Fig. 4: Machine Learning (ML) in a radiomics pipeline for evaluating tumour habitats.

a Whole tumour segmentation and identification of physiologically different regions by means of tissue-specific sub-segmentation on computed tomograhy (CT) imaging (e.g. using 3D volume rendering of tissue components with colour codes shown below). This is followed by b voxel-based radiomic feature map extraction and unsupervised clustering for tumour habitats considering the most clinically relevant region. Next, c quantitative measurements and inferred tumoural heterogeneity metrics are processed by ML predictive models to yield diagnostic and prognostic results. In this example, we have used CT images from a patient with metastatic ovarian cancer with a representative omental lesion.