Image analysis and machine learning in patients with brain tumors

Abstract

Diffuse lower-grade gliomas (dLGG) are slowly growing primary brain tumors. Patients with dLGG represent a heterogeneous group with various symptoms, diverse progression of the disease as well as survival length. Collecting multidimensional patient data gives the opportunity for application of advanced analysis methods. These methods have shown a great promise with the ability to recognize complex patterns in the data and the potential to improve patient treatment and knowledge on the disease. However, the methods should be applied with consideration to the clinical context. This thesis aims to apply advanced analysis methods for the patients with dLGG in a clinically meaningful manner. Depending on the research question, patients with suspected or histomolecularly confirmed dLGG diagnosis from Sahlgrenska University hospital and external sites were included in the studies of the thesis. The analysed data included multi-modal MRI, clinical data, neuropsychological data and health-related quality of life (HRQoL) assessments. Radiomics was applied for survival prediction (Study I), deep learning for tumor classification (Study II), correlation analysis for investigation of symptom interconnection (Study III) and lesion mapping to find brain structures at risk in isocitrate dehydrogenase (IDH)- mutated gliomas (Study IV). The results showed that radiomics can identify clinically relevant information and a model combining radiomics features with clinical variables improved survival prediction (Study I). Deep learning showed similar performance as the clinical model in classification of IDH-mutation status in non-enhancing gliomas (Study II). Correlation networks revealed that self-reported and observer-assessed variables on mental fatigue and cognitive functioning were not connected (Study III). Lesion mapping quantified brain structures at risk in IDH-mutated gliomas, which were often related to advanced brain functions (Study IV). Advanced analysis methods have aided in the understanding of dLGG in several perspectives from symptoms to prognostic factors for patient survival. The machine learning model showed better performance than clinical models in survival prediction, but not in clinically relevant tumor classification. Our findings strengthen the notion of the importance to apply these methods in a relevant context to gain insight where the methods are clinically useful.