<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/NCT04461990</url>
  </required_header>
  <id_info>
    <org_study_id>BCIG</org_study_id>
    <nct_id>NCT04461990</nct_id>
  </id_info>
  <brief_title>Clinical Study of Imaging Genomics Based on Machine Learning for BCIG</brief_title>
  <official_title>Clinical Study of Imaging Genomics Based on Machine Learning for Breast Cancer Molecular Typing and Risk Prediction (BCIG)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Fudan University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>RenJi Hospital</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>International Peace Maternity and Child Health Hospital</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Fudan University</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>
      1. Identify the imaging features of breast cancer with different molecular types&#xD;
&#xD;
        2. Reveal the association between hormone receptor positive/HER2 negative breast cancer and&#xD;
           imaging histology, Oncotype Dx recurrence score&#xD;
&#xD;
        3. Combine genomics and imaging to establish a predictive model for the sensitivity of&#xD;
           HER2-positive breast cancer targeted therapy&#xD;
&#xD;
        4. Establish an imaging genomics prediction model for triple-negative breast cancer&#xD;
           molecular subtypes, and clarify the imaging genomics characteristics of the therapeutic&#xD;
           targets of each subtype&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Research design&#xD;
&#xD;
        1. Research on the molecular typing of breast cancer based on imaging features&#xD;
&#xD;
        2. Establish a Luminal breast cancer recurrence risk prediction model&#xD;
&#xD;
        3. Establish HER2 targeted therapy sensitivity prediction model&#xD;
&#xD;
        4. Establish TNBC molecular subtype prediction model Research methods Research Object This&#xD;
           study used a multi-center study to prospectively enroll breast cancer patients diagnosed&#xD;
           with pathology. All enrolled patients had complete clinical data, including demographic&#xD;
           characteristics (gender, age, menstrual status and fertility history), and pathological&#xD;
           data (histopathological data). Staging, immunohistochemical status and FISH, genetic&#xD;
           testing records the recurrence score and genotype), imaging data, complete treatment and&#xD;
           follow-up (whether there is local recurrence and metastasis, and the time of diagnosis).&#xD;
&#xD;
      Magnetic resonance examination In order to maintain the comparability between the images and&#xD;
      reduce the systematic errors, each center selects a fixed MR device for scanning. Among them,&#xD;
      a. Oncology Hospital chose to scan images with 3.0T (Siemens Skyra) MR equipment. A special&#xD;
      breast coil is used to add high-definition diffusion-weighted scanning and multi-b value&#xD;
      diffusion-weighted scanning before the dynamic enhancement scan. Dynamically enhanced&#xD;
      acquisition in 5 phases with a time resolution of 65s. b. Renji Hospital uses Netherlands&#xD;
      Philips Achieva 3.0 T superconductor MR scanner, 4-channel dedicated breast phased array&#xD;
      coil. Scanning sequences include T1WI, T2WI, T2WI fat suppression, DWI and DCE-MRI. The&#xD;
      contrast agent was Gd-DTPA, with a dose of 0.1 mmol/kg, an injection rate of 2.0 mL/s, and an&#xD;
      additional 20 mL of saline was added to the tube after injection. The T1WI scan was performed&#xD;
      first, and 5 time phases were continuously scanned after the injection of contrast agent, and&#xD;
      each time phase was separated by 61 s, for a total of 6 time phases. c. Chinese women and&#xD;
      babies are scanned with 1.5T SIEMENS AERA MR equipment and special breast coils. Scanning&#xD;
      sequence includes 5 phases of T1WI, T2WI fat suppression, DWI and dynamic enhancement scan,&#xD;
      time resolution 71s.&#xD;
&#xD;
      Image processing Use software to make semi-automatic and automatic outlines of the tumor&#xD;
