Clinicoradiomics Models
Clinicoradiomics Models - Related Videos
12:31
In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Cited by 65 •
2013
Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.
02:00
Molecular Models
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2020
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules. Skeletal Model Simpler two-dimensional representations of chemical compounds are accomplished using skeletal models. The illustration shows only the molecular framework or bonds without explicitly showing the atoms. In this representation, many of the carbon atoms...
11:05
Behavioral Characterization of an Angelman Syndrome Mouse Model
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Cited by 1 •
2023
This manuscript presents a set of highly reproducible behavioral tests to validate an Angelman syndrome mouse model.
02:18
The Bohr Model
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2020
Following the work of Ernest Rutherford and his colleagues in the early twentieth century, the picture of atoms consisting of tiny dense nuclei surrounded by lighter and even tinier electrons continually moving about the nucleus was well established. This picture was called the planetary model since it pictured the atom as a miniature “solar system” with the electrons orbiting the nucleus like planets orbiting the sun. The simplest atom is hydrogen, consisting of a single proton as the nucleus...
07:57
Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving (SSLE) of Glass Crystals
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2017
A methodology is described herein for representing anatomical imaging data within crystals. We create scaled three-dimensional models of biomedical imaging data for use in Sub-Surface Laser Engraving (SSLE) of crystal glass. This tool offers a useful complement to computational display or three-dimensionally printed models used within clinical or educational settings.
FAQs
The model links quantitative image measurements with patient-specific variables so that each data source contributes different information. Intensity, shape, texture, or spatial-pattern features describe characteristics visible within medical images, while demographics, laboratory results, staging, and treatment history provide clinical context. Their combined signal can support more informed disease classification, risk stratification, prognosis estimation, or treatment-response prediction than either source considered alone.
These feature groups capture distinct properties of disease-related imaging patterns. Intensity describes measured image values, shape represents structural form, texture reflects variation or organization within a region, and spatial features describe how patterns are distributed. Including complementary measurements allows a model to characterize disease more broadly, although the usefulness of each feature depends on the imaging data and prediction task.
Clinical variables place image findings within the patient’s broader medical context. Demographics, laboratory results, disease staging, and treatment history may help distinguish patients who appear similar on imaging but differ in risk or expected outcome. Integrating these factors enables the predictive framework to address clinically relevant endpoints, including prognosis and treatment response, rather than relying on image measurements in isolation.
Development generally begins with medical images and patient-level clinical data, followed by image processing to measure selected quantitative characteristics. Radiomic variables are then combined with clinical factors in a statistical or machine-learning model. The resulting predictions must undergo robust validation, with attention to performance across patient populations, before the model’s potential value for medical decision-making can be assessed.
These models are relevant when a study seeks to classify disease, separate patients by risk, estimate prognosis, or predict how patients may respond to treatment. Their value is especially tied to medical decision-making, because they connect measurable imaging patterns with clinical information. Researchers can therefore investigate whether integrated predictors improve characterization of disease or support more informative patient-level assessments.
Reproducible imaging protocols help ensure that measured radiomic features reflect patient-related patterns rather than inconsistent image acquisition or processing. Validation across patient populations tests whether model performance remains reliable beyond the data used for development. Without these safeguards, apparent predictive value may not translate consistently to other patients, limiting the model’s clinical usefulness and interpretability.