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Related Concept Videos

Metallic Solids02:37

Metallic Solids

20.5K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
20.5K
Structures of Solids02:22

Structures of Solids

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Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
17.6K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

20.0K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
20.0K
Molecular Comparison of Gases, Liquids, and Solids02:26

Molecular Comparison of Gases, Liquids, and Solids

54.5K
Particles in a solid are tightly packed together (fixed shape) and often arranged in a regular pattern; in a liquid, they are close together with no regular arrangement (no fixed shape); in a gas, they are far apart with no regular arrangement (no fixed shape). Particles in a solid vibrate about fixed positions (cannot flow) and do not generally move in relation to one another; in a liquid, they move past each other (can flow) but remain in essentially constant contact; in a gas, they move...
54.5K
Energy Bands in Solids01:01

Energy Bands in Solids

1.9K
Isolated atoms have discrete energy levels that are well described by the Bohr model. And, it quantifies the energy of an electron in a hydrogen atom as En. Higher quantum numbers 'n' yield less negative, closer electron energy levels.
 Band Formation:
When atoms are brought close together, as in a solid, these discrete energy levels begin to split due to the overlap of electron orbitals from adjacent atoms. This split occurs because of the Pauli exclusion principle, which states...
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Predictive Immune Modeling of Solid Tumors
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Predictive Immune Modeling of Solid Tumors

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Image-based biomarkers for solid tumor quantification.

Peter Savadjiev1, Jaron Chong2, Anthony Dohan2,3

  • 1Department of Diagnostic Radiology, McGill University, Montreal, QC, Canada.

European Radiology
|April 10, 2019
PubMed
Summary
This summary is machine-generated.

Image biomarkers are advancing rapidly for tumor analysis, but clinical use lags behind technology. Artificial intelligence is now enhancing tumor characterization and prediction for solid tumors.

Keywords:
Artificial intelligence (AI)BiomarkersComputer-assisted image interpretationComputer-assisted image processingDiagnostic imaging

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Image-based biomarkers have evolved significantly from manual measurements to sophisticated computational analyses.
  • Despite technological advancements, a gap persists between novel imaging biomarker techniques and their integration into routine clinical practice.
  • The field is gaining momentum due to interest in personalized medicine and the application of artificial intelligence (AI) to large medical datasets.

Purpose of the Study:

  • To review the current status of image biomarkers for solid tumor characterization and predictive quantification.
  • To provide an overview of validated imaging biomarkers currently used in clinical settings.
  • To explore recent AI-driven advancements, including radiomics and deep learning, for tumor analysis.

Main Methods:

  • Review of current literature on validated imaging biomarkers in clinical practice.
  • Analysis of artificial intelligence-based methods for tumor characterization, including radiomics and deep learning.
  • Discussion of technological developments in image acquisition, processing, and analysis algorithms.

Main Results:

  • Validated imaging biomarkers are established in clinical practice for tumor quantification and characterization.
  • AI-based methods like radiomics and deep learning show promise for advanced tumor analysis.
  • Image biomarkers offer non-invasive monitoring of disease progression and treatment response.

Conclusions:

  • Significant technological progress in image biomarkers necessitates bridging the gap to clinical application.
  • AI is a key driver in advancing the capabilities of image biomarkers for personalized medicine.
  • Further research and validation are crucial for widespread adoption of advanced imaging biomarkers in oncology.