Predictive Cancer ModelTOP-RLCNCT04452058
CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC
An observational study of Predictive Cancer Model and Lung Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 3 sites in China.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Predictive Cancer Model, Lung Cancer, Preinvasive Adenocarcinoma
- What is being tested?
- Radiomic Algorithm
- How many people
- 500 participants
- Where
- 3 sitesChina
- Study dates
- 1 Aug 2019to 30 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- (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.
- (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.
Who cannot join
- (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.
- (a) Ever receiving pulmonary operation on the same side of the lesion.
- (b) suffering from other tumor disease before or at the same time.
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- (c) Contain other pathological components( SCLC or lymphoma) or
- (d) poor image quality.
- (e) incomplete clinical data.
Trial locations and contacts
3 sites in 1 country.
Luyu Huang contact
China3 sites, 3 recruiting
- Recruiting
Guangdong Provincial People's Hospital
Guangzhou, Guangdong, 510000
Haiyu Zhou, PhD, +8613710342002, lungcancer@163.com
- Recruiting
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Guangzhou, Guangdong, 510000
Herui Yao, PhD, +8613500018020, yaoherui@mail.sysu.edu.cn
Yufang Yu, +8613660238987, yuyf9@mail.sysu.edu.cn
- Recruiting
Zhoushan Lung Cancer Institution
Zhoushan, Zhejiang, 316000
Hanbo Cao, PhD, 13567690608
About this study
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.
Read the 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.
- Being tested
- Radiomic Algorithm
- Keywords
- Radiomics, Early stage, Pulmonary nodule, Pericancerous tissue, Immunotherapy
- Medical subject headings
- Lung Neoplasms, Adenocarcinoma in Situ
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Radiomic AlgorithmOther
Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Radiomic Algorithm | |
| 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 Receives: Other: Radiomic Algorithm | |
| 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 Receives: Other: Radiomic Algorithm | |
| 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. Receives: Other: Radiomic Algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pathological subtype
Pathological type of pulmonary nodules
Measured: 5 years
Objective Response Rate (ORR)
Rate of ORR in all subjects for the patients who receiving immunotherapy
Measured: 5 years
Progression-free survival (PFS)
From enrollment to progression or death (for any reason) in immunotherapy cohort
Measured: 5 years
Secondary outcomes (2)
Overall survival (OS)
From enrollment to death (for any reason) in immunotherapy cohort
Measured: 5 years
Clinical Benefit Rate (CBR)
Rate of CBR greater than or equal to 24 weeks in all subjects
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jun 2020
- Study start1 Aug 2019
- Primary completion1 Dec 2021 (estimated)
- Study completion30 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Predictive Cancer ModelStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Guangdong Provincial People's Hospital
- Responsible party
- Principal investigator, Herui Yao, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Investigators
- Haiyu Zhou, PhD, Study chair, Guangdong Provincial People's HospitalLuyu Huang, Principal investigator, Guangdong Provincial People's HospitalHerui Yao, PhD, Study director, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityYunfang Yu, Study director, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityHanbo Cao, PhD, Study director, Zhoushan Lung Cancer Institution
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04452058
- Sponsor's protocol ID
- SYSEC-KY-KS-2019-107
- Acronym
- TOP-RLC
- Status last verified
- Jun 2020
- First submitted
- 25 Jun 2020
- Last updated
- 30 Jun 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: The datasets used or analysed during the current study are available from the corresponding author on reasonable request.
Study at a glance
From the registry record, last updated by the sponsor on 30 Jun 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC
- ClinicalTrials.gov ID
- NCT04452058
- Acronym / sponsor ID
- TOP-RLC / SYSEC-KY-KS-2019-107
- Keywords
- Radiomics, Early stage, Pulmonary nodule, Pericancerous tissue, Immunotherapy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- Guangdong Provincial People's Hospital
- Study start
- 1 Aug 2019 (actual)
- Primary completion
- 1 Dec 2021 (estimated)
- Study completion
- 30 Dec 2022 (estimated)
- First posted
- 30 Jun 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Internal cohort | – | Other: Radiomic AlgorithmThe 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. |
| External cohort 1 | – | Other: Radiomic AlgorithmThe 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 |
| External cohort 2 | – | Other: Radiomic AlgorithmThe 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 |
| Immune Cohort | – | Other: Radiomic AlgorithmThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (6 listed)
- (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.
- (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；
Key exclusion criteria (9 listed)
- (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.
- (a) Ever receiving pulmonary operation on the same side of the lesion.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Radiomic AlgorithmDifferent radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction | Other | External cohort 1, External cohort 2, Immune Cohort, Internal cohort |
Primary outcome
Pathological subtype
Time frame: 5 years
Pathological type of pulmonary nodules
Objective Response Rate (ORR)
Time frame: 5 years
Rate of ORR in all subjects for the patients who receiving immunotherapy
Progression-free survival (PFS)
Time frame: 5 years
From enrollment to progression or death (for any reason) in immunotherapy cohort
2 secondary outcomes
Overall survival (OS)
Time frame: 5 years
Clinical Benefit Rate (CBR)
Time frame: 5 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jun 2020.
- Individual participant dataSharing planned: No. The datasets used or analysed during the current study are available from the corresponding author on reasonable request.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Luyu Huang contact
Haiyu Zhou, PhD study chair
Guangdong Provincial People's Hospital
Luyu Huang principal investigator
Guangdong Provincial People's Hospital
Herui Yao, PhD study director
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Predictive Cancer Model and Lung Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 3 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 30 Jun 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Haiyu Zhou, PhD (+8613710342002, lungcancer@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pathological subtype (time frame: 5 years). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Jun 2020.