Predictive dosimetry in radiopharmaceutical therapy using advanced artificial intelligence, physiologically based pharmacokinetic modeling, and imaging data

Cancer is a leading cause of death worldwide. One of the main ways to remove cancer cell is targeting them using beta and alpha particle radiation. These kinds of radiations can be reached to the cancer sites by attaching them to special drugs that have specific binding sites on their cells. However, there is no accurate method to find how much radiation dose is required to remove these cells. As such measuring accurate dose received by cancer cells is not a straightforward method. In this era, we need to personalize therapy in terms of prediction of the dose. In this study, we try to predict dose using AI and imaging data, as well as models that describe the behavior’s of drugs in the body.

Faculty Supervisor:

Arman Rahmim

Student:

Partner:

BC Cancer

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

The University of British Columbia

Program: