Translating preclinical MRI methods to clinical oncology.
David A HormuthAnna G SoraceJohn VirostkoRichard G AbramsonZaver M BhujwallaPedro Enriquez-NavasRobert GilliesJohn D HazleRalph P MasonC Chad QuarlesJared A WeisJennifer G WhisenantJunzhong XuThomas E YankeelovPublished in: Journal of magnetic resonance imaging : JMRI (2019)
The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- papillary thyroid
- blood flow
- cell therapy
- diffusion weighted imaging
- high resolution
- deep learning
- palliative care
- computed tomography
- magnetic resonance
- squamous cell
- convolutional neural network
- squamous cell carcinoma
- small cell lung cancer
- endothelial cells
- newly diagnosed
- mass spectrometry
- prognostic factors
- big data
- mesenchymal stem cells
- intellectual disability
- childhood cancer
- artificial intelligence
- chronic kidney disease
- sensitive detection