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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04452058
Other study ID # SYSEC-KY-KS-2019-107
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date August 1, 2019
Est. completion date December 30, 2022

Study information

Verified date June 2020
Source Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Contact Haiyu Zhou, PhD
Phone +8613710342002
Email lungcancer@163.com
Is FDA regulated No
Health authority
Study type Observational

Clinical 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 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.


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.


Recruitment information / eligibility

Status Recruiting
Enrollment 500
Est. completion date December 30, 2022
Est. primary completion date December 1, 2021
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility 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 Design


Intervention

Other:
Radiomic Algorithm
Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction

Locations

Country Name City State
China Guangdong Provincial People's Hospital Guangzhou Guangdong
China Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University Guangzhou Guangdong
China Zhoushan Lung Cancer Institution Zhoushan Zhejiang

Sponsors (2)

Lead Sponsor Collaborator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University Guangdong Provincial People's Hospital

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Pathological subtype Pathological type of pulmonary nodules 5 years
Primary Objective Response Rate (ORR) Rate of ORR in all subjects for the patients who receiving immunotherapy 5 years
Primary Progression-free survival (PFS) From enrollment to progression or death (for any reason) in immunotherapy cohort 5 years
Secondary Overall survival (OS) From enrollment to death (for any reason) in immunotherapy cohort 5 years
Secondary Clinical Benefit Rate (CBR) Rate of CBR greater than or equal to 24 weeks in all subjects 5 years
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