Researchers Use Supercomputers to Predict Cancer Growth or Decline

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Cancer, the dreadful malady yet without a proper cure, has compelled physicians and scientists to constantly focus on treatments and methods to reverse or stall its progress. The main challenge for them is to be able to detect it right at the start and also map its progress accurately.

To that end, a team of researchers headed by Thomas Yankeelov, who is director at the Center for Computational Oncology, University of Texas at Austin (UT Austin) and also director of Cancer Imaging Research at Dell Medical School, have come up with computer models to understand how cancer will progress in specific patients. It will be done on the basis of tissue, cellular and subcellular protein signaling responses.

Latest Computer Models to Accurately Predict the Course of Cancer

The computer models are capable of predicting with far more accuracy than earlier models how brain tumors, also known as gliomas, will grow. Of late, the team of researchers have embarked on a clinical study to understand how cancer in an individual will progress after the completion of one cycle of therapy. By leveraging that prediction, they can predict the course of the treatment.

Thomas Yankeelov feels that current cancer research despite being data-rich, lacks governing laws and models. To solve the problem one also needs to mathematize cancer, i.e. unravel the fundamental formulas that show how different forms of cancer behave.

Hence they have developed mathematically complex models to predict the growth and decline of cancer and its reactions to different therapies.

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