New mathematical model can help predict breast cancer growth, the appearance of metastases -
Double Attack on breast cancer
personalized medicine is an approach to health care in which medical procedures are tailored to the genetic, physiological of each patient, and the biochemical type, and other characteristics. Cancer treatment is a type of care that a personal approach is needed most often applied.
Some classical mathematical models were developed to describe the natural way of breast cancer, but they tend to treat the primary tumor and "combined mathematical model of breast cancer growth" secondary metastatic growth separately according to the paper http: // .. itas2015
But the natural history of cancer does not always end with the primary tumor removal. in many cases, secondary distant metastases can then develop and, if allowed to grow to a certain size, are likely to cause death. in addition to the comprehensive treatment of the primary tumor, cancer patients are now assessed for secondary metastatic growth. The mathematical models used to facilitate such evaluation should be able to describe the steps accurately:
calculation of the Invisible
by developing a new mathematical model Neznanov Tyuryumina and aimed to improve the prediction of cancer growth. In particular they aim to:
Their combined model describes both primary tumor growth and metastasis by side histological stages and helps predict survival prognosis. "Right corresponding to the official classification of breast cancer, we found our model to be quite accurate in correlating the size of the primary tumor with patient survival prognosis," says Neznanov. "As already known, the removal of the primary tumor is often followed by the latent growth of secondary metastases, our model clearly shows how the rate of five-year survival depends on the size of the primary tumor in patients breast cancer. " In particular, the model can give an indication of when the metastatic cells are likely to occur, depending on the size of the primary tumor.
In order to facilitate the practical application of this combined model, a software tool has been developed which should also be used in other research - for example, allowing simple addition of new parameters for prediction more accurate survival prognosis patients. The software is integrated with a source database and the prediction results. Just two steps from the primary tumor is needed as the minimum basic data for the model.
The model and software implementing it can improve the accuracy of prediction of breast cancer development and patient outcomes, which in turn can help detect secondary metastases.
Tested a relatively small amount of clinical data, the new model showed better performance than existing tools. First results of the study were presented at the conference "Information Technologies and 2015 systems." However, according Tyuryumina, this is only the first step. The researchers now plan to test the combined model of the large amounts of clinical data, consult cancer specialists to further improve its applicability, and integrate the software with other clinical data analysis tools used in oncology.
personalized medicine is an approach to health care in which medical procedures are tailored to the genetic, physiological of each patient, and the biochemical type, and other characteristics. Cancer treatment is a type of care that a personal approach is needed most often applied.
Some classical mathematical models were developed to describe the natural way of breast cancer, but they tend to treat the primary tumor and "combined mathematical model of breast cancer growth" secondary metastatic growth separately according to the paper http: // .. itas2015
But the natural history of cancer does not always end with the primary tumor removal. in many cases, secondary distant metastases can then develop and, if allowed to grow to a certain size, are likely to cause death. in addition to the comprehensive treatment of the primary tumor, cancer patients are now assessed for secondary metastatic growth. The mathematical models used to facilitate such evaluation should be able to describe the steps accurately:
- latency period of growth of the primary tumor;
- visible primary tumor, its diagnosis and removal;
- latency remote metastatic growth; and
- visible secondary metastases, diagnosis and treatment, and patient outcomes, including death.
calculation of the Invisible
by developing a new mathematical model Neznanov Tyuryumina and aimed to improve the prediction of cancer growth. In particular they aim to:
- examination known mathematical models to describe the growth of the primary tumor and secondary metastases in breast cancer;
- develop a comprehensive mathematical model of primary and secondary tumors develop metastases for this type of cancer (combined model);
- identify critical periods in the context of the primary tumor and the development of secondary metastases, which can affect the survival prognosis;
- to implement the combined model as a software solution; and
- to determine the scope of applicability and areas of the combined model for other research.
Their combined model describes both primary tumor growth and metastasis by side histological stages and helps predict survival prognosis. "Right corresponding to the official classification of breast cancer, we found our model to be quite accurate in correlating the size of the primary tumor with patient survival prognosis," says Neznanov. "As already known, the removal of the primary tumor is often followed by the latent growth of secondary metastases, our model clearly shows how the rate of five-year survival depends on the size of the primary tumor in patients breast cancer. " In particular, the model can give an indication of when the metastatic cells are likely to occur, depending on the size of the primary tumor.
In order to facilitate the practical application of this combined model, a software tool has been developed which should also be used in other research - for example, allowing simple addition of new parameters for prediction more accurate survival prognosis patients. The software is integrated with a source database and the prediction results. Just two steps from the primary tumor is needed as the minimum basic data for the model.
The model and software implementing it can improve the accuracy of prediction of breast cancer development and patient outcomes, which in turn can help detect secondary metastases.
Tested a relatively small amount of clinical data, the new model showed better performance than existing tools. First results of the study were presented at the conference "Information Technologies and 2015 systems." However, according Tyuryumina, this is only the first step. The researchers now plan to test the combined model of the large amounts of clinical data, consult cancer specialists to further improve its applicability, and integrate the software with other clinical data analysis tools used in oncology.
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