Tumor control probability in radiation treatment

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.

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

“…Zaider et al, showed that there are numerous important factors that determine tumor response to radiation, such as cell cycling, interaction with the immune system, selection effects, spatial heterogeneity of the tumor and its capillary network, etc. (30) have not been taken into account using LQ-Poisson model. However, the EUD has the advantage of fewer model parameters compared to another TCP or NTCP models, and allows more clinical flexibility.…”

Stochastic Process Pharmacodynamics: Dose Timing in Neonatal Gentamicin Therapy as an Example

“…Tumor treatment planning, especially in the case of radiotherapy, uses many different stochastic process calculations, relevant on different time scales, from femtoseconds to years, as reviewed in (16)(17)(18)(19) and in the online AAPS Supplement section S.4.1. The influence on IIV of stochastic fluctuations at the single cell level is considered (16,18); an approach similar to the statistical methods of population PK/PD is also used to analyze IIV [reviewed, e.g., in (20)].…”

“…The influence on IIV of stochastic fluctuations at the single cell level is considered (16,18); an approach similar to the statistical methods of population PK/PD is also used to analyze IIV [reviewed, e.g., in (20)]. …”

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

“…A number of new localization and fixing techniques, dose calculation algorithms and treatment concepts make radiotherapy even more precise. With deeper understanding of the concepts of factors such as the biologically effective dose (BED), equivalent uniform dose (EUD), α/β, tumor control probability (TCP) and normal tissue complication probability (NTCP), biological responses could be translated into a means for radiation physics optimization (11)(12)(13). To conclude, there is an urgent clinical need for this new radiation model, and its feasibility is based on growing knowledge regarding radiation responses and advances in modern radiation physics and imaging technology.…”