Lifetime analyses frequently apply a parametric functional description from measured data of the Kaplan-Meier non-parametric estimate (KM) of the survival probability. The cumulative Weibull distribution function (WF)...Lifetime analyses frequently apply a parametric functional description from measured data of the Kaplan-Meier non-parametric estimate (KM) of the survival probability. The cumulative Weibull distribution function (WF) is the primary choice to parametrize the KM. but some others (e.g. Gompertz, logistic functions) are also widely applied. We show that the cumulative two-parametric Weibull function meets all requirements. The Weibull function is the consequence of the general self-organizing behavior of the survival, and consequently shows self-similar death-rate as a function of the time. The ontogenic universality as well as the universality of tumor-growth fits to WF. WF parametrization needs two independent parameters, which could be obtained from the median and mean values of KM estimate, which makes an easy parametric approximation of the KM plot. The entropy of the distribution and the other entropy descriptions are supporting the parametrization validity well. The goal is to find the most appropriate mining of the inherent information in KM-plots. The two-parameter WF fits to the non-parametric KM survival curve in a real study of 1180 cancer patients offering satisfactory description of the clinical results. Two of the 3 characteristic parameters of the KM plot (namely the points of median, mean or inflection) are enough to reconstruct the parametric fit, which gives support of the comparison of survival curves of different patient’s groups.展开更多
A heuristic stochastic solution of the Pennes equation is developed in this paper by applying the self-organizing, self-similar behaviour of living structures. The stochastic solution has a probability distribution th...A heuristic stochastic solution of the Pennes equation is developed in this paper by applying the self-organizing, self-similar behaviour of living structures. The stochastic solution has a probability distribution that fits well with the dynamic changes in the living objects concerned and eliminates the problem of the deterministic behaviour of the Pennes approach. The solution employs the Weibull two-parametric distribution which offers satisfactory delivery of the rate of temperature change by time. Applying the method to malignant tumours obtains certain benefits, increasing the efficacy of the distortion of the cancerous cells and avoiding doing harm to the healthy cells. Due to the robust heterogeneity of these living systems, we used thermal and bioelectromagnetic effects to distinguish the malignant defects, selecting them from the healthy cells. On a selective basis, we propose an optimal protocol using the provided energy optimally such that molecular changes destroy the malignant cells without a noticeable effect on their healthy counterparts.展开更多
We show that the processes described by Avrami functions are self-similar. A comparative function characterizes a self-similar process by a certain Avrami exponent. We define the self-similar categories of some well-k...We show that the processes described by Avrami functions are self-similar. A comparative function characterizes a self-similar process by a certain Avrami exponent. We define the self-similar categories of some well-known biological processes. The method to determine the Avrami exponent by choosing the comparative function is demonstrated on the diffusion model of the growth of nuclei. We generalize the results.展开更多
文摘Lifetime analyses frequently apply a parametric functional description from measured data of the Kaplan-Meier non-parametric estimate (KM) of the survival probability. The cumulative Weibull distribution function (WF) is the primary choice to parametrize the KM. but some others (e.g. Gompertz, logistic functions) are also widely applied. We show that the cumulative two-parametric Weibull function meets all requirements. The Weibull function is the consequence of the general self-organizing behavior of the survival, and consequently shows self-similar death-rate as a function of the time. The ontogenic universality as well as the universality of tumor-growth fits to WF. WF parametrization needs two independent parameters, which could be obtained from the median and mean values of KM estimate, which makes an easy parametric approximation of the KM plot. The entropy of the distribution and the other entropy descriptions are supporting the parametrization validity well. The goal is to find the most appropriate mining of the inherent information in KM-plots. The two-parameter WF fits to the non-parametric KM survival curve in a real study of 1180 cancer patients offering satisfactory description of the clinical results. Two of the 3 characteristic parameters of the KM plot (namely the points of median, mean or inflection) are enough to reconstruct the parametric fit, which gives support of the comparison of survival curves of different patient’s groups.
文摘A heuristic stochastic solution of the Pennes equation is developed in this paper by applying the self-organizing, self-similar behaviour of living structures. The stochastic solution has a probability distribution that fits well with the dynamic changes in the living objects concerned and eliminates the problem of the deterministic behaviour of the Pennes approach. The solution employs the Weibull two-parametric distribution which offers satisfactory delivery of the rate of temperature change by time. Applying the method to malignant tumours obtains certain benefits, increasing the efficacy of the distortion of the cancerous cells and avoiding doing harm to the healthy cells. Due to the robust heterogeneity of these living systems, we used thermal and bioelectromagnetic effects to distinguish the malignant defects, selecting them from the healthy cells. On a selective basis, we propose an optimal protocol using the provided energy optimally such that molecular changes destroy the malignant cells without a noticeable effect on their healthy counterparts.
文摘We show that the processes described by Avrami functions are self-similar. A comparative function characterizes a self-similar process by a certain Avrami exponent. We define the self-similar categories of some well-known biological processes. The method to determine the Avrami exponent by choosing the comparative function is demonstrated on the diffusion model of the growth of nuclei. We generalize the results.
