Institute of Information Theory and Automation

Nuclear medicine - thyroid cancer

Model of activity kinetics

After oral administration of radioactive 131I to a patient, iodine is accummulated in thyroid gland. Its activity rapidly increases and then slowly decreases. Model of activity time course is useful for (i) prediction of thyroid activity in a near future, (ii) time integral of activity is proportional to a dose (i.e. energy of the radiation) absorbed in the tissue. Thyroid activity is measured once or twice a day and usually not much more than 3 such measurements are available. Furthermore, these data contain random and potentially other measurement errors. Because small amount of uncertain data, prior information has been balanced to decrease uncertainty of estimated model parameters and, on the other hand, not to overweight information carried by the data.

The time integral of thyroid activity has been estimated as a random quantity. Its distribution is used for dosimetric and radio-hygienic purposes and as an input quantity for statistical analyses as well.

Advisory system

Radiodestruction of thyroid tumour is achieved by administration of 131I with high activity. The aim is to destroy the target tumour but, on the other hand, to minimize secondary radiation risks. As the response of patients' organism to administered activity is individual, therapeutic activity must be administered individually as well.

The advisory system is based on probabilistic mixture describing a selected multidimensional subset of characteristic patients' data. Then the advisory mixture model is designed, reflecting the user request to minimize the administered activity with successful result of therapy. Advice for a specific patient is conditioned by his actual data obtained in diagnostic examination before the planned therapy.

The results demonstrate that it is crucial to collect wide enough data set for description by the probabilistic mixture. The advices corresponded to medical decisions in one category of the disease that was sufficiently described by the available data used for mixture estimation.

Next effort is focused on

  • collection of more data (variables and records),
  • inclusion of incomplete data records into mixture estimation,
  • dealing with mixtures of both continuous and discrete variables.

 


Contact:

L. Jirsa

Support of grants

  • Intelligent decision support of diagnosis and therapy in nuclear medicine by Bayesian processing of uncertain data and probabilistic mixtures, Academy of Sciences of the Czech Republic, 1ET1 007 50404, 2004-2007
  • Centre of applied research DAR, Ministry of Education, Youth and Sports of the Czech Republic, 1M6 798 555 601
  • Dynamic clustering: theory, algorithms and software, Grant Agency of the Czech Republic, 102/03/0049, 2002-2005
Responsible for information: AS
Last modification: 26.10.2012
Institute of Information Theory and Automation