<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/NCT04441775</url>
  </required_header>
  <id_info>
    <org_study_id>D20003</org_study_id>
    <secondary_id>75N91019C00054-0-9999-1</secondary_id>
    <nct_id>NCT04441775</nct_id>
  </id_info>
  <brief_title>Artificial Intelligence for Prostate Cancer Treatment Planning</brief_title>
  <official_title>Protocol for the Development of a Machine Learning Model for Prostate Cancer Treatment Planning</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Dartmouth-Hitchcock Medical Center</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Oregon Health and Science University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University of Massachusetts, Worcester</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>National Cancer Institute (NCI)</agency>
      <agency_class>NIH</agency_class>
    </collaborator>
    <collaborator>
      <agency>NRG Oncology</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Nicolalde R&amp;D</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Dartmouth-Hitchcock Medical Center</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>
      This project's goal is to develop and test an application that uses Artificial Intelligence&#xD;
      (AI) to improve consistency and quality of Radiation Treatment (RT) plans for prostate&#xD;
      cancer. By understanding expert planner preferences in structure contouring and treatment&#xD;
      planning, and combining this framework with planning data and outcomes amassed in NRG&#xD;
      clinical trials, AI models may be trained to produce contours and treatment plans that are&#xD;
      indistinguishable or even potentially deemed superior to those produced by individual&#xD;
      experts.&#xD;
&#xD;
      At the conclusion of this contract, the awardees will provide a software product which, when&#xD;
      given the input of a description of desired anatomical target volumes and target doses along&#xD;
      with a patient's CT scans, will generate target volumes and radiation treatment plans based&#xD;
      upon a &quot;gold standard&quot; amalgamated from the input of multiple experts, thereby achieving&#xD;
      desired doses to target volumes while meeting or exceeding the dose-volume constraints&#xD;
      imposed by adjacent normal tissues.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      PHASE 1&#xD;
&#xD;
        1. Develop a process and tools (DAST) to capture the rationale, criteria, and logical basis&#xD;
           behind the treatment planning process using well understood Human Factors knowledge&#xD;
           gathering methodologies and Machine Learning tools.&#xD;
&#xD;
        2. Build the AI technology to learn the process and apply it to generating treatment plans.&#xD;
           Images and expert-drawn volumes from Radiation Therapy Oncology Group (RTOG) 0938 will&#xD;
           be used for initial training of the AI system. These data are not Dartmouth-Hitchcock&#xD;
           Medical Center (DHMC) patients, but rather were consented and acquired through RTOG&#xD;
           0938. These data are housed at NRG/RTOG headquarters in Philadelphia. NRG already has an&#xD;
           established, IRB-approved protocol for exploring AI systems for the 0938 data set. A&#xD;
           minimum of 30 cases will be used for this initial training work for the AI system to&#xD;
           &quot;learn&quot; volume segmentation of important structures and targets. Additional patients&#xD;
           from the available 200+ patients on 0938 may be added for additional AI learning of&#xD;
           volume segmentation as initial software programming is implemented.&#xD;
&#xD;
        3. Determine the optimal number of historic treatment plans to train the AI technology and&#xD;
           test it. 45 cases will be provided by NRG from NRG/RTOG studies 0415, 0126, and 0521&#xD;
           (totaling 135 cases), respectively representing favorable-, intermediate-, and high-risk&#xD;
           prostate cancer treatments. These scans and expert-defined volumes are part of NRG&#xD;
           datasets housed in Philadelphia. All these patients already signed study-specific&#xD;
           consents which included permissions to allow personally specific clinical information to&#xD;
           be used for other cancer-related studies. IRB review of the use of these data for this&#xD;
           specific study is anticipated to be achieved through NRG mechanisms in the context of&#xD;
