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/NCT05975736</url>
    </required_header>
    <id_info>
      <org_study_id>2007P000646-E</org_study_id>
      <nct_id>NCT05975736</nct_id>
    </id_info>
    <brief_title>Searching for Masses and Calcifications at the Same Time in Breast Cancer Screening</brief_title>
    <official_title>Improving Perception in Digital Breast Tomography: Searching for Masses and Calcifications at the Same Time. NOTE: Note, This is One Study Under Study ID 386409 Projects 1,2,3: Experiments With Non Experts</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Brigham and Women's Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Brigham and Women's Hospital</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>
        Brief Summary&#xD;
  &#xD;
        A task like breast cancer screening (mammography) can be described as a &quot;hybrid search&quot; task.&#xD;
        In basic visual search tasks, observers search for a target among distractors that are not&#xD;
        the target. In hybrid search, observers search for two or more target types. In mammography,&#xD;
        observers are searching for masses, calcifications (&quot;calcs&quot;), and some other signs of cancer&#xD;
        like architectural distortion. In this experiment, the investigators have created a simulated&#xD;
        version of mammography where non-expert (non-radiologist) observers can look for simulated&#xD;
        masses and calcs. There are two types of stimuli, a 2D version (like an x-ray) and a 3D&#xD;
        version (like the output of Digital Breast Tomosynthesis - DBT). The question that is being&#xD;
        asked is whether it is better to ask about masses and calcs separately (first one, then the&#xD;
        other) or to just let observers look for both at the same time.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Brief Summary&#xD;
  &#xD;
        A task like breast cancer screening (mammography) can be described as a &quot;hybrid search&quot; task.&#xD;
        In basic visual search tasks, observers search for a target among distractors that are not&#xD;
        the target. In hybrid search, observers search for two or more target types. In mammography,&#xD;
        observers are searching for masses, calcifications (&quot;calcs&quot;), and some other signs of cancer&#xD;
        like architectural distortion. In this experiment, the investigators have created a simulated&#xD;
        version of mammography where non-expert (non-radiologist) observers can look for simulated&#xD;
        masses and calcs. There are two types of stimuli, a 2D version (like an x-ray) and a 3D&#xD;
        version (like the output of Digital Breast Tomosynthesis - DBT). The question that is being&#xD;
        asked is whether it is better to ask about masses and calcs separately (first one, then the&#xD;
        other) or to just let observers look for both at the same time.&#xD;
  &#xD;
        Extended protocol&#xD;
  &#xD;
        NOTE: This registration is linked to a Human Subjects registration in ASSIST. That, in turn,&#xD;
        is part of an NCI Grant, CA207490. The grant describes many proposed experiments and notes&#xD;
        that many others might be done as follow-up studies. At the suggestion of the NIH, the&#xD;
        investigators grouped these studies into three &quot;studies&quot;, each covering multiple experiments.&#xD;
        The experiment described here is part of &quot;Study ID 386409 Projects 1,2,3: Experiments with&#xD;
        Non experts&quot;. It is not possible to register a set of experiments through the PRS system in&#xD;
        CT.gov and it is not possible to file an annual report for the grant (RPPR) without an NCT&#xD;
        number for projects that have started collecting participants. Accordingly, the investigators&#xD;
        are describing one experiment here that would be part of the &quot;Project 2&quot; bundle of studies.&#xD;
  &#xD;
        These experiments take what is known about &quot;hybrid search&quot; tasks from the lab and applies it&#xD;
        to clinical mammography. Hybrid search tasks are tasks that involve looking for more than one&#xD;
        type of target at the same time (e.g. search for this pillow and any animal in the scene in&#xD;
        front of you).&#xD;
  &#xD;
        Standard 2D mammography and 3D DBT can be thought of as hybrid search tasks. That is,&#xD;
        mammography can be thought of as a hybrid search for masses and calcifications. What is the&#xD;
        optimal way to do this task? Should readers look for both types of targets at the same time&#xD;
        or should they be asked to look for one target type and then the other. It is likely that the&#xD;
        sequential approach improves accuracy but at some cost in time. The investigators will test&#xD;
        that hypothesis of a &quot;speed/accuracy tradeoff&quot;.&#xD;
  &#xD;
        The investigators have developed a version of the 2D mammography task that can be run on&#xD;
        non-experts. Breast parenchyma is simulated with 1/fk noise (k=1.8 - 2.8). Artificial masses&#xD;
        and calcifications (calcs) can be added to this background. Masses are relatively low&#xD;
        contrast blobs that are deemed to be &quot;bad&quot; if they have many irregular bumps and benign if&#xD;
        they are smoother. Calcs are brighter, higher contrast, small spots. These are deemed to be&#xD;
        benign unless they form a cluster of nearby spots in the image. The task is designed to be&#xD;
        hard. The goal is performance producing a d' value in the range of 2.0 to 2.5. Bad and benign&#xD;
        stimuli are selected from uniformly distributed stimuli sets. For masses, when the target is&#xD;
        'bad', bad mass is randomly selected from 5 levels of bumpiness. The same rule is applied for&#xD;
        selecting benign stimuli from five less-bumpy levels. For calcifications, the number of&#xD;
        pixels in a cluster is randomly selected between 4~12.&#xD;
  &#xD;
        There are four conditions to be tested in the first experiment:&#xD;
  &#xD;
          1. Search for masses alone&#xD;
  &#xD;
          2. Search for calcs alone&#xD;
  &#xD;
          3. Search for both together&#xD;
  &#xD;
          4. Search for one after the other (mass -&gt; calc) or (calc -&gt; mass)&#xD;
  &#xD;
        In addition, there are two different ways of presenting the four conditions. These could be&#xD;
        considered to be two arms of the study. Either participants could see each condition in a&#xD;
        block of 100 trials or the four conditions could be mixed into one set of 400 trials (with&#xD;
        breaks every 100 trials to keep the pacing of the experiment consistent.&#xD;
  &#xD;
        In the initial experiment, target prevalence will be 60%. In future experiments, lower&#xD;
