Tumor Imaging
Tumor Imaging - Related Videos
14:04
Viral Nanoparticles for In vivo Tumor Imaging
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Cited by 45 •
2012
Plant viral nanoparticles (VNPs) are promising platforms for applications in biomedicine. Here, we describe the procedures for plant VNP propagation, purification, characterization, and bioconjugation. Finally, we show the application of VNPs for tumor homing and imaging using a mouse xenograft model and fluorescence imaging.
09:18
Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models
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2026
This protocol uses a multichamber bed for parallel magnetic resonance imaging (MRI) of up to four animals to detect pancreatic adenocarcinoma tumors in genetically engineered mouse models. This multianimal MRI protocol is fast and cost-effective for detecting and measuring tumors, facilitating animal selection for preclinical studies and longitudinal monitoring of tumor growth.
09:04
In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles
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Cited by 13 •
2014
We describe a multi-angle rotational optical imaging (MAROI) system for in vivo quantitation of a fluorescent marker delivered by saposin C (SapC)-dioleoylphosphatidylserine (DOPS) nanovesicles. Employing mouse models of cancer and arthritis, we demonstrate how the MAROI signal curve analysis can be used for the precise mapping and biological characterization of disease processes.
05:08
In vivo Imaging of Tumor Angiogenesis using Fluorescence Confocal Videomicroscopy
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Cited by 5 •
2013
In this paper, we present a method to analyze tumor microvessels in vivo using dynamic contrast-enhanced fluorescence videomicroscopy. Two quantitative parameters were acquired: functional capillary density reflecting the vascularity of the tumor, and index leakage reflecting the leakiness of the endothelial wall.
08:24
Extended Time-lapse Intravital Imaging of Real-time Multicellular Dynamics in the Tumor Microenvironment
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Cited by 25 •
2016
This protocol describes the use of multiphoton microscopy to perform extended time-lapse imaging of multicellular interactions in real time, in vivo at single cell resolution.
FAQs
Each modality emphasizes a different biological or physical property. CT provides information from X-ray attenuation, MRI reflects tissue responses to magnetic fields, ultrasound captures reflected sound, and PET follows radiolabeled molecules associated with metabolic activity. Because these signals differ, combining modalities can relate tumor structure to vascularity, metabolism, and other features rather than relying on a single form of contrast.
Structural measurements can show tumor burden and anatomical distribution, whereas functional signals can indicate processes such as metabolism or vascularity. These features may change differently during disease progression or therapy. Tracking both helps cancer researchers interpret whether an observed change reflects altered tumor size, altered biology, or a combination, strengthening connections between imaging findings and experimental outcomes.
Repeated imaging reveals how tumors and their measurable characteristics change over time. This allows researchers to follow treatment response, progression, and metastatic spread within the same study rather than relying only on separate observations at different time points. Longitudinal measurements can therefore connect biological mechanisms with outcomes and support evaluation of experimental therapies in preclinical models and patients.
Selection should follow the biological question and the feature that must be measured. CT may support assessment of attenuation and structure, MRI can examine tissue responses, ultrasound can provide reflected-sound information, and PET can address radiolabeled molecular activity. The chosen approach should also fit the intended outcome, such as burden, vascularity, metabolism, treatment response, or spread.
Researchers can image tumors before and after an intervention to measure changes in burden, vascularity, metabolism, or metastatic distribution. These observations provide spatial and functional evidence of response, helping compare experimental treatments and relate imaging changes to underlying cancer biology. The same strategy can be applied in preclinical models and patients, supporting translation from laboratory investigation to clinical evaluation.
Tumor imaging can contribute to early detection, patient stratification, and image-guided procedures in addition to assessing tumor burden. It also helps investigators examine metastatic spread and monitor changes over time. In cancer research, these capabilities support selection of study populations, characterization of disease biology, and evaluation of whether imaging findings correspond with clinically meaningful outcomes.