DSAI-11 AN OPTIMIZED MACHINE LEARNING MODEL FOR OVERALL SURVIVAL PREDICTION IN BRAIN METASTASIS PATIENTS USING GENOMIC MUTATION AND COPY NUMBER FEATURES

Abstract

Abstract Background Cancer progression and patient survival are influenced by both tumor-intrinsic and microenvironmental factors, including the ability of tumor cells to disseminate and colonize distant organs. Organ-specific metastases, particularly brain metastases (BM), exhibit distinct tumor–microenvironment interactions, therapeutic responses, and clinical outcomes. Integrating metastatic genomic alterations into survival modeling is essential for improving prognostic accuracy. Here, we present an optimized machine learning framework leveraging genomic mutations and copy number variations to predict overall survival (OS) in BM patients. Methods We implemented a rigorous machine learning pipeline for survival prediction. The dataset was randomly divided into training (70%) and independent test (30%) cohorts. Feature selection, model training, and hyperparameter optimization were performed exclusively within the training set. Prognostic features were initially identified using univariable Cox regression (p < 0.05) and refined using machine learning–based selection, retaining features consistently selected across multiple models. Hyperparameters were optimized via 3-fold cross-validation. Model performance was evaluated using the concordance index (C-index) and time-dependent AUC, while Kaplan–Meier analysis assessed risk stratification. Results The cohort comprised 381 BM patients, primarily from lung cancer (51.1%), followed by melanoma (15.2%) and breast cancer (7.9%). Key prognostic features included recurrent single-nucleotide variants in genes such as PTPRT, ARID1A, PREX2, and FAT1. Ridge regression demonstrated the best performance, achieving a C-index of 0.70 in the test cohort. Time-dependent analyses showed AUCs of 0.642, 0.711, and 0.729 at 1, 2, and 3 years, respectively. The model achieved significant risk stratification (HR = 3.45, p < 0.001), with clear separation between predicted risk groups. Conclusions We developed a robust machine learning framework integrating genomic mutations and copy number alterations to predict survival in BM patients. The model demonstrated stable performance and effective risk stratification in an independent cohort, supporting its potential clinical utility for prognostic assessment and precision oncology applications.