Tumor Lesion Analysis
Tumor Lesion Analysis - Related Videos
08:16
Comparative Lesions Analysis Through a Targeted Sequencing Approach
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2019
This article describes a method to identify clonal and subclonal alterations among different specimens from a given patient. Although the experiments described here focus on a specific tumor type, the approach is broadly applicable to other solid tumors.
06:43
Quantitative Analysis and Characterization of Atherosclerotic Lesions in the Murine Aortic Sinus
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Cited by 51 •
2013
We describe procedures to quantify and characterize atherosclerotic lesions in mouse models using precision sectioning of the aortic sinus and ascending aorta combined with histochemical and immunohistochemical analysis.
15:01
Quantitative, Real-time Analysis of Base Excision Repair Activity in Cell Lysates Utilizing Lesion-specific Molecular Beacons
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Cited by 31 •
2012
We describe a method for the quantitative, real-time measurement of DNA glycosylase and AP endonuclease activities in cell nuclear lysates. The assay yields rates of DNA Repair activity amenable to kinetic analysis and is adaptable for quantification of DNA Repair activity in tissue and tumor lysates or with purified proteins.
08:04
Monitoring Tumor Metastases and Osteolytic Lesions with Bioluminescence and Micro CT Imaging
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Cited by 22 •
2011
An experimental mouse model of bone metastasis was established following intracardiac delivery of luciferase expressing mammary tumor cells. Tumor development and resulted osteolytic lesion were monitored longitudinally with bioluminescence and micro CT imaging.
11:52
Analysis of Targeted Viral Protein Nanoparticles Delivered to HER2+ Tumors
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Cited by 1 •
2013
This article details the procedures for optical imaging analysis of the tumor-targeted nanoparticle, HerDox. In particular, detailed use of the multimode imaging device for detecting tumor targeting and assessing tumor penetration is described here.
FAQs
The analysis depends on separating three linked tasks: detecting a suspected lesion, segmenting its boundaries, and quantifying its characteristics. Detection identifies an abnormal region, segmentation assigns which pixels, image areas, or specimen regions belong to it, and quantification converts that region into measurements such as size, shape, location, or enhancement. Keeping these tasks distinct clarifies how errors arise.
Measurements are most informative when interpreted together rather than in isolation. Size can indicate lesion burden, while shape and location add structural context; contrast enhancement contributes another distinguishing feature in medical images. In tissue specimens, cellular composition supplies complementary information. Combining these features helps separate tumor from surrounding structures and supports more consistent characterization.
Standardization makes measurements comparable across examinations, studies, and observers. In longitudinal work, consistent lesion boundaries and feature definitions help reveal whether disease burden is increasing, decreasing, or remaining stable. That comparability is important for distinguishing apparent change from differences in analysis practice, strengthening reproducibility and supporting the use of quantitative measurements in treatment-response studies.
A basic workflow starts by selecting the relevant medical image or tissue specimen, locating suspected abnormal regions, and delineating the lesion from surrounding structures. The analyst then records supported characteristics, including size, shape, location, contrast enhancement, or cellular composition, depending on the material examined. Applying the same sequence to each case creates organized measurements for later comparison.
Researchers apply Tumor Lesion Analysis to several cancer research questions, including disease burden, staging, treatment response, and progression or regression. The measurements can also supply quantitative inputs for clinical trials and imaging-based biomarkers. Its value increases when lesion characteristics are captured consistently, because standardized data allow changes and differences between cases to be evaluated more reproducibly.
In cancer research, longitudinal lesion assessment connects measurements from multiple time points rather than treating each observation separately. Tracking size, shape, location, or enhancement can document how a lesion changes during study follow-up, while tissue-based cellular composition may add biological context. These records help organize evidence about response or progression and support reproducible comparisons within research studies.