A Texture Feature Ranking Model for Predicting Survival Time of Brain Tumor Patients

Automated prediction of patient-specific disease progression can significantly contribute to clinical treatment. This paper presents a computer-assisted framework to tackle the survival time prediction problem. Inspired by the assumption that niche tumor regions may play a significant role in cancer diagnosis, we explore local visual variations from multiple MRI sequences. The research consists of three parts: 1) the extraction of multi-scale Local Binary Patterns (LBP) to describe the visual variations, 2) a supervised forward feature selection approach, called the Feature Ranking Model (FRM) which captures single feature predictive ability efficiently, and combines the top features to form a feature subset, 3) We cast the clinical survival time prediction task as a binary category classification problem. We tested the framework using a dataset of 32 cases collected from The Cancer Genome Atlas (TCGA). We obtained a 93.75% accuracy rate for the prediction of survival time.