IBM analytics solving cancer
PORTLAND, Ore. -- IBM's medical diagnostics analytics are not exactly a 'cure' for cancer, but they are aiming to lower the cost of a promising new remedy for destroying existing tumors. The new technique uses a high-energy particle accelerator -- a room-sized version of the gigantic accelerators used to unravel physics -- that directs a proton beam to precisely kill the cancer cells inside tumors, leaving adjacent tissue untouched. IBM Research (Austin, Texas) is aiming to reduce the cost of this promising new therapy, using software analytics running on a Power7 cluster supercomputer.The stakes are huge. Last year there were over 12 million cancer patients worldwide receiving various therapies, and that number is predicted to increase to 21 million by 2030. Unfortunately, today there are only 10 centers offering proton therapy, however 17 new ones are currently under construction at a cost of over $200 million each. Over $3.4 billion is being invested in current-generation proton accelerators, and smaller, less expensive accelerators are also being designed to make the technique more affordable.
The big bottleneck, however, is the computational workload required to utilize proton therapy.
Today proton therapy requires a long involved preparation process, starting with a magnetic resonance imaging (MRI) or computational tomography (CT) scan to identify the tumor's location, after which a bevy of doctors and technicians spend over a week mapping out exactly how to use a proton beam to destroy it. Unlike traditional radiation therapy, proton beams do not affect human tissue as they pass through it, only releasing their energy at the very end of the path, which must be carefully plotted to end precisely within the tumor.
Today it takes a team of doctors and technicians a week to map out the path for a proton beam to kill tumors, but in the meantime it grew, making success less likely. IBM's analytics running on a Power 730 cluster computer maps out the same proton beam path in 15 minutes. SOURCE: IBM Click on image to enlarge. |
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