Author/Authors :
Senders, Joeky Tamba Department of Neurosurgery - Brigham and Women’s Hospital - Harvard Medical School - Boston, USA , Harary, Maya Department of Neurosurgery - Brigham and Women’s Hospital - Harvard Medical School - Boston, USA , Morgan Stopa, Brittany Department of Neurosurgery - Brigham and Women’s Hospital - Harvard Medical School - Boston, USA , Staples, Patrick Department of Biostatistics - Harvard T.H. Chan School of Public Health - Harvard University - Boston, USA , Daphne Broekman, Marike Lianne Department of Neurosurgery - Brigham and Women’s Hospital - Harvard Medical School - Boston, USA , Richard Smith, Timothy Department of Neurosurgery - Brigham and Women’s Hospital - Harvard Medical School - Boston, USA , Gormley, William Brian Department of Neurosurgery - Brigham and Women’s Hospital - Harvard Medical School - Boston, USA , Arnaout, Omar Department of Neurosurgery - Brigham and Women’s Hospital - Harvard Medical School - Boston, USA
Abstract :
Glioma constitutes the most common type of primary brain tumor with a dismal survival, ofen measured in terms of months or
years. Te thin line between treatment efectiveness and patient harm underpins the importance of tailoring clinical management
to the individual patient. Randomized trials have laid the foundation for many neuro-oncological guidelines. Despite this, their
fndings focus on group-level estimates. Given our current tools, we are limited in our ability to guide patients on what therapy
is best for them as individuals, or even how long they should expect to survive. Machine learning, however, promises to provide
the analytical support for personalizing treatment decisions, and deep learning allows clinicians to unlock insight from the vast
amount of unstructured data that is collected on glioma patients. Although these novel techniques have achieved astonishing
results across a variety of clinical applications, signifcant hurdles remain associated with the implementation of them in clinical
practice. Future challenges include the assembly of well-curated cross-institutional datasets, improvement of the interpretability of
machine learning models, and balancing novel evidence-based decision-making with the associated liability of automated inference.
Although artifcial intelligence already exceeds clinical expertise in a variety of applications, clinicians remain responsible for
interpreting the implications of, and acting upon, each prediction.
