Literature DB >> 21452694

Tumor control probability in radiation treatment.

Marco Zaider1, Leonid Hanin.   

Abstract

Patients undergoing radiation therapy (and their physicians alike) are concerned with the probability of cure (long-term recurrence-free survival, meaning the absence of a detectable or symptomatic tumor). This is not what current practice categorizes as "tumor control (TC);" instead, TC is taken to mean the extinction of clonogenic tumor cells at the end of treatment, a sufficient but not necessary condition for cure. In this review, we argue that TC thus defined has significant deficiencies. Most importantly, (1) it is an unobservable event and (2) elimination of all malignant clonogenic cells is, in some cases, unnecessary. In effect, within the existing biomedical paradigm, centered on the evolution of clonogenic malignant cells, full information about the long-term treatment outcome is contained in the distribution Pm(T) of the number of malignant cells m that remain clonogenic at the end of treatment and the birth and death rates of surviving tumor cells after treatment. Accordingly, plausible definitions of tumor control are invariably traceable to Pm(T). Many primary cancers, such as breast and prostate cancer, are not lethal per se; they kill through metastases. Therefore, an object of tumor control in such cases should be the prevention of metastatic spread of the disease. Our claim, accordingly, is that improvements in radiation therapy outcomes require a twofold approach: (a) Establish a link between survival time, where the events of interest are local recurrence or distant (metastatic) failure (cancer-free survival) or death (cancer-specific survival), and the distribution Pm(T) and (b) link Pm(T) to treatment planning (modality, total dose, and schedule of radiation) and tumor-specific parameters (initial number of clonogens, birth and spontaneous death rates during the treatment period, and parameters of the dose-response function). The biomedical, mathematical, and practical aspects of implementing this program are discussed.

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Year:  2011        PMID: 21452694     DOI: 10.1118/1.3521406

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  23 in total

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2.  The use of TCP based EUD to rank and compare lung radiotherapy plans: in-silico study to evaluate the correlation between TCP with physical quality indices.

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3.  Strategies to optimize radiotherapy based on biological responses of tumor and normal tissue.

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4.  A note on modeling of tumor regression for estimation of radiobiological parameters.

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5.  Insulin-like growth factor receptor-1 overexpression is associated with poor response of rectal cancers to radiotherapy.

Authors:  Xiao-Yu Wu; Zhen-Feng Wu; Qin-Hong Cao; Che Chen; Zhi-Wei Chen; Zhe Xu; Wei-Su Li; Fu-Kun Liu; Xue-Quan Yao; Gang Li
Journal:  World J Gastroenterol       Date:  2014-11-21       Impact factor: 5.742

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

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Review 7.  Cancer concepts and principles: primer for the interventional oncologist-part II.

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Journal:  J Vasc Interv Radiol       Date:  2013-06-28       Impact factor: 3.464

8.  Mathematical model for the thermal enhancement of radiation response: thermodynamic approach.

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Review 9.  The molecular basis of chemoradiosensitivity in rectal cancer: implications for personalized therapies.

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10.  Toward patient-specific, biologically optimized radiation therapy plans for the treatment of glioblastoma.

Authors:  David Corwin; Clay Holdsworth; Russell C Rockne; Andrew D Trister; Maciej M Mrugala; Jason K Rockhill; Robert D Stewart; Mark Phillips; Kristin R Swanson
Journal:  PLoS One       Date:  2013-11-12       Impact factor: 3.240

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