<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/NCT04452058</url>
  </required_header>
  <id_info>
    <org_study_id>SYSEC-KY-KS-2019-107</org_study_id>
    <nct_id>NCT04452058</nct_id>
  </id_info>
  <brief_title>CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC</brief_title>
  <acronym>TOP-RLC</acronym>
  <official_title>CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Guangdong Provincial People's Hospital</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen 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>
      The purpose of this study was to investigate whether the combined radiomic model based on&#xD;
      radiomic features extracted from focus and perifocal area (5mm) can effectively improve&#xD;
      prediction performance of distinguishing precancerous lesions from early-stage lung&#xD;
      adenocarcinoma, which could assist clinical decision making for surgery indication. Besides,&#xD;
      response and long term clinical benefit of immunotherapy of advanced NSCLC lung cancer&#xD;
      patients could also be predicted by this strategy.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Early detection and diagnosis of pulmonary nodules is clinically significant regarding&#xD;
      optimal treatment selection and avoidance of unnecessary surgical procedures. Deferential&#xD;
      pathology results causes widely different prognosis after standard surgery among pulmonary&#xD;
      precancerous lesion, atypical adenomatous hyperplasia (AAH) as well as adenocarcinoma in situ&#xD;
      (AIS), and early stage invasive adenocarcinoma (IAC). The micro-invasion of pulmonary&#xD;
      perifocal interstitium is difficult to identify from AIS unless pathology immunohistochemical&#xD;
      study was implemented after operation，which may causes prolonged procedure time and&#xD;
      inappropriate surgical decision-making. Key feature-derived variables screened from CT scans&#xD;
      via statistics and machine learning algorithms, could form a radiomics signature for disease&#xD;
      diagnosis, tumor staging, therapy response adn patient prognosis. The purpose of this study&#xD;
      was to investigate whether the combined radiomic signature based on the focal and&#xD;
      perifocal（5mm）radiomic features can effectively improve predictive performance of&#xD;
      distinguishing precancerous lesions from early stage lung adenocarcinoma. Besides,&#xD;
      immunotherapy response is various among patients and no more than 20% of patients could&#xD;
      benefit from it. None reliable biomarker has been found yet expect Programmed death-ligand 1&#xD;
      (PD-L1) expression, the only approved biomarker for immunotherapy. However recent reports&#xD;
      suggested that patients could benefit from immunotherapy regardless of PD-L1 positive or&#xD;
      negative. On the contrast, radiomics has show it advantages of non-invasiveness,&#xD;
      easy-acquired and no limitation of sampling. Therefore, we applied this strategy in&#xD;
      prediction for the immunotherapy response of advanced NSCLC lung cancer patients receiving&#xD;
      immune checkpoint inhibitors (ICIs), which would prevent some non-benefit patient from the&#xD;
      adverse effect of ICIs.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">August 1, 2019</start_date>
  <completion_date type="Anticipated">December 30, 2022</completion_date>
  <primary_completion_date type="Anticipated">December 1, 2021</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>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Pathological subtype</measure>
    <time_frame>5 years</time_frame>
    <description>Pathological type of pulmonary nodules</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Objective Response Rate (ORR)</measure>
    <time_frame>5 years</time_frame>
    <description>Rate of ORR in all subjects for the patients who receiving immunotherapy</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Progression-free survival (PFS)</measure>
    <time_frame>5 years</time_frame>
    <description>From enrollment to progression or death (for any reason) in immunotherapy cohort</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Overall survival (OS)</measure>
    <time_frame>5 years</time_frame>
