Revealing liver micrometastases in vivo using ultra-high definition MRI

Over 90% of cancer-related deaths are caused by metastases. A major obstacle for accurate patient stratification, development of new treatments against metastases and thorough biological understanding is our inability to detect early-stage micrometastases (µMets). The liver is one of the first and most common sites for metastases, particularly in gastrointestinal tract tumours. Thus, early detection of liver metastases is an urgent need in cancer diagnosis for many patients. Present image-based diagnostic methods, including state-of-the-art Magnetic Resonance Imaging (MRI), cannot detect µMets due to major limitations in current image contrast and resolution, as well as a lack of understanding of liver tissue changes during the metastatic process.MicroMetSCAN proposes a paradigm-shifting MRI approach based on highly promising preliminary results that leverages recent progress in MRI acquisition, denoising and contrast generation, to reach the breakthrough of exquisite sensitivity and specificity required to detect and characterise μMets in the liver and provide new insights about metastases-related tissue microenvironment dynamics. To achieve its overarching goal, my proposal consists of four objectives: 1) developing new ultra-high resolution MRI methods targeting robust µMets detection, 2) tailoring the specificity of contrasts towards μMet environments, 3) unravelling the transformations from µMets to full metastases in animal models, yielding critical insights into tumour growth patterns, and 4) translating the new MRI approach to the clinic in a prospective clinical study. MicroMetSCAN will significantly advance the state-of-the-art in cancer imaging, to enable precision diagnosis and treatment planning, with wide-reaching applicability, especially benefitting small lesion detection. Furthermore, it will unveil important biological insights on the progression of metastasis, guiding future therapeutic strategies for better prognosis and outcomes.