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Updated: Jan 19, 2026

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Machine learning for radiomics-based multimodality and multiparametric modeling.

Lise Wei1, Sarah Osman2, Mathieu Hatt3

  • 1Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.

The Quarterly Journal of Nuclear Medicine and Molecular Imaging : Official Publication of the Italian Association of Nuclear Medicine (AIMN) [And] the International Association of Radiopharmacology (IAR), [And] Section of the Society Of
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Summary
This summary is machine-generated.

Multimodality medical imaging, combining anatomical and functional data, enhances tumor characterization. Advanced deep learning on these images improves patient outcome prediction for personalized cancer treatment.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • Multimodality medical imaging is increasingly used in clinical practice and research.
  • Combining anatomical and functional imaging (e.g., PET/CT, SPECT/CT) improves diagnostic specificity and target definition in oncology.
  • Recent advancements include fusing multiparametric MRI, PET tracers, and PET/MRI for comprehensive tumor phenotype characterization.

Purpose of the Study:

  • To present methods for multimodal image analysis for clinical decision-making using radiomics.
  • To explore both handcrafted feature (radiomic) and machine-learned feature (deep learning) based approaches.

Main Methods:

  • Implementation of radiomic-based multimodal image analysis.
  • Application of deep learning algorithms for machine-learned feature extraction from multimodal images.

Main Results:

  • Deep learning algorithms applied to multimodal images show superior performance compared to single-modality models.
  • Multimodal analysis using advanced machine learning aids in predicting clinical outcomes and prognosis.

Conclusions:

  • Multimodal image analysis, particularly with deep learning, offers significant potential for personalized cancer treatment.
  • These approaches can lead to improved patient outcomes through more accurate prognostication and prediction.