Radiomics Models
Radiomics Models - Related Videos
10:17
Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
0 Views •
Cited by 28 •
2018
We describe IBEX, an open-source tool designed for medical imaging radiomics studies, and how to use this tool. In addition, some published works that have used IBEX for uncertainty analysis and model building are showcased.
12:31
In Vivo Modeling of the Morbid Human Genome using Danio rerio
0 Views •
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
0 Views •
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
0 Views •
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
0 Views •
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...
FAQs
Segmentation defines the tumor region from which quantitative measurements are calculated. If the selected region changes, the resulting intensity, shape, and texture features can also change, affecting the patterns presented to statistical or machine-learning analyses. Careful region selection therefore supports more consistent characterization and helps ensure that model outputs reflect the tumor rather than unrelated surrounding tissue.
Radiomic features provide measurable descriptions of imaging phenotypes, but their significance comes from linking those measurements with clinical or molecular data. Statistical and machine-learning methods can identify associations within these combined datasets, supporting outputs such as tumor classification, risk stratification, prognosis estimation, or treatment-response prediction. The resulting patterns extend interpretation beyond visual image assessment alone.
Imaging consistency and robust validation are essential because radiomics results depend on the images analyzed and the features extracted from them. Differences in acquisition or analysis conditions may influence measured intensity, shape, or texture patterns. Standardized protocols help improve comparability, while validation tests whether identified associations remain dependable before supporting biomarker development or cancer-management decisions.
A typical workflow begins with an image from computed tomography, magnetic resonance imaging, or positron emission tomography, followed by segmentation of the tumor region. The analysis then extracts quantitative intensity, shape, and texture measurements and applies statistical or machine-learning methods to identify useful patterns. These outputs can subsequently be examined alongside clinical or molecular information.
In cancer research, these models can support several distinct questions: whether tumors can be classified by imaging characteristics, whether patients can be separated into different risk groups, how prognosis may be estimated, and whether treatment response can be predicted. Their value lies in using measurable imaging patterns to investigate tumor behavior without relying solely on visual assessment.
Radiomics can contribute to noninvasive biomarker development by connecting imaging phenotypes with clinical or molecular data. Quantitative patterns derived from tumor images may help identify characteristics associated with classification, risk, prognosis, or treatment response. Such findings could support more individualized cancer management, but the proposed imaging markers require robust validation and consistent imaging practices before they are considered reliable.