Modeling Radiotherapy Induced Normal Tissue Complications

An Overview beyond Phenomenological Models

Marco D'Andrea, Marcello Benassi, Lidia Strigari

Research output: Contribution to journalReview article

Abstract

An overview of radiotherapy (RT) induced normal tissue complication probability (NTCP) models is presented. NTCP models based on empirical and mechanistic approaches that describe a specific radiation induced late effect proposed over time for conventional RT are reviewed with particular emphasis on their basic assumptions and related mathematical translation and their weak and strong points.

Original languageEnglish
Article number2796186
JournalComputational and Mathematical Methods in Medicine
Volume2016
DOIs
Publication statusPublished - 2016

Fingerprint

Radiotherapy
Probability Model
Complications
Tissue
Modeling
Radiation
Model-based
Model

ASJC Scopus subject areas

  • Medicine(all)
  • Modelling and Simulation
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Applied Mathematics

Cite this

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title = "Modeling Radiotherapy Induced Normal Tissue Complications: An Overview beyond Phenomenological Models",
abstract = "An overview of radiotherapy (RT) induced normal tissue complication probability (NTCP) models is presented. NTCP models based on empirical and mechanistic approaches that describe a specific radiation induced late effect proposed over time for conventional RT are reviewed with particular emphasis on their basic assumptions and related mathematical translation and their weak and strong points.",
author = "Marco D'Andrea and Marcello Benassi and Lidia Strigari",
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AB - An overview of radiotherapy (RT) induced normal tissue complication probability (NTCP) models is presented. NTCP models based on empirical and mechanistic approaches that describe a specific radiation induced late effect proposed over time for conventional RT are reviewed with particular emphasis on their basic assumptions and related mathematical translation and their weak and strong points.

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