Abstract
Purpose: The typically benign, but occasionally rapidly fatal clinical course of papillary thyroid cancer has raised the need for individual survival probability estimation, to tailor the treatment strategy exclusively to a given patient. Materials and methods: A retrospective study was performed on 400 papillary thyroid cancer patients 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 patient 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 death 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 patient 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 patients in the same manner and with the similar probability as within the study population. Results: The patient's age, a distant metastasis at presentation, the extent of the surgical intervention, the primary tumor 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 patients underwent local/regional/distant relapses or thyroid cancer-related death, 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 papillary thyroid cancer patients.
| Original language | English |
|---|---|
| Pages (from-to) | 203-212 |
| Number of pages | 10 |
| Journal | Radiotherapy and Oncology |
| Volume | 44 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Sep 1997 |
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Keywords
- Cox regression
- External irradiation
- Kaplan-Meier estimation
- Markov renewal model
- Papillary carcinoma
- PATHYPRE
- Prognostic factors
- Surgery
- Survival
- Thyroid
- TSH suppression
ASJC Scopus subject areas
- Oncology
- Radiology Nuclear Medicine and imaging
- Urology
Cite this
Survival chance in papillary thyroid cancer in Hungary : Individual survival probability estimation using the Markov method. / Ésik, O.; Tusnády, Gábor; Daubner, Kornél; Németh, György; Füzy, Márton; Szentirmay, Z.
In: Radiotherapy and Oncology, Vol. 44, No. 3, 09.1997, p. 203-212.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Survival chance in papillary thyroid cancer in Hungary
T2 - Individual survival probability estimation using the Markov method
AU - Ésik, O.
AU - Tusnády, Gábor
AU - Daubner, Kornél
AU - Németh, György
AU - Füzy, Márton
AU - Szentirmay, Z.
PY - 1997/9
Y1 - 1997/9
N2 - Purpose: The typically benign, but occasionally rapidly fatal clinical course of papillary thyroid cancer has raised the need for individual survival probability estimation, to tailor the treatment strategy exclusively to a given patient. Materials and methods: A retrospective study was performed on 400 papillary thyroid cancer patients 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 patient 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 death 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 patient 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 patients in the same manner and with the similar probability as within the study population. Results: The patient's age, a distant metastasis at presentation, the extent of the surgical intervention, the primary tumor 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 patients underwent local/regional/distant relapses or thyroid cancer-related death, 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 papillary thyroid cancer patients.
AB - Purpose: The typically benign, but occasionally rapidly fatal clinical course of papillary thyroid cancer has raised the need for individual survival probability estimation, to tailor the treatment strategy exclusively to a given patient. Materials and methods: A retrospective study was performed on 400 papillary thyroid cancer patients 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 patient 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 death 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 patient 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 patients in the same manner and with the similar probability as within the study population. Results: The patient's age, a distant metastasis at presentation, the extent of the surgical intervention, the primary tumor 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 patients underwent local/regional/distant relapses or thyroid cancer-related death, 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 papillary thyroid cancer patients.
KW - Cox regression
KW - External irradiation
KW - Kaplan-Meier estimation
KW - Markov renewal model
KW - Papillary carcinoma
KW - PATHYPRE
KW - Prognostic factors
KW - Surgery
KW - Survival
KW - Thyroid
KW - TSH suppression
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UR - http://www.scopus.com/inward/citedby.url?scp=0342601497&partnerID=8YFLogxK
U2 - 10.1016/S0167-8140(97)00098-4
DO - 10.1016/S0167-8140(97)00098-4
M3 - Article
VL - 44
SP - 203
EP - 212
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
SN - 0167-8140
IS - 3
ER -