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From Semantic Modeling to Precision Radiotherapy: An AI Framework Linking Radiobiology, Oncology, and Public Health

Fernando Gomes de Souza1,2,3, José Maria Aliaga4,5, Paulo C Duarte6,7

  • 1Instituto de Macromoléculas Professora Eloisa Mano, Universidade Federal do Rio de Janeiro (UFRJ), Centro de Tecnologia, Cidade Universitária, Rio de Janeiro 21941-598, Brazil.

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Summary
This summary is machine-generated.

Artificial intelligence mapped radiotherapy

Keywords:
AI-based semantic and temporal analysisDNA repair and molecular responseadvanced radiotherapy technologies (FLASH-RT, hadron therapy, voxel-level analytics)clinical-anatomical axisco-occurrence supergraphmechanistic-molecular axis

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Area of Science:

  • Radiotherapy
  • Radiobiology
  • Oncology
  • Precision Medicine

Background:

  • Over six decades, radiotherapy, radiobiology, and oncology research has expanded significantly.
  • Scientific evidence in these fields is vast yet fragmented.
  • Understanding the evolution of these interconnected domains is crucial.

Purpose of the Study:

  • To systematically map and interpret the conceptual and temporal development of radiotherapy, radiobiology, and oncology.
  • To highlight the integration of molecular mechanisms, clinical applications, and technological innovation within precision radiotherapy.
  • To utilize artificial intelligence (AI)-based methods for comprehensive analysis.

Main Methods:

  • A systematic review of 3343 articles (1964-2025) from Scopus, PubMed, and Web of Science.
  • AI-driven topic modeling (Latent Dirichlet Allocation) and co-occurrence network analysis.
  • Semantic and temporal analyses to identify research trends and connections.

Main Results:

  • Three distinct historical phases of research production were identified: limited (1964-1990), moderate (1991-2010), and exponential (2011-2024).
  • Two principal research axes emerged: clinical-anatomical (cancer sites, treatment, prognosis) and mechanistic-molecular (DNA repair, radiosensitivity, biomarkers).
  • Five operational classes defined from 2014-2025, including DNA repair, precision oncology, radiosensitivity, radioresistance, and advanced technologies (e.g., FLASH radiotherapy).

Conclusions:

  • AI analysis reveals radiotherapy as a mature, interconnected, and interdisciplinary field.
  • A Precision Radiotherapy Implementation Plan translates insights into clinical strategies.
  • These strategies aim to improve survival, reduce toxicity, and support equitable advanced cancer care policies.