CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC

Lung Cancer Clinical Trial

— TOP-RLC  

Official title:

CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04452058
• Other study ID #: SYSEC-KY-KS-2019-107
• Status: Recruiting
• 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
• 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.

Related Conditions & MeSH terms

• Adenocarcinoma
• Adenocarcinoma in Situ
• Lung Cancer
• Predictive Cancer Model
• Preinvasive Adenocarcinoma

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