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.
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1、Histological Type: Patients with squamous cell carcinoma or small cell lung cancer are excluded.
1. Primary Diagnosis: Patients diagnosed with non-small cell lung cancer (NSCLC), confirmed through histopathological examination. 2. Leptomeningeal Metastases (LM): Patients with clinically or pathologically confirmed LM, a serious complication of NSCLC characterized by cancer spread to the membranes surrounding the brain and spinal cord. 3. Age Range: Patients aged 18 years or older. 4. ECOG Performance Status: Patients must have an ECOG score of 3 or lower, indicating varying degrees of functionality but generally not bedridden. 5. Intrathecal Chemotherapy: The study focuses on patients undergoing lumbar puncture and intrathecal chemotherapy as part of their treatment.
Cohort
Prospective
Not listed
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.
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The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Lead sponsor
Nanjing University
Collaborator
Nanjing Jiangning Hospital
Collaborator
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