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/NCT06035250</url>
    </required_header>
    <id_info>
      <org_study_id>CASMI004</org_study_id>
      <nct_id>NCT06035250</nct_id>
    </id_info>
    <brief_title>AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy</brief_title>
    <official_title>Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Chinese Academy of Sciences</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Peking University Cancer Hospital &amp; Institute</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Cancer Institute and Hospital, Chinese Academy of Medical Sciences</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Yunnan Cancer Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Henan Cancer Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Zhenjiang First People's Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>First Hospital of China Medical University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Cancer Hospital of Guangxi Medical University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Peking University People's Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Tianjin Medical University Cancer Institute and Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>The First Affiliated Hospital of Zhengzhou University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Nanfang Hospital, Southern Medical University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>The Affiliated Hospital of Qingdao University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Ruijin Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Sixth Affiliated Hospital, Sun Yat-sen University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Peking Union Medical College Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Xiangya Hospital of Central South University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Affiliated Cancer Hospital &amp; Institute of Guangzhou Medical University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>The First Affiliated Hospital of Soochow University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>First Affiliated Hospital, Sun Yat-Sen University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Fujian Medical University Union Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Fujian Cancer Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>San Raffaele University Hospital, Italy</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Chinese Academy of Sciences</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>
        This study seeks to develop a deep-learning-based intelligent predictive model for the&#xD;
        efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients'&#xD;
        CT imaging data, biopsy pathology images, and clinical information, the intelligent model&#xD;
        will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in&#xD;
        personalized treatment decisions for gastric cancer patients.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        This study seeks to develop a deep learning model to predict the outcomes of neoadjuvant&#xD;
        chemotherapy in patients with gastric cancer. Leveraging participants' CT scans, biopsy&#xD;
        pathology images, and clinical profiles, this model aims to forecast the effectiveness of&#xD;
        post-neoadjuvant chemotherapy and the subsequent prognosis, thereby aiding in individualized&#xD;
        treatment choices for these participants.&#xD;
  &#xD;
        Data Collection: The investigators will gather data from 1,800 retrospective cases and 200&#xD;
        prospective cases from multiple hospitals. The retrospective data will be divided into&#xD;
        training and testing sets to train and validate the model, respectively. The model's&#xD;
        performance will subsequently be evaluated using the prospective dataset.&#xD;
  &#xD;
        Clinical Information: This encompasses the participant's gender, age, tumor markers, staging,&#xD;
        type, specific treatment plans, pre and post-treatment lab results, etc.&#xD;
  &#xD;
        Imaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy,&#xD;
        with at least the venous phase CT imaging included.&#xD;
  &#xD;
        Pathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and&#xD;
        Eosin (HE) taken within one month prior to treatment.&#xD;
  &#xD;
        TRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading&#xD;
        system.&#xD;
  &#xD;
        Prognostic Endpoints: The recorded endpoints are a 3-year progression-free survival (PFS) and&#xD;
        a 5-year overall survival (OS). All deaths due to non-disease factors are excluded from the&#xD;
        prognosis analysis.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 10, 2023</start_date>
    <completion_date type="Anticipated">December 31, 2029</completion_date>
    <primary_completion_date type="Anticipated">August 31, 2024</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>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model</measure>
      <time_frame>two months</time_frame>
      <description>The AUC will be used to evaluate the performance of the AI model in predicting TRG grading of gastric cancer patients after neoadjuvant chemotherapy. An AUC of 1 indicates perfect prediction, while an AUC of 0.5 indicates prediction no better than chance.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Accuracy of TRG prediction by the AI model</measure>
      <time_frame>two months</time_frame>
      <description>Accuracy measures the proportion of true positive and true negative predictions made by the AI model among all predictions. It indicates the capability of the model to correctly classify patients into their respective TRG gradings.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Progression-Free Survival (PFS) at 3 years</measure>
      <time_frame>Three years</time_frame>
      <description>The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Overall Survival (OS) at 5 years</measure>
      <time_frame>Five years</time_frame>
      <description>The duration from the date of patient confirmation to the date of death of the patient.</description>
    </secondary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">200</enrollment>
    <condition>Gastric Cancer</condition>
    <condition>Image</condition>
    <condition>Pathology</condition>
    <arm_group>
      <arm_group_label>Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy</arm_group_label>
      <description>This group comprises participants diagnosed with advanced gastric cancer. The participants will be treated with standard neoadjuvant chemotherapy regimens recommended by clinical guidelines. Treatment details, including the generic name of the drugs, dosage form, dosage, frequency, and duration, will be recorded according to the specific regimen.</description>
    </arm_group>
    <intervention>
      <intervention_type>Drug</intervention_type>
      <intervention_name>Neoadjuvant Chemotherapy</intervention_name>
      <description>Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.</description>
      <arm_group_label>Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy</arm_group_label>
    </intervention>
    <biospec_retention>Samples With DNA</biospec_retention>
    <biospec_descr>
      <textblock>
        The biospecimens consist of gastric tumor biopsy samples, collected from each patient prior&#xD;
        to the initiation of neoadjuvant chemotherapy. These specimens undergo HE (Hematoxylin and&#xD;
        Eosin) staining for pathology imaging.&#xD;
      </textblock>
    </biospec_descr>
    <eligibility>
      <study_pop>
        <textblock>
          The study population comprises gastric cancer patients from various hospitals. Participants&#xD;
          are individuals diagnosed with advanced gastric cancer and are currently undergoing&#xD;
          neoadjuvant chemotherapy treatments. Selection is based on criteria such as age, specific&#xD;
          diagnosis, past treatment history, and the clarity of their medical images and pathology&#xD;
          images.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Age 18 years or older;&#xD;
  &#xD;
            -  Pathologically diagnosed with advanced gastric cancer in accordance with the American&#xD;
               AJCC's TNM staging standards;&#xD;
  &#xD;