        prevalence will be tested. The measures of interest are accuracy and RT and the most&#xD;
        interesting question is whether the sequential condition produces any benefits that might be&#xD;
        worth the presumed cost in time.&#xD;
  &#xD;
        The 3D version is like the 2D version except that a volume of 1/fk noise is created and&#xD;
        masses and calcs are added so that they fade in and out of view as the observer scrolls&#xD;
        through &quot;slices&quot; through the 3D volume. This simulates DBT.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">September 1, 2022</start_date>
    <completion_date type="Anticipated">September 1, 2028</completion_date>
    <primary_completion_date type="Anticipated">September 1, 2027</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Randomized</allocation>
      <intervention_model>Parallel Assignment</intervention_model>
      <intervention_model_description>One group of observers will get separate blocks of each of four conditions. The other group will see all conditions, intermixed.</intervention_model_description>
      <primary_purpose>Basic Science</primary_purpose>
      <masking>None (Open Label)</masking>
      <masking_description>Participants will not know the goals of the study but they will be fully informed about the conditions and asked to respond &quot;as quickly and accurately as possible&quot;.</masking_description>
    </study_design_info>
    <primary_outcome>
      <measure>Time required to respond to a case</measure>
      <time_frame>Through study completion, an average of 1 year</time_frame>
      <description>The measure of response time is an index of the difficulty of each case.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Rates of false positive and false negative errors</measure>
      <time_frame>through study completion, an average of 1 year</time_frame>
      <description>Cases are either positive or negative for simulated cancer. Responses are either correct or incorrect in identifying if the case is positive or negative. Hence each response is either a true or false positive and or a true or false negative response. The relative percentages of these errors provides a measure of accuracy.</description>
    </primary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">70</enrollment>
    <condition>Mammography</condition>
    <condition>Breast Cancer</condition>
    <condition>Radiology</condition>
    <arm_group>
      <arm_group_label>Blocked Trials</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>There are four conditions in the experiment. Each condition will be presented in a separate block of 100 trials.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Mixed Trials</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>There are four conditions in the experiment. All condition will be presented, randomly mixed in a single block of 400 trials (with breaks every 100 trials).</description>
    </arm_group>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Search only for masses</intervention_name>
      <description>Participants search the simulated mammogram for simulated masses.</description>
      <arm_group_label>Blocked Trials</arm_group_label>
      <arm_group_label>Mixed Trials</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Search only for calcifications</intervention_name>
      <description>Participants search the simulated mammogram for simulated calcifications.</description>
      <arm_group_label>Blocked Trials</arm_group_label>
      <arm_group_label>Mixed Trials</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Search for both together</intervention_name>
      <description>Search the simulated mammogram for both simulated masses and simulated calcifications at the same time.</description>
      <arm_group_label>Blocked Trials</arm_group_label>
      <arm_group_label>Mixed Trials</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Search for one after the other</intervention_name>
      <description>Search the simulated mammogram for first for simulated masses and then for simulated calcifications sequentially (or vice versa).</description>
      <arm_group_label>Blocked Trials</arm_group_label>
      <arm_group_label>Mixed Trials</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Age over 18 yrs&#xD;
  &#xD;
            -  Pass Ishihara color vision test&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  vision less than 20/25 with correction&#xD;
  &#xD;
            -  history of neuromuscular or visual disorders&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Jeremy M Wolfe, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Brigham and Women's Hospital</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Jeremy M Wolfe, PhD</last_name>
      <phone>617-851-1166</phone>
      <email>jwolfe@bwh.harvard.edu</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Ava A Mitra, BA</last_name>
      <phone>617-525-3681</phone>
      <email>amitra@bwh.harvard.edu</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Brigham and Women's Hospital</name>
        <address>
          <city>Boston</city>
          <state>Massachusetts</state>
          <zip>02215</zip>
          <country>United States</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Ava A Mitra, BA</last_name>
        <phone>617-525-3681</phone>
        <email>amitra@bwh.harvard.edu</email>
      </contact>
      <contact_backup>
        <last_name>Jeremy M Wolfe, PhD</last_name>
        <phone>617-851-1166</phone>
        <email>jwolfe@bwh.harvard.edu</email>
      </contact_backup>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>July 27, 2023</study_first_submitted>
    <study_first_submitted_qc>August 2, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 4, 2023</study_first_posted>
    <last_update_submitted>August 2, 2023</last_update_submitted>
    <last_update_submitted_qc>August 2, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 4, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Brigham and Women's Hospital</investigator_affiliation>
      <investigator_full_name>Jeremy M Wolfe, PhD</investigator_full_name>
      <investigator_title>Professor</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Breast Neoplasms</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Yes</sharing_ipd>
      <ipd_description>De-identified data are posted on the Open Science Framework</ipd_description>
      <ipd_info_type>Study Protocol</ipd_info_type>
      <ipd_info_type>Informed Consent Form (ICF)</ipd_info_type>
      <ipd_time_frame>As soon as data collection and analysis is complete. Data will be available &quot;forever&quot;</ipd_time_frame>
      <ipd_access_criteria>Open</ipd_access_criteria>
    </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/NCT05975736
  org study id: 2007P000646-E
  nct id: NCT05975736
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  Brief Summary
  
