Significance: We were able to create highly acceptable MR based treatment plans using a CT-trained ML model for treatment planning, although clinically significant dose deviations from the CT based plans were observed.
no code implementations • 28 Jan 2021 • Michal Kazmierski, Mattea Welch, Sejin Kim, Chris McIntosh, Princess Margaret Head, Neck Cancer Group, Katrina Rey-McIntyre, Shao Hui Huang, Tirth Patel, Tony Tadic, Michael Milosevic, Fei-Fei Liu, Andrew Hope, Scott Bratman, Benjamin Haibe-Kains
We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis.
no code implementations • 28 Feb 2020 • Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.