Disease Progression
Disease Progression - Related Videos
06:49
A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS
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Cited by 120 •
2015
This video protocol describes a sensitive, reliable, and quick method for evaluating the neuromuscular deficits in a transgenic mouse model of amyotrophic lateral sclerosis.
08:41
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
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Cited by 3 •
2023
Using a diet-induced non-alcoholic fatty liver disease (NAFLD) mouse model, we describe the use of novel in vivo micro-computed tomography imaging techniques as a non-invasive method to assess the progression stages of NAFLD, focusing predominantly on the hepatic vascular network due to its significant involvement in NAFLD-related hepatic dysregulation.
09:27
Analysis of Liver Microenvironment During Early Progression of Non-Alcoholic Fatty Liver Disease-Associated Hepatocellular Carcinoma in Zebrafish
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Cited by 8 •
2021
Here, we present how to generate a non-alcoholic fatty liver disease (NAFLD)-associated Hepatocellular Carcinoma (HCC) zebrafish model to study the impact of cholesterol surplus on liver microenvironment and immune cell landscape.
05:11
Construction of An Orthotopic Xenograft Model of Non-Small Cell Lung Cancer Mimicking Disease Progression and Predicting Drug Activities
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Cited by 1 •
2024
This study presents an orthotopic non-small cell lung cancer (NSCLC) model based on intrapulmonary inoculation of multicellular spheroids of fluorescent A549-iRFP cells. The model recapitulates clinical NSCLC stages and responds to cisplatin, according to dynamic in vivo monitoring of long-wavelength fluorescence.
09:25
Bioluminescent Orthotopic Model of Pancreatic Cancer Progression
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Cited by 34 •
2013
Improved understanding of pancreatic cancer biology is critically needed to enable the development of better therapeutic options to treat pancreatic cancer. To address this need, we demonstrate an orthotopic model of pancreatic cancer that permits non-invasive monitoring of cancer progression using in vivo bioluminescence imaging.
FAQs
The two approaches capture different aspects of change. Longitudinal measurements show how biomarkers or symptoms evolve across repeated assessments, whereas time-to-event analysis examines when an outcome such as relapse, remission, or death occurs. Using both can provide a more complete picture of patient trajectories and help relate gradual changes to clinically important outcomes.
Repeated-measures analysis evaluates observations collected from the same patients over time rather than treating each measurement as unrelated. This structure helps characterize individual and group trajectories while recognizing that patients may begin at different levels or change at different rates. The resulting analysis can better identify patterns of disease severity and progression.
A hazard model helps examine factors associated with the timing of an event during disease progression. Researchers can use it to study whether patient characteristics, biomarkers, symptoms, or treatments are linked to faster progression toward outcomes such as relapse, remission, or death. This supports comparisons of risk patterns and development of prognostic evidence.
Researchers collect biomarker or symptom measurements repeatedly and analyze how values change across follow-up. Statistical modeling can then describe common patterns, distinguish more rapid from slower changes, and assess variation among patients. These trajectories help clarify how disease severity develops over time and can guide decisions about prognosis and monitoring.
A typical analysis begins by selecting longitudinal measures and clinically meaningful outcomes, followed by organizing observations according to patient and time point. Researchers then apply repeated-measures methods, survival analysis, or hazard models as appropriate, and examine factors related to different trajectories or event timing. The findings can summarize progression and support clinical interpretation.
Progression analyses provide outcomes that can be compared between treatment groups or across patient characteristics. Changes in biomarkers and symptoms can indicate evolving disease status, while time to relapse, remission, or death can show differences in clinical outcomes. These results help evaluate treatment effects, inform trial design, and strengthen evidence for prognosis and disease monitoring.