           these prior consents, to assure that the use of these data for this specific AI study is&#xD;
           appropriate and approved. Provision is anticipated of a total of 30 additional patient&#xD;
           cases as &quot;gold standards,&quot; 10 each from DHMC, University of Massachusetts (UMass), and&#xD;
           Oregon Health Sciences University (OHSU), respectively, all initially planned and&#xD;
           treated within the 2015-2018 time frame. The first 5 from each institution will be&#xD;
           favorable-risk patients, and the next 5 from each institution will be high-risk patients&#xD;
           (thereby achieving a wide range of treatment approaches, with more to be added&#xD;
           subsequently). As part of this effort, each individual institution will contact its own&#xD;
           specific patients (5 favorable-risk, 5 high-risk) to obtain study-specific consent for&#xD;
           the use of their data for this protocol. Once anonymized, these scans and plans will be&#xD;
           shared across all three institutions. For each of these patients, the other two&#xD;
           (&quot;non-host&quot;) institutions will create their own volumes and plans. Using a modified&#xD;
           Delphi approach, the three teams will then meet to generate an agreed-upon &quot;composite&quot;&#xD;
           plan for each patient. Thus, in total there will be four treatment plans for each of&#xD;
           these 30 patients, yielding 120 total plans that will serve as the &quot;gold standard&quot; for&#xD;
           this AI project, and will be inputted for testing/validation to the AI system.&#xD;
&#xD;
           PHASE 2&#xD;
&#xD;
        4. Expand the database to include intermediate-risk patients, 5 respectively from each&#xD;
           institution, following the above procedures, to yield an additional 60 plans to serve as&#xD;
           additional inputs for the AI system.&#xD;
&#xD;
        5. Validate and test the AI technology by inputting patient images and target delineations&#xD;
           from historic case data and assessing whether the AI technology-generated plans are&#xD;
           &quot;consistent&quot; with the final plans that were created by expert clinicians.&#xD;
&#xD;
        6. Test the technology with new patient case data and validate the plan with a team of&#xD;
           expert clinicians. This will involve &quot;modified Turing tests,&quot; as developed in NRG-RTOG&#xD;
           studies exploring AI applications.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">June 22, 2020</start_date>
  <completion_date type="Anticipated">June 2023</completion_date>
  <primary_completion_date type="Anticipated">June 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>Change (trend) in AI System performance over time</measure>
    <time_frame>Baseline measure (Phase 1): June 2021. Repeat assessments every 6 months (Phase 2): June 2021 - June 2023.</time_frame>
    <description>Validate the AI technology by inputting images and targets from historic case data and then assessing whether the AI-generated plans are comparable or even improved when measured against plans created by expert clinicians for the same patient data. To accomplish this, methodology will employ &quot;modified Turing tests,&quot; as previously developed in NRG-RTOG studies exploring AI applications in other venues, whereby blinded experts evaluate alternative plans, score them on a variety of criteria, and more generally assess whether these were generated via machine learning or via human intelligence.&#xD;
These quantified comparisons will be performed several times. The first will be at the end of Phase I (spring of 2021). Phase II will introduce further data and plans. Repeat comparisons of AI- versus human-performance will be performed every six months during Phase II. Final comparisons and overall trend analysis will be reported at conclusion of Phase II in 2023.</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">195</enrollment>
  <condition>Prostate Cancer</condition>
  <condition>Artificial Intelligence</condition>
  <condition>Radiotherapy</condition>
  <arm_group>
    <arm_group_label>All patients for enrollment and analysis</arm_group_label>
    <description>Patients previously treated with RT for prostate cancer who are now being enrolled into this study for data analysis and incorporation into Artificial Intelligence (AI) models</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>AI-assisted RT modelling</intervention_name>
