Use of Radiomics in Characterizing Tumor Hypoxia

Area of Science:

• Oncology
• Medical Imaging
• Radiomics

Background:

• Tumor hypoxia, characterized by limited oxygen, correlates with increased cancer aggressiveness, metastasis, and treatment resistance.
• Conventional hypoxia assessment methods are often invasive and impractical for routine clinical use.
• Radiomics offers a non-invasive alternative for evaluating tumor hypoxia from medical images.

Purpose of the Study:

• To explore the role of radiomics in assessing tumor hypoxia.
• To discuss various imaging techniques and machine learning applications in radiomics for hypoxia detection.
• To highlight challenges and future directions for clinical translation of radiomics in hypoxic tumors.

Main Methods:

• Review of imaging modalities including MRI, PET, and CT for hypoxia assessment.
• Introduction to radiomics, machine learning, and deep learning for quantitative feature extraction.
• Discussion of challenges such as imaging standardization and data availability.

Main Results:

• Radiomics enables non-invasive quantitative assessment of tumor hypoxia.
• Machine and deep learning enhance feature extraction for improved hypoxia visualization.
• Integration with multi-omics shows potential for personalized cancer treatment.

Conclusions:

• Radiomics is a promising tool for diagnosing and managing hypoxic tumors.
• Overcoming challenges in standardization and data availability is crucial for clinical adoption.
• Future integration with multi-omics can personalize cancer therapy for hypoxic conditions.