        A task like breast cancer screening (mammography) can be described as a "hybrid search" task.
        In basic visual search tasks, observers search for a target among distractors that are not
        the target. In hybrid search, observers search for two or more target types. In mammography,
        observers are searching for masses, calcifications ("calcs"), and some other signs of cancer
        like architectural distortion. In this experiment, the investigators have created a simulated
        version of mammography where non-expert (non-radiologist) observers can look for simulated
        masses and calcs. There are two types of stimuli, a 2D version (like an x-ray) and a 3D
        version (like the output of Digital Breast Tomosynthesis - DBT). The question that is being
        asked is whether it is better to ask about masses and calcs separately (first one, then the
        other) or to just let observers look for both at the same time.
  Brief Summary
  
        A task like breast cancer screening (mammography) can be described as a "hybrid search" task.
        In basic visual search tasks, observers search for a target among distractors that are not
        the target. In hybrid search, observers search for two or more target types. In mammography,
        observers are searching for masses, calcifications ("calcs"), and some other signs of cancer
        like architectural distortion. In this experiment, the investigators have created a simulated
        version of mammography where non-expert (non-radiologist) observers can look for simulated
        masses and calcs. There are two types of stimuli, a 2D version (like an x-ray) and a 3D
        version (like the output of Digital Breast Tomosynthesis - DBT). The question that is being
        asked is whether it is better to ask about masses and calcs separately (first one, then the
        other) or to just let observers look for both at the same time.
  
        Extended protocol
  
        NOTE: This registration is linked to a Human Subjects registration in ASSIST. That, in turn,
        is part of an NCI Grant, CA207490. The grant describes many proposed experiments and notes
        that many others might be done as follow-up studies. At the suggestion of the NIH, the
        investigators grouped these studies into three "studies", each covering multiple experiments.
        The experiment described here is part of "Study ID 386409 Projects 1,2,3: Experiments with
        Non experts". It is not possible to register a set of experiments through the PRS system in
        CT.gov and it is not possible to file an annual report for the grant (RPPR) without an NCT
        number for projects that have started collecting participants. Accordingly, the investigators
        are describing one experiment here that would be part of the "Project 2" bundle of studies.
  
        These experiments take what is known about "hybrid search" tasks from the lab and applies it
        to clinical mammography. Hybrid search tasks are tasks that involve looking for more than one
        type of target at the same time (e.g. search for this pillow and any animal in the scene in
        front of you).
  
        Standard 2D mammography and 3D DBT can be thought of as hybrid search tasks. That is,
        mammography can be thought of as a hybrid search for masses and calcifications. What is the
        optimal way to do this task? Should readers look for both types of targets at the same time
        or should they be asked to look for one target type and then the other. It is likely that the
        sequential approach improves accuracy but at some cost in time. The investigators will test
        that hypothesis of a "speed/accuracy tradeoff".
  