    <description>Artificial Intelligence assisted Radiation Treatment</description>
    <arm_group_label>All patients for enrollment and analysis</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Male patients diagnosed with prostate xancer who have received RT and now have elected to&#xD;
        provide data for the development of AI-models.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Favorable-risk inclusion criteria (as per RTOG 0415)&#xD;
&#xD;
               1. Histologically confirmed prostate adenocarcinoma&#xD;
&#xD;
               2. Gleason Score &lt;= 3+4 = 7 ( with less than 50% of all cores positive, and no more&#xD;
                  than one core with Gleason 3+4=7)&#xD;
&#xD;
               3. Clinical stage T1-T2b&#xD;
&#xD;
               4. Prostate Specific Antigen (PSA) &lt;10 ng/ml within 180 days prior to treatment&#xD;
                  planning. PSA may not have been acquired within 30 days of stopping finasteride,&#xD;
                  or within 90 days of stopping dutasteride&#xD;
&#xD;
               5. RT treatment initiated between 1/1/15 and 12/31/16&#xD;
&#xD;
               6. Prostate MRI used as part of RT treatment planning&#xD;
&#xD;
               7. No previous hormonal therapy, such as LHRH agonists, estrogens, anti-androgens,&#xD;
                  or surgical castration&#xD;
&#xD;
               8. No previous use of finasteride within 30 days prior to planning&#xD;
&#xD;
               9. No previous use of dutasteride within 90 days prior to planning&#xD;
&#xD;
          -  High-risk inclusion criteria (as per RTOG 0521)&#xD;
&#xD;
               1. Histologically confirmed prostate adenocarcinoma&#xD;
&#xD;
               2. PSA &lt; 150&#xD;
&#xD;
               3. One of the following combinations:&#xD;
&#xD;
                    1. Gleason 7 or 8 and PSA &gt;= 20&#xD;
&#xD;
                    2. Gleason 8 and clinical T-stage &gt; T2a&#xD;
&#xD;
                    3. Gleason 9 or 10&#xD;
&#xD;
               4. Negative bone scan within 180 days of planning&#xD;
&#xD;
               5. XRT treatment initiated between 1/1/15 and 12/31/16&#xD;
&#xD;
               6. Prostate MRI used as part of RT treatment planning&#xD;
&#xD;
               7. No previous hormonal therapy, such as LHRH agonists, estrogens, anti-androgens,&#xD;
                  or surgical castration, prior to prostate cancer diagnosis&#xD;
&#xD;
          -  Intermediate-risk inclusion criteria&#xD;
&#xD;
               1. Histologically confirmed prostate adenocarcinoma&#xD;
&#xD;
               2. PSA &lt; 20&#xD;
&#xD;
               3. Gleason 7 or 8&#xD;
&#xD;
               4. Not meeting criteria for favorable- or high-risk disease, as per above&#xD;
&#xD;
               5. XRT treatment initiated between 1/1/15 and 12/31/16&#xD;
&#xD;
               6. Prostate MRI used as part of RT treatment planning&#xD;
&#xD;
               7. No previous hormonal therapy, such as LHRH agonists, estrogens, anti-androgens,&#xD;
                  or surgical castration, prior to prostate cancer diagnosis&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Prior or concurrent invasive malignancy (except non-melanomatous skin cancer) or&#xD;
             lymphomatous/hematogenous malignancy unless continually disease free for a minimum of&#xD;
             5 years&#xD;
&#xD;
          2. Evidence of distant metastases&#xD;
&#xD;
          3. Regional lymph node involvement&#xD;
&#xD;
          4. Previous radical prostate surgery or cryosurgery&#xD;
&#xD;
          5. Previous pelvic irradiation or prostate brachytherapy&#xD;
&#xD;
          6. Previous or concurrent cytotoxic chemotherapy for prostate cancer&#xD;
&#xD;
          7. Severe, active comorbidity, defined as follows:&#xD;
&#xD;
               1. Unstable angina, congestive heart failure, and/or transmural myocardial&#xD;
                  infarction requiring hospitalization within the last 6 months&#xD;
&#xD;
               2. Acute bacterial or fungal infection requiring intravenous antibiotics&#xD;
&#xD;
               3. Hepatic insufficiency resulting in clinical jaundice or coagulopathy&#xD;
&#xD;
               4. Acquired immune deficiency syndrome based upon current CDC-defined criteria&#xD;
&#xD;
          8. Zubrod performance status 2 or worse&#xD;
&#xD;
          9. Previous use of finasteride within 60 days of planning&#xD;
&#xD;
         10. Previous use of dutasteride within 180 days of planning&#xD;
      </textblock>
    </criteria>
    <gender>Male</gender>
    <minimum_age>21 Years</minimum_age>
