 'blackbox' effect, in which the underlying basis of
      a CNN is not known. This has been mitigated in part by the visualisation of activation maps
      directly on the image dataset to prove biological plausibility of predictions. The
      comparative performance of both types of modelling is also not known.

      Our objective is to investigate pCR probability in our study population using radiomics-based
      ML and AI-based modelling. We will also investigate the comparative performance of both
      modelling techniques. For DL based prediction modelling, we will attempt to provide
      biological plausibility on the basis of activation maps.
observational model: Cohort
time perspective: Other
measure: Develop models to predict pCR based on pre-neoadjuvant imaging modalities
time frame: August 2021
measure: Perform a clinical audit of patient outcomes (OS, RFS, pCR rate) after new-adjuvant chemoradiation and esophagectomy
time frame: January 2020
arm group label: Study Group
description: Patients undergoing NA-CCRT followed by Surgery
intervention type: Radiation
intervention name: Neo-Adjuvant Radiotherapy
description: Neo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy.
arm group label: Study Group
intervention type: Drug
intervention name: Neo-Adjuvant Chemotherapy
description: Neo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy.
arm group label: Study Group
intervention type: Procedure
intervention name: Esophagectomy
description: Esophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation
arm group label: Study Group
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Kundan S Chufal, MD
role: Principal Investigator
affiliation: Rajiv Gandhi Cancer Institute & Research Center
facility: 
facility: 
country: Australia
country: India
responsible party type: Sponsor-Investigator
investigator affiliation: Rajiv Gandhi Cancer Institute & Research Center, India
investigator full name: Dr Kundan Singh Chufal
investigator title: Senior Consultant & Chief of Thoracic Radiation Oncology, Department of Radiation Oncology
mesh term: Esophageal Neoplasms
sharing ipd: No