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Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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Radiation: Applications

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Positron Emission Tomography

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Related Experiment Video

Updated: Jun 26, 2026

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
05:45

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

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Radiomics in glioma: emerging trends and challenges.

Zihan Wang1, Lei Wang2, Yinyan Wang1

  • 1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Annals of Clinical and Translational Neurology
|February 4, 2025
PubMed
Summary

Radiomics, a neuroimaging analysis technique, enhances glioma understanding by integrating deep learning and multi-omics data. This approach improves tumor characterization, treatment response prediction, and addresses clinical translation challenges.

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Area of Science:

  • * Neuroscience
  • * Medical Imaging
  • * Artificial Intelligence

Background:

  • * Radiomics offers quantitative feature extraction for glioma analysis.
  • * Glioma research benefits from advanced imaging and computational techniques.

Purpose of the Study:

  • * To review radiomics applications, trends, and challenges in glioma.
  • * To explore the integration of deep learning and multi-omics data with radiomics.

Main Methods:

  • * Analysis of radiomics in conjunction with deep learning for enhanced segmentation and feature extraction.
  • * Incorporation of advanced imaging modalities (e.g., MRI, PET) for comprehensive tumor characterization.
  • * Integration of multi-omics data (genomics, proteomics, etc.) with radiomics for biological insights.

Main Results:

  • * Deep learning integration improves radiomics model accuracy and performance.
  • * Radiomics aids in understanding glioblastoma aggressiveness and immune microenvironment.
  • * Multi-omics integration enhances prediction of genetic mutations, prognosis, and treatment response.

Conclusions:

  • * Radiomics, enhanced by AI and multi-omics, shows significant potential in glioma research and clinical application.
  • * Addressing challenges in reproducibility, generalizability, and interpretability is key for clinical translation.