    <maximum_age>90 Years</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Alan C. Hartford, MD PhD FACR</last_name>
    <role>Principal Investigator</role>
    <affiliation>Dartmouth-Hitchcock Medical Center</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Alan C. Hartford, MD PhD FACR</last_name>
    <phone>(603) 650-6600</phone>
    <email>Alan.C.Hartford@hitchcock.org</email>
  </overall_contact>
  <location>
    <facility>
      <name>Dartmouth-Hitchcock Medical Center</name>
      <address>
        <city>Lebanon</city>
        <state>New Hampshire</state>
        <zip>03756</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Alan C Hartford, MD</last_name>
      <phone>603-650-6600</phone>
      <email>alan.c.hartford@hitchcock.org</email>
    </contact>
    <contact_backup>
      <last_name>Colleen J Fox, PhD</last_name>
      <phone>603-650-6600</phone>
      <email>Colleen.J.Fox@hitchcock.org</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>October 2021</verification_date>
  <study_first_submitted>June 6, 2020</study_first_submitted>
  <study_first_submitted_qc>June 19, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 22, 2020</study_first_posted>
  <last_update_submitted>October 10, 2021</last_update_submitted>
  <last_update_submitted_qc>October 10, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">October 19, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Dartmouth-Hitchcock Medical Center</investigator_affiliation>
    <investigator_full_name>Alan Hartford</investigator_full_name>
    <investigator_title>Associate Professor of Medicine (Radiation Oncology)</investigator_title>
  </responsible_party>
  <keyword>Prostate Cancer</keyword>
  <keyword>Artificial Intelligence</keyword>
  <keyword>Radiation Therapy</keyword>
  <keyword>Radiation Planning</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Prostatic Neoplasms</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>There are no plans to share individual participant data at this time.</ipd_description>
  </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/NCT04441775
org study id: D20003
secondary id: 75N91019C00054-0-9999-1
nct id: NCT04441775
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
This project's goal is to develop and test an application that uses Artificial Intelligence
      (AI) to improve consistency and quality of Radiation Treatment (RT) plans for prostate
      cancer. By understanding expert planner preferences in structure contouring and treatment
      planning, and combining this framework with planning data and outcomes amassed in NRG
      clinical trials, AI models may be trained to produce contours and treatment plans that are
      indistinguishable or even potentially deemed superior to those produced by individual
      experts.

      At the conclusion of this contract, the awardees will provide a software product which, when
      given the input of a description of desired anatomical target volumes and target doses along
      with a patient's CT scans, will generate target volumes and radiation treatment plans based
      upon a "gold standard" amalgamated from the input of multiple experts, thereby achieving
      desired doses to target volumes while meeting or exceeding the dose-volume constraints
      imposed by adjacent normal tissues.
PHASE 1

        1. Develop a process and tools (DAST) to capture the rationale, criteria, and logical basis
           behind the treatment planning process using well understood Human Factors knowledge
           gathering methodologies and Machine Learning tools.

        2. Build the AI technology to learn the process and apply it to generating treatment plans.
           Images and expert-drawn volumes from Radiation Therapy Oncology Group (RTOG) 0938 will
           be used for initial training of the AI system. These data are not Dartmouth-Hitchcock
           Medical Center (DHMC) patients, but rather were consented and acquired through RTOG
           0938. These data are housed at NRG/RTOG headquarters in Philadelphia. NRG already has an
           established, IRB-approved protocol for exploring AI systems for the 0938 data set. A
           minimum of 30 cases will be used for this initial training work for the AI system to
           "learn" volume segmentation of important structures and targets. Additional patients
           from the available 200+ patients on 0938 may be added for additional AI learning of
           volume segmentation as initial software programming is implemented.