        The investigators have developed a version of the 2D mammography task that can be run on
        non-experts. Breast parenchyma is simulated with 1/fk noise (k=1.8 - 2.8). Artificial masses
        and calcifications (calcs) can be added to this background. Masses are relatively low
        contrast blobs that are deemed to be "bad" if they have many irregular bumps and benign if
        they are smoother. Calcs are brighter, higher contrast, small spots. These are deemed to be
        benign unless they form a cluster of nearby spots in the image. The task is designed to be
        hard. The goal is performance producing a d' value in the range of 2.0 to 2.5. Bad and benign
        stimuli are selected from uniformly distributed stimuli sets. For masses, when the target is
        'bad', bad mass is randomly selected from 5 levels of bumpiness. The same rule is applied for
        selecting benign stimuli from five less-bumpy levels. For calcifications, the number of
        pixels in a cluster is randomly selected between 4~12.
  
        There are four conditions to be tested in the first experiment:
  
          1. Search for masses alone
  
          2. Search for calcs alone
  
          3. Search for both together
  
          4. Search for one after the other (mass -> calc) or (calc -> mass)
  
        In addition, there are two different ways of presenting the four conditions. These could be
        considered to be two arms of the study. Either participants could see each condition in a
        block of 100 trials or the four conditions could be mixed into one set of 400 trials (with
        breaks every 100 trials to keep the pacing of the experiment consistent.
  
        In the initial experiment, target prevalence will be 60%. In future experiments, lower
        prevalence will be tested. The measures of interest are accuracy and RT and the most
        interesting question is whether the sequential condition produces any benefits that might be
        worth the presumed cost in time.
  
        The 3D version is like the 2D version except that a volume of 1/fk noise is created and
        masses and calcs are added so that they fade in and out of view as the observer scrolls
        through "slices" through the 3D volume. This simulates DBT.
  allocation: Randomized
  intervention model: Parallel Assignment
  intervention model description: One group of observers will get separate blocks of each of four conditions. The other group will see all conditions, intermixed.
  primary purpose: Basic Science
  masking: None (Open Label)
  masking description: Participants will not know the goals of the study but they will be fully informed about the conditions and asked to respond "as quickly and accurately as possible".
  measure: Time required to respond to a case
  time frame: Through study completion, an average of 1 year
  description: The measure of response time is an index of the difficulty of each case.
  measure: Rates of false positive and false negative errors
  time frame: through study completion, an average of 1 year
  description: Cases are either positive or negative for simulated cancer. Responses are either correct or incorrect in identifying if the case is positive or negative. Hence each response is either a true or false positive and or a true or false negative response. The relative percentages of these errors provides a measure of accuracy.
  arm group label: Blocked Trials
  arm group type: Experimental
  description: There are four conditions in the experiment. Each condition will be presented in a separate block of 100 trials.
  arm group label: Mixed Trials
  arm group type: Experimental
  description: There are four conditions in the experiment. All condition will be presented, randomly mixed in a single block of 400 trials (with breaks every 100 trials).
  intervention type: Behavioral
  intervention name: Search only for masses
  description: Participants search the simulated mammogram for simulated masses.
  arm group label: Blocked Trials
  arm group label: Mixed Trials
  intervention type: Behavioral
  intervention name: Search only for calcifications
  description: Participants search the simulated mammogram for simulated calcifications.
  arm group label: Blocked Trials
  arm group label: Mixed Trials
  intervention type: Behavioral
  intervention name: Search for both together
  description: Search the simulated mammogram for both simulated masses and simulated calcifications at the same time.
  arm group label: Blocked Trials
  arm group label: Mixed Trials
  intervention type: Behavioral
  intervention name: Search for one after the other
  description: Search the simulated mammogram for first for simulated masses and then for simulated calcifications sequentially (or vice versa).
  arm group label: Blocked Trials
  arm group label: Mixed Trials
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Jeremy M Wolfe, PhD
  role: Principal Investigator
  affiliation: Brigham and Women's Hospital
  last name: Jeremy M Wolfe, PhD
  phone: 617-851-1166
  email: jwolfe@bwh.harvard.edu
  last name: Ava A Mitra, BA
  phone: 617-525-3681
  email: amitra@bwh.harvard.edu
  facility: 
  status: Recruiting
  contact: 
  contact backup: 
  country: United States
  responsible party type: Principal Investigator
  investigator affiliation: Brigham and Women's Hospital
  investigator full name: Jeremy M Wolfe, PhD
  investigator title: Professor
  mesh term: Breast Neoplasms
  sharing ipd: Yes
  ipd description: De-identified data are posted on the Open Science Framework
  ipd info type: Study Protocol
  ipd info type: Informed Consent Form (ICF)
  ipd time frame: As soon as data collection and analysis is complete. Data will be available "forever"
  ipd access criteria: Open