Predictive Modeling of Brain Metastasis in Advanced Lung Adenocarcinoma: A Hybrid Approach Combining Traditional Radiomics and Deep Learning from Thoracic CT Images

Purpose: Create a deep learning-based radiomics framework to anticipate prediction models for advanced lung adenocarcinoma with brain metastases. This aims to inform individualized treatment and prognosis, enhancing clinical decisions and patient outcomes. Methods: Analyzed 404 patients' CT scans from two hospitals. Extracted handcrafted and deep learning features. Developed three models (Rad, DTL, Combined) to predict brain metastasis risk. The Combined model with clinical features formed the DLRN model. Evaluated using DCA and Calibration Curve. Results: The Combined model outperformed others, with AUCs of 0.978 (training) and 0.833 (validation). When combined with clinical data, DLRN achieved AUCs of 0.979 (training) and 0.837 (validation), with high accuracy, sensitivity, and specificity. DCA showed DLRN's clinical benefit. Conclusions: Developed and validated DLRN model for precise prediction of brain metastases.