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Related Concept Videos

Tumor Immunotherapy01:27

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Artificial intelligence model for tumoral clinical decision support systems.

Guillermo Iglesias1, Edgar Talavera1, Jesús Troya2

  • 1Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Spain.

Computer Methods and Programs in Biomedicine
|May 29, 2024
PubMed
Summary

This study introduces an AI system for brain tumor diagnosis that retrieves similar cases using enriched image descriptors from binary data, improving accuracy and reducing costs without tumor segmentation.

Keywords:
Clinical decision support systemComparative diagnosticContent-based image retrievalDeep learningFeature extractionMagnetic resonance imaging

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Computational Pathology

Background:

  • Comparative diagnostics leverage existing medical data to aid in new patient evaluations.
  • Current AI models for medical image analysis often require complex tumor segmentation.
  • Costly and difficult segmentation processes limit the accessibility of advanced diagnostic tools.

Purpose of the Study:

  • To develop an AI system for retrieving similar brain tumor cases for enhanced diagnostic accuracy.
  • To generate accurate medical image representations focusing on patient-specific features and pathologies.
  • To eliminate the need for tumor segmentation by using binary information for enriched image descriptors.

Main Methods:

  • An AI model was developed to detect patient features and recommend similar cases from a database.
  • The system balances healthy and abnormal feature representation to improve generalization.
  • The AI utilizes binary information to create enriched image descriptors, bypassing segmentation.

Main Results:

  • The proposed AI architecture achieved a Dice coefficient of 0.474 in both tumoral and healthy regions, outperforming prior studies.
  • The model effectively extracts and combines anatomical and pathological features from brain Magnetic Resonances (MRs).
  • State-of-the-art results were achieved with reduced training costs due to reliance on less expensive label information.

Conclusions:

  • The presented AI architecture offers significant potential for improving diagnostic efficiency and accuracy in brain tumor treatment.
  • Further research is warranted to explore the broader applicability and optimization of this novel approach.
  • This AI-assisted image retrieval system promises to reduce costs and enhance patient care by acting as a transparent support tool.