A Deep Learning Framework for Diagnosis and Survival Prognosis of Central Nervous System Tumors

Abstract

Diagnosis and prognosis of central nervous system (CNS) tumors rely on manual and algorithmic approaches that are subject to variations, inefficiencies and bias. With 5-year survival rates as low as 6%, timely tumor assessment is crucial to ensure patient health. Machine learning and deep learning techniques have been used over the past decade to reduce the assessment burden and augment physician diagnoses. Traditional machine learning models require manual feature extraction and engineering, introducing further variability of the results. On the other hand, automated deep learning models require complicated engineering and workflow implementations that need to be managed and updated for new datasets. This study endeavors to define a generalizable, two-phase neural network pipeline and user interface that can be applied to clinical assessment of any tumor with 3D MRI scans, allowing for improved personalization of patient diagnosis and treatment options. In the first diagnostic phase, radiomic features are used to predict tumor severity and extent. The outputs of this phase are then passed to a survival prognosis model along with the patients’ clinical data to predict the 5-year overall survival rate. The diagnostic model achieved a F1-Score, a measure of classification accuracy, of 94% and the prognostic model achieved a risk-adjusted, time-dependent Harrell’s Concordance Index score of 0.92, indicating the framework’s generalizability to new datasets. The mobile app developed as part of this study offer ease of access to physicians and radiologists in reviewing predicted results and subsequent patient interactions.