      interest area, and make the outline of the tumor solid enhancement part, the entire tumor&#xD;
      area and the surrounding edema zone in the transverse position. In order to accurately&#xD;
      delineate the tumor, compare the T1 and T2 weighted and dynamically enhanced images, two&#xD;
      imaging physicians are responsible, one is responsible for delineation and the other is&#xD;
      reviewed, and the disputed area is determined after discussion by a third person. Create a&#xD;
      dynamic enhanced tumor texture analysis program to automatically extract imaging omics&#xD;
      features in the region of interest. Using a labeled data set, a computer-based automatic&#xD;
      segmentation algorithm model based on machine learning is constructed to automatically&#xD;
      extract regions of interest, and segmentation performance evaluation is performed on manually&#xD;
      delineated labels.&#xD;
&#xD;
      Statistical analysis Perform statistical analysis on the obtained images and clinical data,&#xD;
      extract image omics features and use machine learning algorithms to screen important&#xD;
      features. Use statistical tools such as SPSS and R language. Paired t test (continuous&#xD;
      variable) and chi-square test (discontinuous variable) were used to compare the clinical and&#xD;
      imaging characteristics of patients with different prognosis; correlation analysis was used&#xD;
      to evaluate the imaging histology characteristics and different pathological tissue grades,&#xD;
      Correlation between lymph node metastasis and specific gene expression; use Kaplan-Meier&#xD;
      survival curve to analyze the prognostic difference between patients with different imaging&#xD;
      omics characteristics, and use log-rank method to test the difference; use cox survival model&#xD;
      to compare clinical characteristics and imaging omics The characteristics and prognosis of&#xD;
      patients (tumor-free survival, progression-free survival, overall survival) were analyzed by&#xD;
      multiple factors. Further, deep learning algorithms can be used to automatically learn&#xD;
      imaging omics features that may be related to molecular subtypes and prognosis to build&#xD;
      prediction models.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Not yet recruiting</overall_status>
  <start_date type="Anticipated">December 1, 2020</start_date>
  <completion_date type="Anticipated">December 30, 2023</completion_date>
  <primary_completion_date type="Anticipated">December 30, 2022</primary_completion_date>
  <study_type>Observational [Patient Registry]</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <target_duration>5 Years</target_duration>
  <primary_outcome>
    <measure>Image prediction model of different molecular typing</measure>
    <time_frame>30 December，2022----30 December，2023</time_frame>
    <description>Build a model to predict molecular typing based on image&#xD;
Establish a prediction model for predicting the risk of Luminal breast cancer recurrence&#xD;
Establish a prediction model for predicting her2 targeted drug resistance&#xD;
Establishing a triple-negative molecular model for breast cancer</description>
  </primary_outcome>
  <number_of_groups>3</number_of_groups>
  <enrollment type="Anticipated">1500</enrollment>
  <condition>Breast Cancer</condition>
  <condition>Molecular Typing</condition>
  <condition>Pathology</condition>
  <condition>Image, Body</condition>
  <arm_group>
    <arm_group_label>Luminal</arm_group_label>
    <description>Luminal A：ER+ and/or PR+,HER2- Luminal B：ER+ and/or PR+,HER2+&#xD;
* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2</description>
  </arm_group>
  <arm_group>
    <arm_group_label>HER2 overexpression</arm_group_label>
    <description>ER- PR-,HER2+&#xD;
* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Triple negative</arm_group_label>
    <description>ER- PR-,HER2-&#xD;
* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2</description>
  </arm_group>
  <intervention>
    <intervention_type>Procedure</intervention_type>
    <intervention_name>Multidisciplinary cooperative comprehensive treatment</intervention_name>
