Retrospective Analysis
Retrospective Analysis - Related Videos
07:32
Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software
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Cited by 5 •
2024
The presence of mast cells in the inner margin and peritumor areas of hepatocellular carcinoma after resection confers a favorable prognosis. This study endorses QuPath image analysis software as a promising platform that could meet the need for reproducibility, consistency, and accuracy in digital pathology.
09:36
Standardization of Basket Use in Sialendoscopy: A Ten-Year Retrospective Study
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2025
We present the standardization of the basket use in sialendoscopy for obstructive sialolithiasis in a consecutive ten-year series of patients with obstructive salivary gland, describing localization, stone evaluation, size estimation, choosing basket type, and choosing approach technique (Frontal, Side-to-Side, Back-to-Forward) to achieve a high success rate in its removal.
15:04
A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
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Cited by 46 •
2009
This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
08:03
Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model
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2025
This study proposed an age-adjusted regression modeling using midface and cranial base morphology as a potential tool for preoperative evaluation and individualized surgical planning for children with syndromic craniosynostosis (SC).
FAQs
Selection bias occurs when the available records or included patients do not represent the study population investigators intend to examine. Confounding arises when another variable is related to both an exposure or intervention and an outcome, making relationships harder to interpret. Distinguishing these problems helps researchers avoid treating an observed association as a definitive effect.
Missing clinical, pathological, treatment, or survival information can reduce the completeness of comparisons and may make some patients appear less documented than others. Differences in follow-up also mean that outcomes are not observed for equivalent periods. These limitations can influence apparent patterns, so conclusions should reflect the quality and timing of the available data.
Because investigators do not assign treatments prospectively, treatment groups may differ in patient characteristics or clinical circumstances before outcomes are compared. Retrospective Analysis can therefore reveal associations in real-world records, but the findings require careful consideration of confounding and selection bias. This limitation is especially important when evaluating whether an intervention relates to improved outcomes.
A study begins by defining the population and the research question. Investigators identify relevant existing records or databases, then extract clinical, pathological, treatment, and survival information. They next examine relationships among exposures, interventions, and outcomes. Clear population boundaries and consistent variable extraction help make comparisons more interpretable within the available cancer data.
Its applications include examining treatment effectiveness, identifying prognostic factors, describing disease progression, and comparing patient characteristics. Because the information comes from clinical records and databases, investigators can study how these features relate to outcomes in real-world settings. The resulting evidence may clarify patterns worth testing further, while remaining subject to the study's data limitations.
It can provide efficient access to accumulated clinical experience and reveal associations across patient groups, treatments, and outcomes without waiting for new follow-up to occur. This makes it valuable for evaluating real-world patterns and generating hypotheses. Its contribution is strongest when researchers interpret findings alongside incomplete records, selection bias, confounding, and unequal follow-up.