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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Predictive Cancer Model, Lung Cancer, Preinvasive Adenocarcinoma

Intervention / Treatment
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* Other: Radiomic Algorithm


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Internal cohort<br>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. | Other: Radiomic Algorithm<br>* Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction<br>|
| External cohort 1<br>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 | Other: Radiomic Algorithm<br>* Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction<br>|
| External cohort 2<br>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 | Other: Radiomic Algorithm<br>* Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction<br>|
| Immune Cohort<br>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. | Other: Radiomic Algorithm<br>* Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Pathological subtype | Pathological type of pulmonary nodules | 5 years | 
| Objective Response Rate (ORR) | Rate of ORR in all subjects for the patients who receiving immunotherapy | 5 years | 
| Progression-free survival (PFS) | 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) | From enrollment to death (for any reason) in immunotherapy cohort | 5 years | 
| Clinical Benefit Rate (CBR) | Rate of CBR greater than or equal to 24 weeks in all subjects | 5 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Radiomics, Early stage, Pulmonary nodule, Pericancerous tissue, Immunotherapy