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Construction of CT Radiomics Model for Predicting the Efficacy of Immunotherapy in Patients With Stage III NSCLC

Construction of CT Radiomics Model to Assess PD-L1 Status and Predict the Efficacy of Chemoradiotherapy Combined With Immunotherapy in Unresectable Locally Advanced Non-small Cell Lung Cancer

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04984148
Enrollment
70
Registered
2021-07-30
Start date
2019-02-01
Completion date
2024-01-31
Last updated
2021-11-09

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Stage III Non-small Cell Lung Cancer

Keywords

non-small cell lung cancer, radiomics, immunotherapy, chemoradiotherapy

Brief summary

Consolidation immunotherapy of immune checkpoint inhibitor (ICI) following chemoradiotherapy (CRT) is the current standard of care for patients with unresectable locally advanced non-small cell lung cancer (NSCLC) as it improves both progression-free survival and overall survival. However, a substantial proportion of patients still experience disease recurrence despite consolidation ICI. It is important for personalized treatment to predict the efficacy of consolidation ICI. PD-L1 expression is used as a predictive biomarker for ICI response and efficacy in advanced NSCLC, but its role in patients with stage III disease is unclear. One important reason is PD-L1 testing performed on pre-CRT tissue may not reflect changes in PD-L1 expression after CRT. CT-based radiomics approaches have been successfully applied to generate imaging biomarkers as decision support tools for clinical practice. The hypothesis of this study is that CT radiomics model can assess PD-L1 status after CRT and predict the efficacy of CRT combined with ICI in unresectable locally advanced NSCLC.

Detailed description

This is an observational longitudinal prospective study. CT scan is performed before radiotherapy, during radiotherapy and at the end of radiotherapy in patients with unresectable locally advanced NSCLC who undergo CRT. Radiomic features were extracted from CT images and baseline PD-L1 expression is assessed. CT-based radiomics models is developed to assess PD-L1 expression and predict the efficacy of ICI.

Interventions

DIAGNOSTIC_TESTCT

Contrast-enhanced thoracic computed tomography

Sponsors

Shenzhen University
CollaboratorOTHER
Guangdong Provincial People's Hospital
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Histological or cytological proven non-small cell lung cancer * Unresectable stage III according to American Joint Committee of Cancer stage (the eighth edition) * 18 years or older

Exclusion criteria

* Previous thoracic radiotherapy * Palliative treatment

Design outcomes

Primary

MeasureTime frameDescription
The association of CT radiomics features with PD-L1 expression of the tumor12 weeksTo construct a radiomics model for predicting PD-L1 expression after chemoradiotherapy

Secondary

MeasureTime frameDescription
Association between CT radiomics model and progression-free survival of chemoradiotherapy followed by ICIFrom date of inclusion to the trial until the date of first documented iRECIST progression or date of death from any cause, assessed up to 5 yearsTo construct CT radiomics model for evaluating progression-free survival in patients undergo chemoradiotherapy followed by ICI
Association between CT radiomics model and overall survival of chemoradiotherapy followed by ICIFrom date of inclusion to the trial until the date of death from any cause, assessed up to 5 yearsto assess the association between CT radiomics features and overall survival
Association between CT radiomics model and ICI related pneumonitis5 yearsTo develop CT radiomics signatures for predicting ICI related pneumonitis

Countries

China

Contacts

Primary ContactYi Pan
panyiff011@163.com+862083827812

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026