Literature DB >> 11384067

Generalization of a model of tissue response to radiation based on the idea of functional subunits and binomial statistics.

P Stavrev1, N Stavreva, A Niemierko, M Goitein.   

Abstract

This work investigates the existing biological models describing the response of tumours and normal tissues to radiation, with the purpose of developing a general biological model of the response of tissue to radiation. Two different types of normal tissue behaviour have been postulated with respect to its response to radiation, namely critical element and critical volume behaviour. Based on the idea that an organ is composed of functional subunits, models have been developed describing these behaviours. However, these models describe the response of an individual, a particular patient or experimental animal, while the clinically or experimentally observed quantity is the population response. There is a need to extend the models to address the population response, based on the ideas we have about the individual response. We have attempted here to summarize and unify the existing individual models. Finally, the population models are investigated by fitting to pseudoexperimental sets of data and comparing them with each other in terms of goodness-of-fit and in terms of their power to recover the values of the population parameters.

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Mesh:

Year:  2001        PMID: 11384067     DOI: 10.1088/0031-9155/46/5/312

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  11 in total

1.  A comparison of HDR brachytherapy and IMRT techniques for dose escalation in prostate cancer: a radiobiological modeling study.

Authors:  M Fatyga; J F Williamson; N Dogan; D Todor; J V Siebers; R George; I Barani; M Hagan
Journal:  Med Phys       Date:  2009-09       Impact factor: 4.071

2.  Temporally feathered intensity-modulated radiation therapy: A planning technique to reduce normal tissue toxicity.

Authors:  Juan Carlos López Alfonso; Shireen Parsai; Nikhil Joshi; Andrew Godley; Chirag Shah; Shlomo A Koyfman; Jimmy J Caudell; Clifton D Fuller; Heiko Enderling; Jacob G Scott
Journal:  Med Phys       Date:  2018-06-08       Impact factor: 4.071

3.  Dose volume histogram analysis and comparison of different radiobiological models using in-house developed software.

Authors:  Arun S Oinam; Lakhwant Singh; Arvind Shukla; Sushmita Ghoshal; Rakesh Kapoor; Suresh C Sharma
Journal:  J Med Phys       Date:  2011-10

4.  Introducing the RadBioStat Educational Software: Computer-Assisted Teaching of the Random Nature of Cell Killing.

Authors:  A Safari; Smj Mortazavi; H Mozdarani
Journal:  J Biomed Phys Eng       Date:  2014-06-08

Review 5.  Modeling Radiotherapy Induced Normal Tissue Complications: An Overview beyond Phenomenological Models.

Authors:  Marco D'Andrea; Marcello Benassi; Lidia Strigari
Journal:  Comput Math Methods Med       Date:  2016-12-01       Impact factor: 2.238

6.  Influence of concentration, nanoparticle size, beam energy, and material on dose enhancement in radiation therapy.

Authors:  Chulhwan Hwang; Ja Mee Kim; JungHoon Kim
Journal:  J Radiat Res       Date:  2017-07-01       Impact factor: 2.724

7.  Analyzing adjuvant radiotherapy suggests a non monotonic radio-sensitivity over tumor volumes.

Authors:  Jack Y Yang; Andrzej Niemierko; Mary Qu Yang; Youping Deng
Journal:  BMC Genomics       Date:  2008-09-16       Impact factor: 3.969

Review 8.  Big Data Analytics for Prostate Radiotherapy.

Authors:  James Coates; Luis Souhami; Issam El Naqa
Journal:  Front Oncol       Date:  2016-06-14       Impact factor: 6.244

9.  A TCP-NTCP estimation module using DVHs and known radiobiological models and parameter sets.

Authors:  Brad Warkentin; Pavel Stavrev; Nadia Stavreva; Colin Field; B Gino Fallone
Journal:  J Appl Clin Med Phys       Date:  2004-01-01       Impact factor: 2.102

10.  Assessment of radiobiological metrics applied to patient-specific QA process of VMAT prostate treatments.

Authors:  Francisco Clemente-Gutiérrez; Consuelo Pérez-Vara; María H Clavo-Herranz; Concepción López-Carrizosa; José Pérez-Regadera; Carmen Ibáñez-Villoslada
Journal:  J Appl Clin Med Phys       Date:  2016-03-08       Impact factor: 2.102

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