Literature DB >> 2745189

The effect of patient-to-patient variability on the accuracy of predictive assays of tumor response to radiotherapy: a theoretical evaluation.

S L Tucker1, H D Thames.   

Abstract

Mathematical modeling was used to investigate the relative accuracy that might be expected from predictive assays of tumor response to radiotherapy based on one of the following four tumor characteristics: intrinsic tumor-cell radiosensitivity, doubling time of the clonogenic tumor cells, number of clonogens in the tumor at the start of treatment, and extent of hypoxia in the tumor. In particular, the influence of inter-tumor heterogeneity on predictive accuracy was investigated. Wide patient-to-patient variability in a tumor characteristic contributing to treatment response adds to the accuracy of a predictive assay based on that characteristic, but variability from patient to patient in factors influencing response, but not measured by the assay, has a confounding effect and reduces predictive accuracy. The results of this theoretical study suggest that predictive assays based on intrinsic tumor-cell radiosensitivity are much more likely to be correlated with clinical outcome than are assays based on clonogen doubling time, hypoxic fraction, or clonogen number, since individual differences in tumor radiosensitivity can seriously confound assays based on the other factors. It is concluded that it may be necessary to correct for individual differences in intrinsic radiosensitivity before predictive assays based on other tumor characteristics, such as potential doubling time, might have any detectable clinical significance.

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Year:  1989        PMID: 2745189     DOI: 10.1016/0360-3016(89)90382-9

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


  8 in total

Review 1.  Theory and practice of predictive assays in radiation therapy.

Authors:  N E Crompton; M Ozsahin; P Schweizer; B Larsson; U M Luetolf
Journal:  Strahlenther Onkol       Date:  1997-02       Impact factor: 3.621

2.  The lack of correlation between proliferation (Ki-67, PCNA, LI, Tpot), p53 expression and radiosensitivity for head and neck cancers.

Authors:  T Björk-Eriksson; C M West; E Cvetskovska; M Svensson; E Karlsson; B Magnusson; N J Slevin; S Edström; C Mercke
Journal:  Br J Cancer       Date:  1999-07       Impact factor: 7.640

3.  Tumour vascularity is a significant prognostic factor for cervix carcinoma treated with radiotherapy: independence from tumour radiosensitivity.

Authors:  R A Cooper; C M West; D P Wilks; J P Logue; S E Davidson; S A Roberts; R D Hunter
Journal:  Br J Cancer       Date:  1999-09       Impact factor: 7.640

4.  A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy.

Authors:  Qi-En He; Yi-Fan Tong; Zhou Ye; Li-Xia Gao; Yi-Zhi Zhang; Ling Wang; Kai Song
Journal:  Technol Cancer Res Treat       Date:  2020 Jan-Dec

5.  The in vitro radiosensitivity of human head and neck cancers.

Authors:  T Björk-Eriksson; C M West; E Karlsson; N J Slevin; S E Davidson; R D James; C Mercke
Journal:  Br J Cancer       Date:  1998-06       Impact factor: 7.640

6.  Intrinsic radiosensitivity and prediction of patient response to radiotherapy for carcinoma of the cervix.

Authors:  C M West; S E Davidson; S A Roberts; R D Hunter
Journal:  Br J Cancer       Date:  1993-10       Impact factor: 7.640

Review 7.  Optimal treatment and stochastic modeling of heterogeneous tumors.

Authors:  Hamidreza Badri; Kevin Leder
Journal:  Biol Direct       Date:  2016-08-23       Impact factor: 4.540

8.  Molecular signature of response to preoperative radiotherapy in locally advanced breast cancer.

Authors:  Miljana Tanić; Ana Krivokuća; Milena Čavić; Jasmina Mladenović; Vesna Plesinac Karapandžić; Stephan Beck; Siniša Radulović; Snezana Susnjar; Radmila Janković
Journal:  Radiat Oncol       Date:  2018-10-01       Impact factor: 3.481

  8 in total

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