Abstract

Glioma constitutes the most common type of primary brain tumor with a dismal survival, often measured in terms of months or years. The thin line between treatment effectiveness 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 findings 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, significant 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 artificial intelligence already exceeds clinical expertise in a variety of applications, clinicians remain responsible for interpreting the implications of, and acting upon, each prediction.

Highlights

  • Joeky Tamba Senders,1,2 Maya Harary,1 Brittany Morgan Stopa,1 Patrick Staples,3 Marike Lianne Daphne Broekman,1,4 Timothy Richard Smith,1 William Brian Gormley,1 and Omar Arnaout 1

  • For much of the history of scientific inquiry, classical frequentist statistics have formed the basis of data analysis, with randomized clinical trial (RCT) design forming the pinnacle of evidence-based medicine [5]

  • Predicted Survival for and enrolling clinical trials for glioblastoma, which employ a range of treatment modalities including investigational drugs, biologics, standard-of-care treatment, radiation, and surgical intervention to try to find an optimal cure for glioblastoma

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Summary

Review

Glioma constitutes the most common type of primary malignant brain tumor with an incidence of over 20,000 new cases a year in the United States[1]. Gliomas are subgrouped into low grade gliomas (LGG) and high-grade gliomas (HGG) which include glioblastoma, on the basis of tumor genetic and molecular markers [3]. They are noncurative, due to the aggressive nature of the tumor. The current median expected survival after diagnosis in glioma patients remains, solely a few months for HGGs, and years for LGGs, despite optimal treatment [1].

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