Literature DB >> 2211246

Optimization of uncomplicated control for head and neck tumors.

A Agren1, A Brahme, I Turesson.   

Abstract

Almost 200 patients have been treated for head and neck tumors at two different dose levels. Based on the clinically observed probabilities for tumor control and fatal normal tissue complications at the two dose levels, the dose giving maximum uncomplicated control has retrospectively been calculated and compared with the clinical data. A Poisson statistical model for control and complications has been used including a correlation parameter, delta, to describe the fraction of patients where control and complications are statistically independent. The clinically observed probability of uncomplicated tumor control, P+, is consistent with only a small fraction of the patients treated being statistically independent (delta = 0.2 or 20%). Customarily, 100% of the patients are assumed to be statistically independent with regard to tumor control and normal tissue complications. More precisely, the clinical data are consistent, with almost 20% of the patients being significantly more sensitive to radiation since they gain local tumor control but simultaneously suffer fatal complications. An even larger fraction of the patients (almost 30%) seemed to be more resistant to radiation, showing neither serious treatment complications nor control of the local tumor growth. It is suggested that if these patient groups could be identified by a predictive assay for the radiation sensitivity of their normal tissues and preferably also for their tumors, the uncomplicated tumor control could be increased by about 20%. This figure is based on the actuarial survival of the patients and has been corrected for the inevitable uncertainty in dose delivery. It is also pointed out that about 20% of the patients can never be saved by a predictive assay because of the considerable statistical variance associated with the Poisson process and the eradication of the last clonogenic tumor cell. Finally, note that the possible existence of radiation sensitive and resistant patient groups is consistent with known genetic deficiencies such as ataxia telangiectasia for the sensitive patients and the existence of repair efficient head and neck tumors that are unusually efficient in repairing double strand breaks. If such sensitive and resistant patient groups do exist, it should be sufficient to perform a predictive assay on normal tissues alone avoiding the often impossible task of sampling the most radiation resistant tumor cell line.

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Mesh:

Year:  1990        PMID: 2211246     DOI: 10.1016/0360-3016(90)90037-k

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  26 in total

1.  Use of normal tissue complication probability models in the clinic.

Authors:  Lawrence B Marks; Ellen D Yorke; Andrew Jackson; Randall K Ten Haken; Louis S Constine; Avraham Eisbruch; Søren M Bentzen; Jiho Nam; Joseph O Deasy
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-01       Impact factor: 7.038

2.  Risk-adaptive optimization: selective boosting of high-risk tumor subvolumes.

Authors:  Yusung Kim; Wolfgang A Tomé
Journal:  Int J Radiat Oncol Biol Phys       Date:  2006-12-01       Impact factor: 7.038

3.  Impact of dose calculation models on radiotherapy outcomes and quality adjusted life years for lung cancer treatment: do we need to measure radiotherapy outcomes to tune the radiobiological parameters of a normal tissue complication probability model?

Authors:  Abdulhamid Chaikh; Nicolas Docquière; Pierre-Yves Bondiau; Jacques Balosso
Journal:  Transl Lung Cancer Res       Date:  2016-12

4.  SPIDERplan: A tool to support decision-making in radiation therapy treatment plan assessment.

Authors:  Tiago Ventura; Maria do Carmo Lopes; Brigida Costa Ferreira; Leila Khouri
Journal:  Rep Pract Oncol Radiother       Date:  2016-08-24

5.  Objective assessment of the effects of tumor motion in radiation therapy.

Authors:  Yijun Ding; Harrison H Barrett; Matthew A Kupinski; Yevgeniy Vinogradskiy; Moyed Miften; Bernard L Jones
Journal:  Med Phys       Date:  2019-06-07       Impact factor: 4.071

6.  On the Inclusion of Short-distance Bystander Effects into a Logistic Tumor Control Probability Model.

Authors:  David G Tempel; N Patrik Brodin; Wolfgang A Tomé
Journal:  Cureus       Date:  2018-01-01

7.  Tradeoffs for assuming rigid target motion in Mlc-based real time target tracking radiotherapy: a dosimetric and radiobiological analysis.

Authors:  T Roland; C Shi; Y Liu; R Crownover; P Mavroidis; N Papanikolaou
Journal:  Technol Cancer Res Treat       Date:  2010-04

8.  Optimization of radiotherapy fractionation schedules based on radiobiological functions.

Authors:  Fernando Pizarro; Araceli Hernández
Journal:  Br J Radiol       Date:  2017-08-22       Impact factor: 3.039

9.  Deep reinforcement learning for automated radiation adaptation in lung cancer.

Authors:  Huan-Hsin Tseng; Yi Luo; Sunan Cui; Jen-Tzung Chien; Randall K Ten Haken; Issam El Naqa
Journal:  Med Phys       Date:  2017-11-14       Impact factor: 4.071

10.  Personalized dose selection in radiation therapy using statistical models for toxicity and efficacy with dose and biomarkers as covariates.

Authors:  Matthew J Schipper; Jeremy M G Taylor; Randy TenHaken; Martha M Matuzak; Feng-Ming Kong; Theodore S Lawrence
Journal:  Stat Med       Date:  2014-08-27       Impact factor: 2.373

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