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EH 1_2015

Vue Cloud Print Ad 210mm W x 297mm H CLINICAL COLLABORATION AND AFFORDABLE IT SUPPORT MATTER. F O S T E R I N G C L I N I C A L C O L L A B O R AT I O N . K N O W I N G M AT T E R S .F O S T E R I N G C L I N I C A L C O L L A B O R AT I O N . K N O W I N G M AT T E R S . Optimal patient care demands close clinical collaboration – which requires convenient, real-time information access and sharing. Increased information exchange means greater demand on IT infrastructure and support, often generating costs difficult to plan for and budget. Vue delivers secure cloud-based services that can consolidate silos of data and facilitate image-exchange services – all in a predictable operational model. V U E . K N O W I N G M AT T E R S . operational model.operational model.operational model.operational model.operational model.operational model.operational model. V U E .V U E .V U E .V U E . K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S .K N O W I N G M AT T E R S . 13EH @ ECR Advancing sequencing technology on medicine in oncology dict treatment response and assess the effectiveness of treatment early on. However, many of these biomarkers still need to be validated in multicen- tre prospective studies. ‘In the future, the new discipline of ‘radiomics’ promises to dramatically expand the number of imaging bio- markers we can derive from MRI. With radiomics analyses, a large number of features quantifying tumour signal intensity, texture and shape, as well as functional parameters and clusters of multiparametric data, can be extracted from MR images and correlated with treatment outcome data. Furthermore, in an approach called ‘radiogenomics’, such features can be correlated with omic data, including specific gene clusters. ‘By providing spatially and temporally specific information about tumour biology, radiomics and radiogenom- ics will enable radiologists to recom- mend where to biopsy, make predic- tions about tumour aggressiveness in areas where biopsy is not feasible, and improve treatment selection and planning as well as assessment of treatment response. As discussed (see below), hyperpolarised MRSI is anoth- er MRI technique with great potential to contribute to precision medicine.’ Hyperpolarised MR spectroscopic imaging may revolutionise the way MRI is used in cancer care. What does this revolution look like? ‘Hyperpolarised MR spectroscopic imaging (HP-MRSI) is a new tech- nology that increases the MR signal 10,000–100,000-fold, and therefore enables MR imaging of nuclei other than 1H with great speed and sensitiv- ity. Imaging after injection of a hyper- polarised agent, such as 13C-pyruvate, allows visualisation of the distribution of the agent itself as well as its down- stream enzymatic products. ‘By allowing precise identification of aberrant molecular processes, HP-MRSI should enable better treat- ment selection and earlier assessment of treatment response.’ How do you see these advanced MRI techniques being trans- lated into clinical routine for greater precision in medicine? ‘Importantly, HP-MRSI allows short imaging times (seconds to minutes) that can be added to existing protocols without significantly affecting work- flow, and injected HP-MRSI agents are naturally occurring substances with no inherent toxicity, making them safe for use in patients. Because of these practical characteristics, HP-MRSI could easily be incorporated into rou- tine MRI examinations that include other sequences, such as T-2-weighted imaging, DCE-MRI or DW-MRI. ‘When these capabilities are also com- bined with an overlay of augment- ed information from radiomics and radiogenomics, MRI may become an extremely powerful tool for increasing precision in all areas of cancer care, from diagnosis to treatment selection and planning, treatment monitoring and follow up. ‘Of note, machine learning, construc- tion of radiomics algorithms and automated pattern recognition should make it possible to develop augment- ed programmes and therefore dissem- inate and introduce the added value of radiomics/radiogenomics in clinical practice, thus improving accuracy in oncologic imaging among radiologists who do not sub-specialise in the field. ‘Getting to that point will need a great deal of teamwork and much greater integration of advanced biomedical informatics in clinical settings.’ Texture analysis: Compared to T2WI & DWI there is further improvement in PCa detection, visualisation of tumor hetero­geneity and tumour characterisation Professor Hedvig Hricak MD PhD, Depart­ ment of Radiology, Memorial Sloan- Kettering Cancer Center. New York and Cornell University, Ithaca, New York

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