System Bits: Aug. 14

Machine-learning system determines the fewest, smallest doses that could still shrink brain tumors In an effort to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for glioblastoma, the most aggressive form of brain cancer, MIT researchers are employing novel machine-learning techniques. According to the team, glioblastoma is a malignant tumor that appears in the brain or spinal cord, with a prognosis for adults of no more than five years. Patients must endure a combination of radiation therapy and multiple drugs taken every month whereby medical professionals generally administer maximum safe drug doses to shrink the tumor as much as possible but these strong pharmaceuticals still cause debilitating side effects in patients. MIT researchers aim to improve the quality of life for patients suffering from glioblastoma, the most aggressive form of brain cancer, with a machine-learning model that makes chemotherapy and radiotherapy dosing regimens less toxic but still as effective as human-designed regimens. In a paper being presented at the 2018 Machine Learning for Healthcare conference at Stanford University, MIT Media Lab researchers detail a model that could make dosing regimens less toxic but still effective.