    <description>From enrollment to death (for any reason) in immunotherapy cohort</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Clinical Benefit Rate (CBR)</measure>
    <time_frame>5 years</time_frame>
    <description>Rate of CBR greater than or equal to 24 weeks in all subjects</description>
  </secondary_outcome>
  <number_of_groups>4</number_of_groups>
  <enrollment type="Anticipated">500</enrollment>
  <condition>Predictive Cancer Model</condition>
  <condition>Lung Cancer</condition>
  <condition>Preinvasive Adenocarcinoma</condition>
  <arm_group>
    <arm_group_label>Internal cohort</arm_group_label>
    <description>The internal cohort was retrospective enrolled in Guangdong Provincial People's hospital from March 1, 2015 to December 31,2019. Patients with single pulmonary lesion underwent preoperative chest CT scan and histologically confirmed precancerous lesions or early stage lung adenocarcinoma after thoracic surgery was included.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>External cohort 1</arm_group_label>
    <description>The same inclusion/exclusion criteria were applied for another independent centers, Sun Yat-sen Memorial Hospital ,Guangdong Province, China, forming an external validation cohort of 73 patients</description>
  </arm_group>
  <arm_group>
    <arm_group_label>External cohort 2</arm_group_label>
    <description>The same inclusion/exclusion criteria were applied for another independent centers, Zhoushan Lung Cancer Institution, Zhejiang Province, China, forming second external validation cohort of 30 patients</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Immune Cohort</arm_group_label>
    <description>The internal cohort was retrospective enrolled in Guangdong Provincial People's hospital from March 1, 2015 to May 31,2020. Patients with advanced lung cancer underwent preoperative chest CT scan and histologically confirmed NSCLC before receiving immunotherapy was included.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Radiomic Algorithm</intervention_name>
    <description>Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction</description>
    <arm_group_label>External cohort 1</arm_group_label>
    <arm_group_label>External cohort 2</arm_group_label>
    <arm_group_label>Immune Cohort</arm_group_label>
    <arm_group_label>Internal cohort</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients in Guangdong Provincial People's hospital from March 1, 2015 to May 31,2022.&#xD;
&#xD;
        Patients from Sun Yat-sen Memorial Hospital ,Guangdong Province, China ; Zhoushan Lung&#xD;
        Cancer Institution,Zhejiang Province,China during 2019.01-2022.3&#xD;
&#xD;
        All Patients should be histologically confirmed NSCLC and those have preoperative chest CT&#xD;
        scan.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  (a) that were pathologically confirmed as precancerous lesions or Stage I lung&#xD;
             adenocarcinoma （≤3cm）&#xD;
&#xD;
          -  (b) standard Chest CT scans with or without contrast enhancement performed &lt;3 months&#xD;
             before surgery；&#xD;
&#xD;
          -  (c) availability of clinical characteristics.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  (a) preoperative therapy (neoadjuvant chemotherapy or radiotherapy) performed,&#xD;
&#xD;
          -  (b) suffering from other tumor disease before or at the same time.&#xD;
&#xD;
          -  (c) Contain other pathological components such as squamous cell lung carcinoma (SCC)&#xD;
             or small cell lung carcinoma (SCLC) or&#xD;
&#xD;
          -  (d) poor image quality.&#xD;
&#xD;
        Inclusion Criteria of immunotherapy cohort:&#xD;
&#xD;
          -  (a) that were diagnosed as advanced NSCLC&#xD;
&#xD;
          -  (b) Both standard Chest CT scans with contrast enhancement performed &lt;3 months before&#xD;
             and after first dose of immunotherapy are available；&#xD;
&#xD;
          -  (c) availability of clinical characteristics.&#xD;
&#xD;
        Exclusion Criteria of immunotherapy cohort:&#xD;
&#xD;
          -  (a) Ever receiving pulmonary operation on the same side of the lesion.&#xD;
&#xD;
          -  (b) suffering from other tumor disease before or at the same time.&#xD;
&#xD;
