Diffusion-Weighted MR imaging: Clinical applications of kurtosis analysis to prostate cancer

Magnetic resonance imaging technique known as DWI (diffusion-weighted imaging) allows measurement of water diffusivity on a pixel basis for evaluating pathology throughout the body and is now routinely incorporated into many body MRI protocols, mainly in oncology. Indeed water molecules motion reflects the interactions with other molecules, membranes, cells, and in general the interactions with the environment. Microstructural changes as e.g. cellular organization and/or integrity then affect the motion of water molecules, and consequently alter the water diffusion properties measured by DWI. Then DWI technique can be used to extract information about tissue organization at the cellular level indirectly from water motion. In general the signal intensity in DWI can be quantified by using a parameter known as ADC (Apparent Diffusion Coefficient) emphasizing that it is not the real diffusion coefficient, which is a measure of the average water molecular motion. In the simplest models, the distribu- tion of a water molecule diffusing in a certain period of time is considered to have a Gaussian form with its width proportional to the ADC. However, water in biological structures often displays non-Gaussian diffusion behavior, consequently the DWI signal shows a more complex behavior that need to be modeled following different approaches. In this work we explore the possibility to quantify the degree to which water diffusion in biologic tissues is non-Gaussian introducing the AKC parameter (Apparent Kurtosis Coefficient). In this work we have realized DWI non-Gaussian diffusion maps to be used in the clinical routine along with standard ADC maps, giving to the radiologist another tool to explore how much structure inside a voxel is organized. In particular in this work some prostate DWI examples have been analyzed and will be shown

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients.

NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e., standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence