Tackling imbalance radiomics in acoustic neuroma

Abstract

Acoustic neuroma is a primary intracranial tumour of the myelin-forming cells of the 8th cranial nerve. Although it is a slow growing benign tumour, symptoms in the advanced phase can be serious. Hence, controlling tumour growth is essential and stereotactic radiosurgery, which can be performed with the CyberKnife robotic device, has proven effective for managing this disease. However, this approach may have side effects and a follow-up is necessary to assess its efficacy. To optimise the administration of this treatment, in this work we present a machine learning-based radiomics approach that first computes quantitative biomarkers from MR images routinely collected before the CyberKnife treatment and then predicts the treatment response. To tackle the challenge of class imbalance observed in the available dataset we present a cascade of cost-sensitive decision trees. We also experimentally compare the proposed approach with several approaches suited for learning under class skew. The results achieved demonstrate that radiomics has a great potential in predicting patients response to radiosurgery prior to the treatment that, in turns, can reflect into great advantages in therapy planning, sparing radiation toxicity and surgery when unnecessary.