CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC (TOP-RLC)

June 29, 2020 updated by: Herui Yao, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
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.

Study Overview

Detailed Description

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.

Study Type

Observational

Enrollment (Anticipated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

    • Guangdong
      • Guangzhou, Guangdong, China, 510000
        • Recruiting
        • Guangdong Provincial People's Hospital
        • Contact:
      • Guangzhou, Guangdong, China, 510000
    • Zhejiang
      • Zhoushan, Zhejiang, China, 316000
        • Recruiting
        • Zhoushan Lung Cancer Institution
        • Contact:
          • Hanbo Cao, PhD
          • Phone Number: 13567690608

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients in Guangdong Provincial People's hospital from March 1, 2015 to May 31,2022.

Patients from Sun Yat-sen Memorial Hospital ,Guangdong Province, China ; Zhoushan Lung Cancer Institution,Zhejiang Province,China during 2019.01-2022.3

All Patients should be histologically confirmed NSCLC and those have preoperative chest CT scan.

Description

Inclusion Criteria:

  • (a) that were pathologically confirmed as precancerous lesions or Stage I lung adenocarcinoma (≤3cm)
  • (b) standard Chest CT scans with or without contrast enhancement performed <3 months before surgery;
  • (c) availability of clinical characteristics.

Exclusion Criteria:

  • (a) preoperative therapy (neoadjuvant chemotherapy or radiotherapy) performed,
  • (b) suffering from other tumor disease before or at the same time.
  • (c) Contain other pathological components such as squamous cell lung carcinoma (SCC) or small cell lung carcinoma (SCLC) or
  • (d) poor image quality.

Inclusion Criteria of immunotherapy cohort:

  • (a) that were diagnosed as advanced NSCLC
  • (b) Both standard Chest CT scans with contrast enhancement performed <3 months before and after first dose of immunotherapy are available;
  • (c) availability of clinical characteristics.

Exclusion Criteria of immunotherapy cohort:

  • (a) Ever receiving pulmonary operation on the same side of the lesion.
  • (b) suffering from other tumor disease before or at the same time.
  • (c) Contain other pathological components( SCLC or lymphoma) or
  • (d) poor image quality.
  • (e) incomplete clinical data.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Internal cohort
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.
Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction
External cohort 1
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
Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction
External cohort 2
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
Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction
Immune Cohort
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.
Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Pathological subtype
Time Frame: 5 years
Pathological type of pulmonary nodules
5 years
Objective Response Rate (ORR)
Time Frame: 5 years
Rate of ORR in all subjects for the patients who receiving immunotherapy
5 years
Progression-free survival (PFS)
Time Frame: 5 years
From enrollment to progression or death (for any reason) in immunotherapy cohort
5 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Overall survival (OS)
Time Frame: 5 years
From enrollment to death (for any reason) in immunotherapy cohort
5 years
Clinical Benefit Rate (CBR)
Time Frame: 5 years
Rate of CBR greater than or equal to 24 weeks in all subjects
5 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Investigators

  • Study Chair: Haiyu Zhou, PhD, Guangdong Provincial People's Hospital
  • Principal Investigator: Luyu Huang, Guangdong Provincial People's Hospital
  • Study Director: Herui Yao, PhD, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
  • Study Director: Yunfang Yu, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
  • Study Director: Hanbo Cao, PhD, Zhoushan Lung Cancer Institution

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2019

Primary Completion (Anticipated)

December 1, 2021

Study Completion (Anticipated)

December 30, 2022

Study Registration Dates

First Submitted

June 25, 2020

First Submitted That Met QC Criteria

June 29, 2020

First Posted (Actual)

June 30, 2020

Study Record Updates

Last Update Posted (Actual)

June 30, 2020

Last Update Submitted That Met QC Criteria

June 29, 2020

Last Verified

June 1, 2020

More Information

Terms related to this study

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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