Neurospine.  2019 Dec;16(4):669-677. 10.14245/ns.1938402.201.

Predictive Analytics in Spine Oncology Research: First Steps, Limitations, and Future Directions

• Massaad E
• Fatima N
• Hadzipasic M
• Alvarez-Breckenridge C
• Shankar GM
• Shin JH

• KMID: 2469040
• DOI: http://doi.org/10.14245/ns.1938402.201

Abstract

The potential of big data analytics to improve the quality of care for patients with spine tumors is significant. At this moment, the application of big data analytics to oncology and spine surgery is at a nascent stage. As such, efforts are underway to advance data-driven oncologic care, improve patient outcomes, and guide clinical decision making. This is both relevant and critical in the practice of spine oncology as clinical decision making is often made in isolation looking at select variables deemed relevant by the physician. With rapidly evolving therapeutics in surgery, radiation, interventional radiology, and oncology, there is a need to better develop decision-making algorithms utilizing the vast data available for each patient. The challenges and limitations inherent to big data analyses are presented with an eye towards future directions.

Keyword

Predictive analytics; Machine learning; Artificial intelligence; Spine tumor; Spine metastases; Primary spine tumor

MeSH Terms

Artificial Intelligence
Clinical Decision-Making
Humans
Machine Learning
Radiology, Interventional
Spine*
Statistics as Topic