    <description>Local surgery, radiation therapy, and systemic therapy such as chemotherapy, endocrine and molecular targeting.</description>
    <arm_group_label>HER2 overexpression</arm_group_label>
    <arm_group_label>Luminal</arm_group_label>
    <arm_group_label>Triple negative</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Prospectively enrolled breast cancer patients diagnosed by pathology, all clinical data of&#xD;
        all enrolled patients are complete, including demographic characteristics (gender, age,&#xD;
        menstrual status and fertility history), pathological data (staging in histopathology,&#xD;
        immunohistochemistry) Status and FISH, genetic testing records the recurrence score and&#xD;
        genotype), imaging data, complete treatment and follow-up (whether there is local&#xD;
        recurrence and metastasis, and the time of diagnosis)&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Pathological and immunohistochemical diagnosis of breast cancer by biopsy&#xD;
&#xD;
          2. No MRI contraindications and no biopsy before MRI&#xD;
&#xD;
          3. Without radiotherapy and chemotherapy before enrollment&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Those with previous history of breast cancer surgery, hormone replacement therapy and&#xD;
             chest radiotherapy&#xD;
&#xD;
          2. Patients with severe diseases who cannot cooperate with the examination&#xD;
&#xD;
          3. People with contraindications to MRI&#xD;
&#xD;
          4. The researchers believe that other conditions are not suitable for breast MRI&#xD;
             examination&#xD;
      </textblock>
    </criteria>
    <gender>Female</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Gu Ya Jia</last_name>
    <role>Principal Investigator</role>
    <affiliation>Fudan University</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Gu Ya Jia</last_name>
    <phone>86-18017317817</phone>
    <email>guyajia@126.com</email>
  </overall_contact>
  <location>
    <facility>
      <name>Fudan University Shanghai Cancer Center</name>
      <address>
        <city>Shanghai</city>
        <state>Shanghai</state>
        <zip>200032</zip>
        <country>China</country>
      </address>
    </facility>
    <contact>
      <last_name>Gu Ya Jia</last_name>
      <phone>86-18017312040</phone>
      <email>guyajia@126.com</email>
    </contact>
    <investigator>
      <last_name>Hua jia</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>Qian zhaoxia</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>Wang he</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>You chao</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Zhuang zhiguo</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Jiang ling</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Zheng rencheng</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Xiao qin</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Chen yanqiong</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Hu xiaoxin</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>August 2020</verification_date>
  <study_first_submitted>July 4, 2020</study_first_submitted>
  <study_first_submitted_qc>July 4, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 8, 2020</study_first_posted>
  <last_update_submitted>August 20, 2020</last_update_submitted>
  <last_update_submitted_qc>August 20, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">August 21, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Fudan University</investigator_affiliation>
    <investigator_full_name>Guyajia</investigator_full_name>
    <investigator_title>Director of Radiology</investigator_title>
  </responsible_party>
  <keyword>breast cancer</keyword>
  <keyword>Pathology</keyword>
  <keyword>Molecular Typing</keyword>
  <keyword>magnetic resonance</keyword>
  <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>Undecided</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/NCT04461990
org study id: BCIG
nct id: NCT04461990
lead sponsor: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
1. Identify the imaging features of breast cancer with different molecular types