          -  (c) Contain other pathological components( SCLC or lymphoma) or&#xD;
&#xD;
          -  (d) poor image quality.&#xD;
&#xD;
          -  (e) incomplete clinical data.&#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>Haiyu Zhou, PhD</last_name>
    <role>Study Chair</role>
    <affiliation>Guangdong Provincial People's Hospital</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Luyu Huang</last_name>
    <role>Principal Investigator</role>
    <affiliation>Guangdong Provincial People's Hospital</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Herui Yao, PhD</last_name>
    <role>Study Director</role>
    <affiliation>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Yunfang Yu</last_name>
    <role>Study Director</role>
    <affiliation>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Hanbo Cao, PhD</last_name>
    <role>Study Director</role>
    <affiliation>Zhoushan Lung Cancer Institution</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Haiyu Zhou, PhD</last_name>
    <phone>+8613710342002</phone>
    <email>lungcancer@163.com</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Luyu Huang</last_name>
    <email>13lyhuang1@gmail.com</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Guangdong Provincial People's Hospital</name>
      <address>
        <city>Guangzhou</city>
        <state>Guangdong</state>
        <zip>510000</zip>
        <country>China</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Haiyu Zhou, PhD</last_name>
      <phone>+8613710342002</phone>
      <email>lungcancer@163.com</email>
    </contact>
  </location>
  <location>
    <facility>
      <name>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</name>
      <address>
        <city>Guangzhou</city>
        <state>Guangdong</state>
        <zip>510000</zip>
        <country>China</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Herui Yao, PhD</last_name>
      <phone>+8613500018020</phone>
      <email>yaoherui@mail.sysu.edu.cn</email>
    </contact>
    <contact_backup>
      <last_name>Yufang Yu</last_name>
      <phone>+8613660238987</phone>
      <email>yuyf9@mail.sysu.edu.cn</email>
    </contact_backup>
  </location>
  <location>
    <facility>
      <name>Zhoushan Lung Cancer Institution</name>
      <address>
        <city>Zhoushan</city>
        <state>Zhejiang</state>
        <zip>316000</zip>
        <country>China</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Hanbo Cao, PhD</last_name>
      <phone>13567690608</phone>
    </contact>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>June 2020</verification_date>
  <study_first_submitted>June 25, 2020</study_first_submitted>
  <study_first_submitted_qc>June 29, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 30, 2020</study_first_posted>
  <last_update_submitted>June 29, 2020</last_update_submitted>
  <last_update_submitted_qc>June 29, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 30, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</investigator_affiliation>
    <investigator_full_name>Herui Yao</investigator_full_name>
    <investigator_title>Principal Investigator</investigator_title>
  </responsible_party>
  <keyword>Radiomics</keyword>
  <keyword>Early stage</keyword>
  <keyword>Pulmonary nodule</keyword>
  <keyword>Pericancerous tissue</keyword>
  <keyword>Immunotherapy</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Adenocarcinoma in Situ</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>The datasets used or analysed during the current study are available from the corresponding author on reasonable request.</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/NCT04452058
org study id: SYSEC-KY-KS-2019-107
nct id: NCT04452058
lead sponsor: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
The purpose of this study was to investigate whether the combined radiomic model based on
      radiomic features extracted from focus and perifocal area (5mm) can effectively improve
      prediction performance of distinguishing precancerous lesions from early-stage lung
      adenocarcinoma, which could assist clinical decision making for surgery indication. Besides,
      response and long term clinical benefit of immunotherapy of advanced NSCLC lung cancer
      patients could also be predicted by this strategy.
