ReviewJournal of magnetic resonance imaging : JMRI2019
Translating preclinical MRI methods to clinical oncology.
Review in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 38 citations in OpenAlex.
- Recent Advances in Magnetic Resonance Imaging for the Diagnosis of Liver Cancer: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Gadolinium-Based Contrast Agents (GBCAs) for MRI: A Benefit-Risk Balance Analysis from a Chemical, Biomedical, and Environmental Point of View.Global challenges (Hoboken, NJ) · 2025Review
- Assessment of Tumor Cell Invasion and Radiotherapy Response in Experimental Glioma by Magnetic Resonance Elastography.Journal of magnetic resonance imaging : JMRI · 2025Article
- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- Label-Free Chemically and Molecularly Selective Magnetic Resonance Imaging.Chemical & biomedical imaging · 2023Review
- Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging.Tomography (Ann Arbor, Mich.) · 2023Article
- Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials.Tomography (Ann Arbor, Mich.) · 2023Article
- Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom.Tomography (Ann Arbor, Mich.) · 2023Article
- Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy.Advanced drug delivery reviews · 2022Review
- Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.Biophysics reviews · 2022Review
- Establishing Imaging Biomarkers of Host Immune System Efficacy during Glioblastoma Therapy Response: Challenges, Obstacles and Future Perspectives.Metabolites · 2022Article
- Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer.Tomography (Ann Arbor, Mich.) · 2021Article
- Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.Cancers · 2021Review
- Non-Invasive Evaluation of Acute Effects of Tubulin Binding Agents: A Review of Imaging Vascular Disruption in Tumors.Molecules (Basel, Switzerland) · 2021Review
- Preclinical Applications of Multi-Platform Imaging in Animal Models of Cancer.Cancer research · 2021Review
- Probing neural tissues at small scales: Recent progress of oscillating gradient spin echo (OGSE) neuroimaging in humans.Journal of neuroscience methods · 2021Article
- Math, magnets, and medicine: enabling personalized oncology.Expert review of precision medicine and drug development · 2021Article
- Towards integration ofScientific reports · 2020Article
- Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine.Tomography (Ann Arbor, Mich.) · 2020Article
- An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI.Scientific reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 8 institutions in 1 country.
Funding
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.