            -  Have not undergone any systematic anti-cancer treatments before neoadjuvant&#xD;
               chemotherapy and have not had surgery for local progression or distant metastasis;&#xD;
  &#xD;
            -  Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines,&#xD;
               and have documented treatment details;&#xD;
  &#xD;
            -  CT imaging and biopsy pathology images strictly taken within one month prior to&#xD;
               starting neoadjuvant treatment;&#xD;
  &#xD;
            -  Patients possess comprehensive preoperative clinical information and post-operative&#xD;
               TRG grading.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients whose CT or pathology images are unclear, making lesion assessment&#xD;
               infeasible;&#xD;
  &#xD;
            -  Patients diagnosed with other concurrent tumors.&#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>Yali Zang, Ph.D.</last_name>
      <role>Study Director</role>
      <affiliation>Institute of Automation, Chinese Academy of Sciences</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Di Dong, Ph.D.</last_name>
      <phone>+86 13811833760</phone>
      <email>di.dong@ia.ac.cn</email>
    </overall_contact>
    <location>
      <facility>
        <name>Cancer Institute and Hospital, Chinese Academy of Medical Sciences</name>
        <address>
          <city>Beijing</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Xinming Zhao</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Peking Union Medical College Hospital</name>
        <address>
          <city>Beijing</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Zhenyu Jin</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Peking University Cancer Hospital &amp; Institute</name>
        <address>
          <city>Beijing</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Lei Tang</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Peking University People's Hospital</name>
        <address>
          <city>Beijing</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Yi Wang</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Xiangya Hospital of Central South University</name>
        <address>
          <city>Changsha</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Weihua Liao</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Fujian Cancer Hospital</name>
        <address>
          <city>Fuzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Yangming Li</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Fujian Medical University Union Hospital</name>
        <address>
          <city>Fuzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Changming Huang</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Affiliated Cancer Hospital &amp; Institute of Guangzhou Medical University</name>
        <address>
          <city>Guangzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Shuzhong Cui</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>First Affiliated Hospital, Sun Yat-Sen University</name>
        <address>
          <city>Guangzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Shenping Yu</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Nanfang Hospital of Southern Medical University</name>
        <address>
          <city>Guangzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Guoxin Li</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Sixth Affiliated Hospital, Sun Yat-sen University</name>
        <address>
          <city>Guangzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Xiaochun Meng</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Yunnan Cancer Hospital</name>
        <address>
          <city>Kunming</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Zhenhui Li</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Cancer Hospital of Guangxi Medical University</name>
        <address>
          <city>Nanning</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Guanqiao Jin</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>The Affiliated Hospital of Qingdao University</name>
        <address>
          <city>Qingdao</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Hexiang Wang</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Ruijin Hospital</name>
        <address>
          <city>Shanghai</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Jun Zhang</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>First Hospital of China Medical University</name>
        <address>
          <city>Shenyang</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Zhenning Wang</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>The First Affiliated Hospital of Soochow University</name>
        <address>
          <city>Suzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Jie Bao</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Tianjin Medical University Cancer Institute and Hospital</name>
        <address>
          <city>Tianjin</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Zhaoxiang Ye</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Henan Cancer Hospital</name>
        <address>
          <city>Zhengzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Jing Li</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>The First Affiliated Hospital of Zhengzhou University</name>
        <address>
          <city>Zhengzhou</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Jianbo Gao</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Zhenjiang First People's Hospital</name>
        <address>
          <city>Zhenjiang</city>
          <country>China</country>
        </address>
      </facility>
      <contact>
        <last_name>Xiuhong Shan</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>San Raffaele University Hospital, Italy</name>
        <address>
          <city>Milan</city>
          <country>Italy</country>
        </address>
      </facility>
      <contact>
        <last_name>Francesco De Cobelli</last_name>
      </contact>
    </location>
    <location_countries>
      <country>China</country>
      <country>Italy</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 13, 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>Institute of Automation, Chinese Academy of Sciences</investigator_affiliation>
      <investigator_full_name>Di Dong</investigator_full_name>
      <investigator_title>Professor</investigator_title>
    </responsible_party>
    <keyword>Gastric Cancer</keyword>
    <keyword>Neoadjuvant Chemotherapy</keyword>
    <keyword>Radiomics</keyword>
    <keyword>Treatment Outcome Prediction</keyword>
    <keyword>Pathomics</keyword>
    <keyword>Radiopathomics</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Stomach Neoplasms</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Yes</sharing_ipd>
      <ipd_description>Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.</ipd_description>
      <ipd_info_type>Study Protocol</ipd_info_type>
      <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
      <ipd_info_type>Analytic Code</ipd_info_type>
      <ipd_time_frame>Data will become available 1 year after study completion and will remain available for a period of 5 years.</ipd_time_frame>
      <ipd_access_criteria>Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.</ipd_access_criteria>
      <ipd_url>http://www.radiomics.net.cn/</ipd_url>
    </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/NCT06035250
  org study id: CASMI004
  nct id: NCT06035250
  lead sponsor: 
  collaborator: 
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  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  This study seeks to develop a deep-learning-based intelligent predictive model for the
        efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients'
        CT imaging data, biopsy pathology images, and clinical information, the intelligent model
        will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in
        personalized treatment decisions for gastric cancer patients.
  This study seeks to develop a deep learning model to predict the outcomes of neoadjuvant
        chemotherapy in patients with gastric cancer. Leveraging participants' CT scans, biopsy
        pathology images, and clinical profiles, this model aims to forecast the effectiveness of
        post-neoadjuvant chemotherapy and the subsequent prognosis, thereby aiding in individualized
        treatment choices for these participants.
  