        3. Determine the optimal number of historic treatment plans to train the AI technology and
           test it. 45 cases will be provided by NRG from NRG/RTOG studies 0415, 0126, and 0521
           (totaling 135 cases), respectively representing favorable-, intermediate-, and high-risk
           prostate cancer treatments. These scans and expert-defined volumes are part of NRG
           datasets housed in Philadelphia. All these patients already signed study-specific
           consents which included permissions to allow personally specific clinical information to
           be used for other cancer-related studies. IRB review of the use of these data for this
           specific study is anticipated to be achieved through NRG mechanisms in the context of
           these prior consents, to assure that the use of these data for this specific AI study is
           appropriate and approved. Provision is anticipated of a total of 30 additional patient
           cases as "gold standards," 10 each from DHMC, University of Massachusetts (UMass), and
           Oregon Health Sciences University (OHSU), respectively, all initially planned and
           treated within the 2015-2018 time frame. The first 5 from each institution will be
           favorable-risk patients, and the next 5 from each institution will be high-risk patients
           (thereby achieving a wide range of treatment approaches, with more to be added
           subsequently). As part of this effort, each individual institution will contact its own
           specific patients (5 favorable-risk, 5 high-risk) to obtain study-specific consent for
           the use of their data for this protocol. Once anonymized, these scans and plans will be
           shared across all three institutions. For each of these patients, the other two
           ("non-host") institutions will create their own volumes and plans. Using a modified
           Delphi approach, the three teams will then meet to generate an agreed-upon "composite"
           plan for each patient. Thus, in total there will be four treatment plans for each of
           these 30 patients, yielding 120 total plans that will serve as the "gold standard" for
           this AI project, and will be inputted for testing/validation to the AI system.

           PHASE 2

        4. Expand the database to include intermediate-risk patients, 5 respectively from each
           institution, following the above procedures, to yield an additional 60 plans to serve as
           additional inputs for the AI system.

        5. Validate and test the AI technology by inputting patient images and target delineations
           from historic case data and assessing whether the AI technology-generated plans are
           "consistent" with the final plans that were created by expert clinicians.

        6. Test the technology with new patient case data and validate the plan with a team of
           expert clinicians. This will involve "modified Turing tests," as developed in NRG-RTOG
           studies exploring AI applications.
observational model: Cohort
time perspective: Other
measure: Change (trend) in AI System performance over time
time frame: Baseline measure (Phase 1): June 2021. Repeat assessments every 6 months (Phase 2): June 2021 - June 2023.
description: Validate the AI technology by inputting images and targets from historic case data and then assessing whether the AI-generated plans are comparable or even improved when measured against plans created by expert clinicians for the same patient data. To accomplish this, methodology will employ "modified Turing tests," as previously developed in NRG-RTOG studies exploring AI applications in other venues, whereby blinded experts evaluate alternative plans, score them on a variety of criteria, and more generally assess whether these were generated via machine learning or via human intelligence.
These quantified comparisons will be performed several times. The first will be at the end of Phase I (spring of 2021). Phase II will introduce further data and plans. Repeat comparisons of AI- versus human-performance will be performed every six months during Phase II. Final comparisons and overall trend analysis will be reported at conclusion of Phase II in 2023.
arm group label: All patients for enrollment and analysis
description: Patients previously treated with RT for prostate cancer who are now being enrolled into this study for data analysis and incorporation into Artificial Intelligence (AI) models
intervention type: Other
intervention name: AI-assisted RT modelling
description: Artificial Intelligence assisted Radiation Treatment
arm group label: All patients for enrollment and analysis
study pop: 
sampling method: Probability Sample
criteria: 
gender: Male
minimum age: 21 Years
maximum age: 90 Years
last name: Alan C. Hartford, MD PhD FACR
role: Principal Investigator
affiliation: Dartmouth-Hitchcock Medical Center
last name: Alan C. Hartford, MD PhD FACR
phone: (603) 650-6600
email: Alan.C.Hartford@hitchcock.org
facility: 
status: Recruiting
contact: 
contact backup: 
country: United States
responsible party type: Principal Investigator
investigator affiliation: Dartmouth-Hitchcock Medical Center
investigator full name: Alan Hartford
investigator title: Associate Professor of Medicine (Radiation Oncology)
mesh term: Prostatic Neoplasms
sharing ipd: No
ipd description: There are no plans to share individual participant data at this time.