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Radiomics and Artificial Intelligence Landscape for [18F]FDG PET/CT in Multiple Myeloma.

Christos Sachpekidis1, Hartmut Goldschmidt2, Lars Edenbrandt3

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Radiomics and artificial intelligence (AI) offer promising solutions for standardizing [18F]FDG PET/CT interpretation in multiple myeloma (MM). These advanced methods aim to improve diagnostic accuracy and treatment response assessment in MM patients.

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Area of Science:

  • Medical Imaging
  • Oncology
  • Artificial Intelligence

Background:

  • ¹⁸F]FDG PET/CT is a high-performance imaging technique for multiple myeloma (MM), crucial for assessing treatment response.
  • However, complex bone marrow infiltration patterns in MM complicate PET/CT interpretation, affecting reproducibility and prognostic capabilities.
  • Current standardization methods for PET/CT interpretation and quantification in MM are insufficient, necessitating advanced approaches.

Purpose of the Study:

  • To review the emerging applications of radiomics and AI in standardizing [18F]FDG PET/CT interpretation for multiple myeloma.
  • To highlight the potential of these technologies in improving diagnostic and prognostic accuracy in MM management.

Main Methods:

  • Review of current literature on radiomics and AI applications in [18F]FDG PET/CT for MM.
  • Analysis of studies employing machine learning and deep learning for automated image analysis in MM.

Main Results:

  • Radiomics enables high-throughput mining of image-derived features for clinical decision-making in oncology.
  • AI, including machine learning and deep learning, shows potential for automated and standardized evaluation of imaging modalities like PET/CT.
  • Initial studies applying radiomics and AI to [18F]FDG PET/CT in MM demonstrate encouraging results for interpretation optimization.

Conclusions:

  • Radiomics and AI-based methods represent a promising avenue for optimizing and standardizing [18F]FDG PET/CT interpretation in multiple myeloma.
  • These advanced techniques offer a potential solution to the challenges of interobserver variability and limited diagnostic ability in MM.
  • Further research and validation are needed to fully integrate these tools into routine clinical practice for MM management.