Patient stratification using unsupervised clustering and radiomics

Abstract

Medical care is currently provided in clinical practice according to the “one-size-fitsall” approach, through which all patients suffering from the same symptoms and diseases receive the same treatment. Despite its wide use, the current approach has reached its maximal performance, as a treatment that performs well for a majority of the population may not be suitable for a specific patient or subgroup of patients. Alternative approaches have therefore been proposed, including the so-called personalized medicine. However, this patient-specific paradigm is yet to find its way to clinical practice as it makes clinical decision making highly complex. Consequently, stratified medicine was proposed to provide medical care on a subgroup basis. Specifically, the population of diseased individuals are stratified according to subgroups of patient characteristics, disease manifestations and treatment responses. Subsequently, the treatments are adapted according to the subgroup to which a given patient belong, thus potentially optimizing recovery. However, this approach depends on the definition of the subgroups in question, which is not trivial. To address this from a computational point of view, a potential solution would be to apply unsupervised clustering. Yet, the techniques that are most commonly used are limited by the fact that they require as input the number of clusters, which varies between diseases and which is often not known in advance. This thesis aims to implement and validate a recently proposed clustering technique called “Cancer integration via multi-kernel learning” (CIMLR). While it was developed for oncology, this study will apply it for the first time for cardiac stratification and will test its applicability by considering a group of hypertensive patients. The phenotypes used as input for CIMLR-based stratification are cardiac radiomics data, a new type of imaging features that describe a range of shape, size, intensity and texture characteristics of the organs. The results show that the obtained clusters are clinically meaningful and that they are in correspondence with key lifestyle information of the patients. This indicate the future potential of the approach for image-based stratified medicine in cardiology.