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Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective.

Jérémy Dana1, Vincent Agnus2, Farid Ouhmich2

  • 1IHU of Strasbourg, Strasbourg, France; Inserm & University of Strasbourg UMR-S1110, Strasbourg, France; Faculty of Medicine, University of Paris, Paris, France.

Seminars in Nuclear Medicine
|October 16, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in medical imaging offers new quantitative biomarkers for precision oncology. Machine and deep learning can analyze complex data to noninvasively monitor tumors and guide treatment, improving patient outcomes.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Precision oncology currently lacks advanced imaging biomarkers beyond simple metrics like RECIST and SUV.
  • Tumor characterization and monitoring of intratumoral changes remain significant challenges in medical imaging.

Purpose of the Study:

  • To explore the potential of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), in advancing medical imaging for precision oncology.
  • To highlight the development of novel, automated, and reproducible quantitative imaging biomarkers.

Main Methods:

  • Utilizing supervised or unsupervised ML/DL algorithms to analyze heterogeneous medical imaging data.
  • Identifying and leveraging unrevealed structural patterns within complex datasets.
  • Developing human-free, reproducible, and automated quantitative imaging biomarkers.

Main Results:

  • AI algorithms can integrate diverse data for comprehensive tumor analysis.
  • ML/DL enable noninvasive monitoring of molecular expression and tumor progression.
  • AI facilitates anticipation of therapeutic failure and personalized treatment management.

Conclusions:

  • AI, particularly DL, promises to revolutionize medical imaging for precision oncology by providing advanced quantitative biomarkers.
  • Establishing quality standards, including data standardization, model transparency, and rigorous validation, is crucial for clinical adoption.
  • Interpretable AI models are essential for trust and integration into clinical workflows.