Computational infrastructure for the real-time patient-specific treatment of cancer

Minimally invasive treatments of cancer are key to improving posttreatment quality of life. ermal therapies delivered under various treatment modalities are a form of minimally invasive cancer treatment that has the potential to become an eective option to eradicate the disease, maintain the functionality of infected organs, and minimize complications and relapse. However, the ability to control the energy deposition to prevent damage to adjacent healthy tissue is a limiting factor in all forms of thermal therapies [1], including cryotherapy, microwave, radio frequency, ultrasound, and laser. e combination of image guidance with computational prediction has the potential to allow unprecedented control over the bioheat transfer. Image guidance facilitates real-time treatment monitoring through temperature feedback during treatment delivery [2,3], and highperformance numerical implementations of mathematical bioheat transfer models can use the current-time thermal-imaging data to predict the outcome of the treatment minutes in advance [4].