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AI-guided Prognostication and Cranial Radiotherapy Optimization in EGFR-TKI-treated Non-small Cell Lung Cancer Patients With Baseline Brain Metastases
Sponsor: Fudan University
Summary
The goal of this observational study is to extract the imaging features of brain lesions and primary lung lesions in NSCLC patients with brain metastases by deep learning, as well as common clinicopathological parameters, which are used to construct a multimode model that can accurately predict the treatment efficacy and survival of the third-generation EGFR-TKI treatment, and to use the model to assist in screening high-risk populations suitable for upfront cranial radiotherapy. Participants receiving third-generation EGFR-TKI treatment will be enrolled in our study and we will collect their regular contrast-enhanced chest CT and contrast-enhanced brain MRI for model construction.
Official title: Artificial Intelligence-guided Prognostication and Cranial Radiotherapy Optimization in First-line Third-generation EGFR-TKI-treated EGFR-mutant Non-small Cell Lung Cancer With Baseline Brain Metastases: a Multicenter, Observational Study
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
800
Start Date
2024-09-30
Completion Date
2025-10-01
Last Updated
2024-11-12
Healthy Volunteers
No
Interventions
third-generation EGFR TKIs (Almonertinib/Furmonertinib/Osimertinib)
EGFR-mutated NSCLC patients with brain metastases who met the inclusion and exclusion criteria, would receive first-line third-generation EGFR-TKI treatment (monotherapy or combined with upfront cranial radiotherapy)
Locations (1)
Shanghai Cancer center
Shanghai, Shanghai Municipality, China