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
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.
• Pathologically confirmed non-small cell lung cancer;
• clinical stage IV (AJCC, 8th edition, 2017);
• EGFR sensitive mutations: EGFR L858R, EGFR exon 19 deletion;
• age≥18 years old;
• KPS score≥70;
• brain metastases at diagnosis;
• complete systemic imaging (including brain MRI) before third-generation EGFR-TKI treatment;
• received standard third-generation EGFR-TKI therapy (monotherapy or combined with brain radiotherapy);
• willing to cooperate with the follow-up after third-generation EGFR-TKI treatment;
• informed consent of the patient.