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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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[Radiomics and automation in radiotherapy].

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Artificial intelligence, specifically radiomics, is crucial for automating radiotherapy processes. This technology addresses increasing cancer cases and data challenges, enabling personalized treatment plans.

Keywords:
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Area of Science:

  • Oncology and Radiotherapy
  • Medical Physics
  • Artificial Intelligence in Healthcare

Background:

  • Rising global cancer incidence and aging populations increase workload in oncology and radiotherapy.
  • Shortages of skilled professionals, like medical physicists, exacerbate workload challenges.
  • Patients generate vast amounts of complex data, particularly from medical imaging, requiring advanced analysis.

Purpose of the Study:

  • To highlight the critical role of radiomics in automating radiotherapy workflows.
  • To emphasize radiomics' contribution to personalized and individualized cancer treatment.
  • To address the challenges posed by increasing data volume and limited human resources in radiotherapy.

Main Methods:

  • Utilizing artificial intelligence for automated data extraction from medical images.
  • Applying radiomics to analyze morphological and functional data from tumors.
  • Integrating AI-driven insights into treatment planning and patient management.

Main Results:

  • Demonstrated the potential of AI to handle large, complex datasets in radiotherapy.
  • Showcased radiomics as a key technology for extracting previously indiscernible image information.
  • Highlighted the feasibility of automating laborious data extraction processes.

Conclusions:

  • Radiomics is essential for automating processes in radiotherapy, particularly for treatment planning.
  • AI-driven radiomics facilitates a more personalized and individualized approach to cancer treatment.
  • The integration of radiomics is vital for managing increasing workloads and data complexity in oncology.