PURPOSE: The typically benign, but occasionally rapidly fatal clinical course of @entity3557 has raised the need for individual survival probability estimation, to tailor the treatment strategy exclusively to a given @entity1 . MATERIALS AND METHODS: A retrospective study was performed on 400 @entity3557 @entity1 with a median follow-up time of 7.1 years to establish a clinical database for uni- and multivariate analysis of the prognostic factors related to survival (Kaplan-Meier product limit method and Cox regression). For a more precise prognosis estimation, the effect of the most important clinical events were then investigated on the basis of a Markov renewal model. The basic concept of this approach is that each @entity1 has an individual disease course which (besides the initial clinical categories) is affected by special events, e.g. internal covariates (local/regional/distant relapses). On the supposition that these events and the cause-specific @entity204 are influenced by the same biological processes, the parameters of transient survival probability characterizing the speed of the course of the disease for each clinical event and their sequence were determined. The individual survival curves for each @entity1 were calculated by using these parameters and the independent significant clinical variables selected from multivariate studies, summation of which resulted in a mean cause-specific survival function valid for the entire group. On the basis of this Markov model, prediction of the cause-specific survival probability is possible for extrastudy cases, if it is supposed that the clinical events occur within new @entity1 in the same manner and with the similar probability as within the study population. RESULTS: The @entity1 's age, a distant @entity3 at presentation, the extent of the surgical intervention, the @entity5 size and extent (pT), the external irradiation dosage and the degree of TSH suppression proved to be statistically significant and independent prognostic factors with regard to cause-specific survival in multivariate studies. During follow-up, 14, 14, 9 and 12% of the @entity1 underwent local/regional/distant relapses or @entity259 -related @entity204 , respectively. Through use of the above six independent clinical variables and the parameters relating to the four clinical events and their interrelations, mean cause-specific survival probabilities of 88, 83 and 78% were determined at 10, 20 and 30 years, respectively. The survival-predicting software (PATHYPRE) written on the basis of the biostatistical model is available through Internet connections on the home page of the National Institute of Oncology, Budapest (www.oncol.hu). CONCLUSION: Prediction of the individual survival probability for extrastudy cases affords a rationale for individualization of the treatment of @entity3557 @entity1 .

Survival chance in XXXX in Hungary: individual survival probability estimation using the Markov method.

@entity3557 :: ('MESH:C536915', 'Disease') :: ['papillary thyroid cancer'] @entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity5 :: ('MESH:D009369', 'Disease') :: ['primary tumor'] @entity259 :: ('MESH:D013964', 'Disease') :: ['thyroid cancer'] @entity3 :: ('MESH:D009362', 'Disease') :: ['metastasis'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death']
@entity3557 :: (MESH:C536915,Disease) :: ['papillary thyroid cancer']
