Principles of physics and advanced computational analyses and the future of cancer care

Physics-driven methods to characterize malignant tumors due to cancer as well as their response to therapy have traditionally taken a back-seat to selecting treatments based on biological sub-type cellular analyses. Newer opportunities to use physics principles combined with machine learning and other computational approaches, however, imply an exciting future for the application of physical science principles together with advanced mathematical methods in oncology. Two key applications include applying energy budget principles as a basis for simulation models of tumor response and applying least-action and continuity principles to fluid flow to better characterize tumor vasculature and blood oxygen characteristics. At the same time, new artificial intelligence / machine learning approaches enable, for the first time, useful quantifications of tumor changes in response to therapy. Another opportunity is that AI/ML methods can be used to provide a quantitative understanding of how multiple physical imaging modalities relate to each other, and furthermore how this can be used to define initial conditions and corrections for the prediction of treatment response. The near future of application of physical thinking in cancer research and therapy, when put together with advanced computational methods, is indeed bright.