        Data Collection: The investigators will gather data from 1,800 retrospective cases and 200
        prospective cases from multiple hospitals. The retrospective data will be divided into
        training and testing sets to train and validate the model, respectively. The model's
        performance will subsequently be evaluated using the prospective dataset.
  
        Clinical Information: This encompasses the participant's gender, age, tumor markers, staging,
        type, specific treatment plans, pre and post-treatment lab results, etc.
  
        Imaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy,
        with at least the venous phase CT imaging included.
  
        Pathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and
        Eosin (HE) taken within one month prior to treatment.
  
        TRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading
        system.
  
        Prognostic Endpoints: The recorded endpoints are a 3-year progression-free survival (PFS) and
        a 5-year overall survival (OS). All deaths due to non-disease factors are excluded from the
        prognosis analysis.
  observational model: Cohort
  time perspective: Prospective
  measure: Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model
  time frame: two months
  description: The AUC will be used to evaluate the performance of the AI model in predicting TRG grading of gastric cancer patients after neoadjuvant chemotherapy. An AUC of 1 indicates perfect prediction, while an AUC of 0.5 indicates prediction no better than chance.
  measure: Accuracy of TRG prediction by the AI model
  time frame: two months
  description: Accuracy measures the proportion of true positive and true negative predictions made by the AI model among all predictions. It indicates the capability of the model to correctly classify patients into their respective TRG gradings.
  measure: Progression-Free Survival (PFS) at 3 years
  time frame: Three years
  description: The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first.
  measure: Overall Survival (OS) at 5 years
  time frame: Five years
  description: The duration from the date of patient confirmation to the date of death of the patient.
  arm group label: Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy
  description: This group comprises participants diagnosed with advanced gastric cancer. The participants will be treated with standard neoadjuvant chemotherapy regimens recommended by clinical guidelines. Treatment details, including the generic name of the drugs, dosage form, dosage, frequency, and duration, will be recorded according to the specific regimen.
  intervention type: Drug
  intervention name: Neoadjuvant Chemotherapy
  description: Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.
  arm group label: Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy
  The biospecimens consist of gastric tumor biopsy samples, collected from each patient prior
        to the initiation of neoadjuvant chemotherapy. These specimens undergo HE (Hematoxylin and
        Eosin) staining for pathology imaging.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Yali Zang, Ph.D.
  role: Study Director
  affiliation: Institute of Automation, Chinese Academy of Sciences
  last name: Di Dong, Ph.D.
  phone: +86 13811833760
  email: di.dong@ia.ac.cn
  facility: 
  contact: 
  facility: 
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  country: China
  country: Italy
  responsible party type: Principal Investigator
  investigator affiliation: Institute of Automation, Chinese Academy of Sciences
  investigator full name: Di Dong
  investigator title: Professor
  mesh term: Stomach Neoplasms
  sharing ipd: Yes
  ipd description: Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data.
  ipd info type: Study Protocol
  ipd info type: Statistical Analysis Plan (SAP)
  ipd info type: Analytic Code
  ipd time frame: Data will become available 1 year after study completion and will remain available for a period of 5 years.
  ipd access criteria: Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access.
  ipd url: http://www.radiomics.net.cn/