Welcome to Radiomics

Tumour Phenotyping with non-invasive imaging

For a brief introduction on 'radiomics' please view the animation:

About Radiomics

Human cancers exhibit strong intra and inter patient heterogeneity, which occurs at different levels: genes, proteins, cells, microenvironment, tissues and organs. This limits the use of, for instance, biopsy based molecular assays but in contrast gives a huge potential for non-invasive imaging techniques. Over the past decade, the use and role of medical imaging technologies in clinical oncology has greatly expanded from primarily a diagnostic tool to include a more central role in the context of individualized medicine (figure 1). Recent advances in medical imaging technology allow the use of more advanced image analysis methods beyond simple measurements of, for instance, tumor size or radiotracer uptake metrics. Imaging therefore has great potential to guide therapy and to monitor the development and progression of the disease or its response to therapy.

Radiomics enables the high-throughput extraction of a large amount (400+) quantitative features from medical images of a given modality (e.g. CT, PET, or MR), providing a comprehensive quantification of the tumor phenotype, based on simple medical imaging. Radiomics can provide complementary and interchangeable information compared to other sources (e.g. demographics, pathology, blood biomarkers, or genomics), improving individualized treatment selection and monitoring. Radiomics can have large clinical impact, since imaging is routinely used in clinical practice worldwide, providing an unprecedented opportunity to improve decision-support at low cost.

Lecture: Introduction to Radiomics




The Radiomics workflow basically consists the following steps (Figure 3). The first step is acquisition of high quality standardized imaging, for diagnostic or planning purposes. The macroscopic tumor is defined on these images, either with an automated segmentation method or alternatively by an experienced radiologist or radiation oncologist. Next, a large number of quantitative features is extracted from the previously defined tumor region. These features describe, amongst others, tumor image intensity, texture and shape and size of the tumor. The final step is analysis of the acquired imaging features.

Figure 1: Different sources of information, e.g. demographics, imaging, pathology, toxicity, biomarkers, genomics and proteomics, can be used for selecting the optimal treatment.

Figure 2: Typical examples of lung tumors, showing differences captured by simple CT imaging.. Displayed regions are 16x16 cm for the displayed CT slices and 16x16x16 cm for the 3D renderings

Figure 3: Schematic representation of the workflow