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

NCT ID: NCT04452058

Last Updated: 2020-06-30

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-08-01

Study Completion Date

2022-12-30

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.

Conditions

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Predictive Cancer Model Lung Cancer Preinvasive Adenocarcinoma

Keywords

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Radiomics Early stage Pulmonary nodule Pericancerous tissue Immunotherapy

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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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.

Radiomic Algorithm

Intervention Type OTHER

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

Radiomic Algorithm

Intervention Type OTHER

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

Radiomic Algorithm

Intervention Type OTHER

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.

Radiomic Algorithm

Intervention Type OTHER

Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction

Interventions

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Radiomic Algorithm

Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction

Intervention Type OTHER

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.


* (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

* (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.
* (c) Contain other pathological components( SCLC or lymphoma) or
* (d) poor image quality.
* (e) incomplete clinical data.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Guangdong Provincial People's Hospital

OTHER

Sponsor Role collaborator

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

OTHER

Sponsor Role lead

Responsible Party

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Herui Yao

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Haiyu Zhou, PhD

Role: STUDY_CHAIR

Guangdong Provincial People's Hospital

Luyu Huang

Role: PRINCIPAL_INVESTIGATOR

Guangdong Provincial People's Hospital

Herui Yao, PhD

Role: STUDY_DIRECTOR

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Yunfang Yu

Role: STUDY_DIRECTOR

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Hanbo Cao, PhD

Role: STUDY_DIRECTOR

Zhoushan Lung Cancer Institution

Locations

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Guangdong Provincial People's Hospital

Guangzhou, Guangdong, China

Site Status RECRUITING

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Guangzhou, Guangdong, China

Site Status RECRUITING

Zhoushan Lung Cancer Institution

Zhoushan, Zhejiang, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Haiyu Zhou, PhD

Role: CONTACT

Phone: +8613710342002

Email: [email protected]

Luyu Huang

Role: CONTACT

Email: [email protected]

Facility Contacts

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Haiyu Zhou, PhD

Role: primary

Herui Yao, PhD

Role: primary

Yufang Yu

Role: backup

Hanbo Cao, PhD

Role: primary

Other Identifiers

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SYSEC-KY-KS-2019-107

Identifier Type: -

Identifier Source: org_study_id