This study focuses on developing an innovative, artificial intelligence-based model using optic nerve sheath ultrasound videos to predict intracranial pressure in lung cancer patients with leptomeningeal metastasis. The study also aims to create a multimodal clinical prognosis model that can help improve patient outcomes. By analyzing ultrasound data from patients at two major medical centres, the research seeks to provide more accurate and early predictions of complications related to elevated intracranial pressure, ultimately improving treatment and management strategies for these patients.
Study Type
OBSERVATIONAL
Enrollment
142
Jiangning Hospital Affiliated to Nanjing Medical University
Nanjing, Jiangsu, China
Nanjing Drum-tower Hospital Affiliated to Medical College of Nanjing University
Nanjing, Jiangsu, China
Accuracy of the Intracranial Pressure (ICP) Prediction Model and Efficacy of the Prognostic Model
Accuracy of the Intracranial Pressure (ICP) Prediction Model: This involves constructing a prediction model for ICP based on optic nerve sheath diameter (ONSD) and other ultrasound measurements (e.g., optic disk height, ODH). The model's accuracy will be evaluated by comparing the predicted ICP values with those obtained through lumbar puncture, using metrics such as the area under the ROC curve (AUC), sensitivity, and specificity. Efficacy of the Prognostic Model: This model will predict clinical outcomes such as progression-free survival (PFS) and overall survival (OS) based on multimodal data, including clinical and imaging data. The predictive performance will be assessed using the C-index and other statistical measures to determine its effectiveness in forecasting patient outcomes.
Time frame: Half an hour before the lumbar puncture
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