Early detection and diagnosis of pulmonary nodules is clinically significant regarding
      optimal treatment selection and avoidance of unnecessary surgical procedures. Deferential
      pathology results causes widely different prognosis after standard surgery among pulmonary
      precancerous lesion, atypical adenomatous hyperplasia (AAH) as well as adenocarcinoma in situ
      (AIS), and early stage invasive adenocarcinoma (IAC). The micro-invasion of pulmonary
      perifocal interstitium is difficult to identify from AIS unless pathology immunohistochemical
      study was implemented after operation，which may causes prolonged procedure time and
      inappropriate surgical decision-making. Key feature-derived variables screened from CT scans
      via statistics and machine learning algorithms, could form a radiomics signature for disease
      diagnosis, tumor staging, therapy response adn patient prognosis. The purpose of this study
      was to investigate whether the combined radiomic signature based on the focal and
      perifocal（5mm）radiomic features can effectively improve predictive performance of
      distinguishing precancerous lesions from early stage lung adenocarcinoma. Besides,
      immunotherapy response is various among patients and no more than 20% of patients could
      benefit from it. None reliable biomarker has been found yet expect Programmed death-ligand 1
      (PD-L1) expression, the only approved biomarker for immunotherapy. However recent reports
      suggested that patients could benefit from immunotherapy regardless of PD-L1 positive or
      negative. On the contrast, radiomics has show it advantages of non-invasiveness,
      easy-acquired and no limitation of sampling. Therefore, we applied this strategy in
      prediction for the immunotherapy response of advanced NSCLC lung cancer patients receiving
      immune checkpoint inhibitors (ICIs), which would prevent some non-benefit patient from the
      adverse effect of ICIs.
observational model: Cohort
time perspective: Retrospective
measure: Pathological subtype
time frame: 5 years
description: Pathological type of pulmonary nodules
measure: Objective Response Rate (ORR)
time frame: 5 years
description: Rate of ORR in all subjects for the patients who receiving immunotherapy
measure: Progression-free survival (PFS)
time frame: 5 years
description: From enrollment to progression or death (for any reason) in immunotherapy cohort
measure: Overall survival (OS)
time frame: 5 years
description: From enrollment to death (for any reason) in immunotherapy cohort
measure: Clinical Benefit Rate (CBR)
time frame: 5 years
description: Rate of CBR greater than or equal to 24 weeks in all subjects
arm group label: Internal cohort
description: The internal cohort was retrospective enrolled in Guangdong Provincial People's hospital from March 1, 2015 to December 31,2019. Patients with single pulmonary lesion underwent preoperative chest CT scan and histologically confirmed precancerous lesions or early stage lung adenocarcinoma after thoracic surgery was included.
arm group label: External cohort 1
description: The same inclusion/exclusion criteria were applied for another independent centers, Sun Yat-sen Memorial Hospital ,Guangdong Province, China, forming an external validation cohort of 73 patients
arm group label: External cohort 2
description: The same inclusion/exclusion criteria were applied for another independent centers, Zhoushan Lung Cancer Institution, Zhejiang Province, China, forming second external validation cohort of 30 patients
arm group label: Immune Cohort
description: The internal cohort was retrospective enrolled in Guangdong Provincial People's hospital from March 1, 2015 to May 31,2020. Patients with advanced lung cancer underwent preoperative chest CT scan and histologically confirmed NSCLC before receiving immunotherapy was included.
intervention type: Other
intervention name: Radiomic Algorithm
description: Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction
arm group label: External cohort 1
arm group label: External cohort 2
arm group label: Immune Cohort
arm group label: Internal cohort
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Haiyu Zhou, PhD
role: Study Chair
affiliation: Guangdong Provincial People's Hospital
last name: Luyu Huang
role: Principal Investigator
affiliation: Guangdong Provincial People's Hospital
last name: Herui Yao, PhD
role: Study Director
affiliation: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
last name: Yunfang Yu
role: Study Director
affiliation: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
last name: Hanbo Cao, PhD
role: Study Director
affiliation: Zhoushan Lung Cancer Institution
last name: Haiyu Zhou, PhD
phone: +8613710342002
email: lungcancer@163.com
last name: Luyu Huang
email: 13lyhuang1@gmail.com
facility: 
status: Recruiting
contact: 
facility: 
status: Recruiting
contact: 
contact backup: 
facility: 
status: Recruiting
contact: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
investigator full name: Herui Yao
investigator title: Principal Investigator
mesh term: Adenocarcinoma in Situ
sharing ipd: No
ipd description: The datasets used or analysed during the current study are available from the corresponding author on reasonable request.