04 Sep Math Algorithm Helps Predict Recurrence of Prostate Cancer
MedicalResearch.com Interview with:
Ilaria Stura PhD
Università degli Studi di Torino
Turin, Piedmont, Italy
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Man has always tried to predict the future, especially to prevent catastrophes, diseases and death. In this case, we want to prevent the ‘personal catastrophe’, i.e. the spread of the disease (recurrence of prostate cancer) in the patient. Our work therefore belongs to the so-called ‘personalized medicine’, a very important and innovative clinical approach.
In particular this study may potentially improve the quality of life of the patients and help the clinicians, since it could give valuable information to the urologist, for example reporting that the growth velocity of the tumor is increasing and that a relapse is expected within few months. With this information, the clinician could chose the best therapy for the patient (e.g. hormone or radio therapy) in order to stop the spread of the disease or, conversely, the use of drugs can be delayed if not necessary.
Obviously clinicians already try to do this, based on their experience, but our method provides further confidence in their ‘investigation’ work, since the algorithm is validated on data coming from a database much larger than his/her personal experience.
MedicalResearch.com: What should readers take away from your report?
Response: The most important thing is that this method could well predict the time interval of the relapse, helping the clinician to take the right decision about therapy.
MedicalResearch.com: What recommendations do you have for future research as a result of this study?
Response: We would like to improve the model reliability and test it using data from new patients and make the algorithm available from a larger number of people. To do this, we are collaborating with Professor Feng Dong of the University of Bedfordshire, implementing our model in the mobile app developed by their team, called ‘MyHealthAvatar’. A beta-release should be ready within a few months downloadable for free from Google Play. The app MyHealthAvatar is already downloadable: it contains different programs to monitor your heart, your activity and other parameters related to your health. We hope that our model will be implemented soon to be used both by clinicians and by patients (for free).
Moreover, we would like to include another adjuvant therapeutic modality, e.g. immunotherapy. This clinical approach is very popular in the USA but scarcely diffused in Europe yet, so cooperation with American clinical teams is very welcome lo validate new mathematical models on large clinical database.
MedicalResearch.com: Is there anything else you would like to add?
Response: We are working in collaboration with other mathematicians (Emma Perracchione) to test whether other growth models, e.g. the Gompertzian one, can work just as well. This last model is even more reliable but it requires very accurate data pre-processing based on more sophisticated mathematical tools. If someone is interested on this topic, we submitted a paper on Mathematics and Computer Simulation and we already published a preliminary study in the DRNA proceedings.
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