        2. Reveal the association between hormone receptor positive/HER2 negative breast cancer and
           imaging histology, Oncotype Dx recurrence score

        3. Combine genomics and imaging to establish a predictive model for the sensitivity of
           HER2-positive breast cancer targeted therapy

        4. Establish an imaging genomics prediction model for triple-negative breast cancer
           molecular subtypes, and clarify the imaging genomics characteristics of the therapeutic
           targets of each subtype
Research design

        1. Research on the molecular typing of breast cancer based on imaging features

        2. Establish a Luminal breast cancer recurrence risk prediction model

        3. Establish HER2 targeted therapy sensitivity prediction model

        4. Establish TNBC molecular subtype prediction model Research methods Research Object This
           study used a multi-center study to prospectively enroll breast cancer patients diagnosed
           with pathology. All enrolled patients had complete clinical data, including demographic
           characteristics (gender, age, menstrual status and fertility history), and pathological
           data (histopathological data). Staging, immunohistochemical status and FISH, genetic
           testing records the recurrence score and genotype), imaging data, complete treatment and
           follow-up (whether there is local recurrence and metastasis, and the time of diagnosis).

      Magnetic resonance examination In order to maintain the comparability between the images and
      reduce the systematic errors, each center selects a fixed MR device for scanning. Among them,
      a. Oncology Hospital chose to scan images with 3.0T (Siemens Skyra) MR equipment. A special
      breast coil is used to add high-definition diffusion-weighted scanning and multi-b value
      diffusion-weighted scanning before the dynamic enhancement scan. Dynamically enhanced
      acquisition in 5 phases with a time resolution of 65s. b. Renji Hospital uses Netherlands
      Philips Achieva 3.0 T superconductor MR scanner, 4-channel dedicated breast phased array
      coil. Scanning sequences include T1WI, T2WI, T2WI fat suppression, DWI and DCE-MRI. The
      contrast agent was Gd-DTPA, with a dose of 0.1 mmol/kg, an injection rate of 2.0 mL/s, and an
      additional 20 mL of saline was added to the tube after injection. The T1WI scan was performed
      first, and 5 time phases were continuously scanned after the injection of contrast agent, and
      each time phase was separated by 61 s, for a total of 6 time phases. c. Chinese women and
      babies are scanned with 1.5T SIEMENS AERA MR equipment and special breast coils. Scanning
      sequence includes 5 phases of T1WI, T2WI fat suppression, DWI and dynamic enhancement scan,
      time resolution 71s.

      Image processing Use software to make semi-automatic and automatic outlines of the tumor
      interest area, and make the outline of the tumor solid enhancement part, the entire tumor
      area and the surrounding edema zone in the transverse position. In order to accurately
      delineate the tumor, compare the T1 and T2 weighted and dynamically enhanced images, two
      imaging physicians are responsible, one is responsible for delineation and the other is
      reviewed, and the disputed area is determined after discussion by a third person. Create a
      dynamic enhanced tumor texture analysis program to automatically extract imaging omics
      features in the region of interest. Using a labeled data set, a computer-based automatic
      segmentation algorithm model based on machine learning is constructed to automatically
      extract regions of interest, and segmentation performance evaluation is performed on manually
      delineated labels.

      Statistical analysis Perform statistical analysis on the obtained images and clinical data,
      extract image omics features and use machine learning algorithms to screen important
      features. Use statistical tools such as SPSS and R language. Paired t test (continuous
      variable) and chi-square test (discontinuous variable) were used to compare the clinical and
      imaging characteristics of patients with different prognosis; correlation analysis was used
      to evaluate the imaging histology characteristics and different pathological tissue grades,
      Correlation between lymph node metastasis and specific gene expression; use Kaplan-Meier
      survival curve to analyze the prognostic difference between patients with different imaging
      omics characteristics, and use log-rank method to test the difference; use cox survival model
      to compare clinical characteristics and imaging omics The characteristics and prognosis of
      patients (tumor-free survival, progression-free survival, overall survival) were analyzed by
      multiple factors. Further, deep learning algorithms can be used to automatically learn
      imaging omics features that may be related to molecular subtypes and prognosis to build
      prediction models.
observational model: Cohort
time perspective: Prospective
measure: Image prediction model of different molecular typing
time frame: 30 December，2022----30 December，2023
description: Build a model to predict molecular typing based on image
Establish a prediction model for predicting the risk of Luminal breast cancer recurrence
Establish a prediction model for predicting her2 targeted drug resistance
Establishing a triple-negative molecular model for breast cancer
arm group label: Luminal
description: Luminal A：ER+ and/or PR+,HER2- Luminal B：ER+ and/or PR+,HER2+
* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2
arm group label: HER2 overexpression
description: ER- PR-,HER2+
* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2
arm group label: Triple negative
description: ER- PR-,HER2-
* ER:estrogen receptor PR:progesterone receptor HER2:human epidermalgrowth factor receptor-2
intervention type: Procedure
intervention name: Multidisciplinary cooperative comprehensive treatment
description: Local surgery, radiation therapy, and systemic therapy such as chemotherapy, endocrine and molecular targeting.
arm group label: HER2 overexpression
arm group label: Luminal
arm group label: Triple negative
study pop: 
sampling method: Probability Sample
criteria: 
gender: Female
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Gu Ya Jia
role: Principal Investigator
affiliation: Fudan University
last name: Gu Ya Jia
phone: 86-18017317817
email: guyajia@126.com
facility: 
contact: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Fudan University
investigator full name: Guyajia
investigator title: Director of Radiology
mesh term: Breast Neoplasms
sharing ipd: Undecided