Annual Brain MRI Surveillance for Detection of Brain Metastasis in Patients With Lung Cancer
NCT07646041 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 229323
Last updated 2026-06-12
Summary
This retrospective observational study will evaluate the effectiveness of periodic brain magnetic resonance imaging surveillance for detecting brain metastasis in patients with lung cancer. Using linked nationwide claims and cancer registry data from Korea, the study will emulate a sequence of monthly target trials among patients with newly diagnosed lung cancer and no prior brain metastasis.
At each monthly trial, eligible patients will be classified according to whether they receive active brain MRI surveillance or remain in an inactive surveillance state. The main analysis will define the active surveillance period as one year after brain MRI. A sensitivity analysis will define the active surveillance period as two years. Patients may re-enter later trials if they again become eligible. Patients will be excluded from a given trial if they have recent neurologic symptoms suggesting diagnostic MRI, prior brain MRI within the preceding surveillance interval, brain metastasis, or death.
The primary objective is to assess whether annual brain MRI surveillance increases detection of brain metastasis. Secondary objectives are to evaluate whether surveillance-detected brain metastases are more likely to be asymptomatic or potentially treatable, and whether surveillance-detected brain metastasis is associated with lower mortality among patients who develop brain metastasis.
Conditions
- Lung Cancer
- Brain Metastasases
- MRI
- Surveillance
Sponsors & Collaborators
-
Samsung Medical Center
lead OTHER
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2012-01-01
- Primary Completion
- 2021-12-31
- Completion
- 2021-12-31
More Related Trials
-
Comparison Between LDCT With DL Recontruction and Standard Dose CT
NCT05324046 ·Status: UNKNOWN
-
Contrast Between Traditional Regression Model and AI in Predicting Prolonged Stay Stay After Head and Neck Tumors
NCT06570486 ·Status: RECRUITING
-
Artificial Intelligence-supported Reading Versus Standard Double Reading for the Interpretation of Magnetic Resonance Imaging in the Detection of Local Recurrence for Nasopharyngeal Carcinoma: a Randomised Controlled Multicenter Study
NCT06356441 ·Status: NOT_YET_RECRUITING
-
Automated Segmentation and Volumetry for Meningioma Using Deep Learning
NCT05093751 ·Status: COMPLETED
-
Spectral Precise Image Study for Brain Tumors
NCT07620470 ·Status: RECRUITING
-
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
NCT06831357 ·Status: RECRUITING
-
Low Contrast Agent and Radiation Dose Protocol for Liver CT in Patients With HCC
NCT04722120 ·Status: COMPLETED ·Phase: NA
-
Development and Prospective Validation of a Digital Pathology-based Artificial Intelligence Diagnostic Model for Pan-cancer Lymphatic Metastasis
NCT06517979 ·Status: RECRUITING
-
A Radiomic Model for Risk of Local Recurrence and DFS for T3 and T4 Non-small Cell Lung Cancer
NCT06405815 ·Status: COMPLETED
-
Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma
NCT07050576 ·Status: RECRUITING
-
Deep Learning Time-Series Prediction of Long-Term Growth Patterns of Pulmonary Ground-Glass Nodules Using Serial CT
NCT07647692 ·Status: NOT_YET_RECRUITING
-
Predicting Immunotherapy Response and Survival of Lung Cancer Patients Using Artificial Intelligence and Radiomics (Radiology-AI-Lung)
NCT07059923 ·Status: RECRUITING
-
Artificial Intelligence-Based Early Warning for Distant Metastasis in Malignant Tumors
NCT07616011 ·Status: NOT_YET_RECRUITING
-
AI-assisted Diagnosis of Malignant Brain Tumors
NCT07198256 ·Status: RECRUITING
-
Multimodal Machine Learning Characterization of Solid Tumors
NCT04687969 ·Status: COMPLETED
-
Multicenter Prospective Study on MRI AI Model for Midline Glioma Subtyping and Prognosis:
NCT07608003 ·Status: RECRUITING
-
Mechanistic Study on the Diagnosis of Esophageal Cancer Lymph Node Metastasis Using Spectral CT, Multimodal MRI, FAPI PET-CT, Pathology, and AI Evaluation System
NCT06818214 ·Status: NOT_YET_RECRUITING
-
Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
NCT05925738 ·Status: UNKNOWN
-
A Transfer Learning Radiomics Model for Predicting Response to Initial Transarterial Embolization in Patients with Gastroenteropancreatic Neuroendocrine Tumor Liver Metastases
NCT06853457 ·Status: COMPLETED
-
Spectral CT Combined With MSI for Predicting Regional Lymph Node Metastasis in Gastric Cancer
NCT07565818 ·Status: ACTIVE_NOT_RECRUITING ·Phase: NA
-
A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps
NCT07244094 ·Status: RECRUITING
-
Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
NCT05925751 ·Status: UNKNOWN
-
CONNEctome-guided Navigation for Eloquent-area Tumor Surgery Trial
NCT07657403 ·Status: RECRUITING ·Phase: NA
-
Development of Intelligent Model for Radioactive Brain Damage of Nasopharyngeal Carcinoma Based on Radio-metabolomics
NCT05547971 ·Status: UNKNOWN
-
A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC)
NCT06829147 ·Status: RECRUITING