Literature DB >> 2263727

How well is the probability of tumor cure after fractionated irradiation described by Poisson statistics?

S L Tucker1, H D Thames, J M Taylor.   

Abstract

The probability of tumor cure in a homogeneous population of tumors exposed to fractionated radiotherapy was modeled using numerical simulations and compared with the predictions of Poisson statistics, assuming exact knowledge of the relevant tumor parameters (clonogen number, radiosensitivity, and growth kinetics). The results show that although Poisson statistics (based on exact knowledge of all parameters) accurately describes the probability of tumor cure when no proliferation occurs during treatment, it underestimates the cure rate when proliferation does occur. In practice, however, the inaccuracy is not likely to be more than about 10%. When the tumor parameters are unknown and are estimated by fitting an empirical Poisson model to tumor-cure data from a homogeneous population of proliferative tumors, the resulting estimates of tumor growth rate and radiosensitivity accurately reflect the true values, but the estimate of initial clonogen number is biased downward. A new formula that is more accurate than Poisson statistics in predicting the probability of tumor cure when proliferation occurs during treatment is discussed.

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Year:  1990        PMID: 2263727

Source DB:  PubMed          Journal:  Radiat Res        ISSN: 0033-7587            Impact factor:   2.841


  15 in total

1.  Flexible Cure Rate Modeling Under Latent Activation Schemes.

Authors:  Freda Cooner; Sudipto Banerjee; Bradley P Carlin; Debajyoti Sinha
Journal:  J Am Stat Assoc       Date:  2007-06-01       Impact factor: 5.033

2.  Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models.

Authors:  A D Tsodikov; J G Ibrahim; A Y Yakovlev
Journal:  J Am Stat Assoc       Date:  2003-12-01       Impact factor: 5.033

3.  Modelling geographically referenced survival data with a cure fraction.

Authors:  Freda Cooner; Sudipto Banerjee; A Marshall McBean
Journal:  Stat Methods Med Res       Date:  2006-08       Impact factor: 3.021

4.  Stochastic process pharmacodynamics: dose timing in neonatal gentamicin therapy as an example.

Authors:  Tomas Radivoyevitch; Nopphon Siranart; Lynn Hlatky; Rainer Sachs
Journal:  AAPS J       Date:  2015-02-07       Impact factor: 4.009

Review 5.  Linear quadratic and tumour control probability modelling in external beam radiotherapy.

Authors:  S F C O'Rourke; H McAneney; T Hillen
Journal:  J Math Biol       Date:  2008-09-30       Impact factor: 2.259

6.  AAPM recommendations on dose prescription and reporting methods for permanent interstitial brachytherapy for prostate cancer: report of Task Group 137.

Authors:  Ravinder Nath; William S Bice; Wayne M Butler; Zhe Chen; Ali S Meigooni; Vrinda Narayana; Mark J Rivard; Yan Yu
Journal:  Med Phys       Date:  2009-11       Impact factor: 4.071

7.  Oxygen distributions partly explain the radiation response of human squamous cell carcinomas.

Authors:  P Okunieff; J de Bie; E P Dunphy; D J Terris; M Höckel
Journal:  Br J Cancer Suppl       Date:  1996-07

8.  The impact of prostate edema on cell survival and tumor control after permanent interstitial brachytherapy for early stage prostate cancers.

Authors:  Zhe Jay Chen; Kenneth Roberts; Roy Decker; Pradip Pathare; Sara Rockwell; Ravinder Nath
Journal:  Phys Med Biol       Date:  2011-07-19       Impact factor: 3.609

9.  Repopulation of interacting tumor cells during fractionated radiotherapy: stochastic modeling of the tumor control probability.

Authors:  Hatim Fakir; Lynn Hlatky; Huamin Li; Rainer Sachs
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

10.  Second cancers after fractionated radiotherapy: stochastic population dynamics effects.

Authors:  Rainer K Sachs; Igor Shuryak; David Brenner; Hatim Fakir; Lynn Hlatky; Philip Hahnfeldt
Journal:  J Theor Biol       Date:  2007-08-12       Impact factor: 2.691

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