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Envision a day that cancer clinicians can ask an app to advise on immuno-oncology (IO) treatment options for a patient. That day may not be far off. Big data, deep analytics, and predictive modeling methods are transforming how cancer clinicians weigh treatment options.

Immune-related adverse events (irAEs) are extremely common in patients being treated with checkpoint inhibitors for advanced melanoma. The type, quality, and severity of these adverse events, however, varies by treatment regimen and by patient.

The Association of Community Cancer Centers (ACCC) is the leading education and advocacy organization for the multidisciplinary cancer team. ACCC is a powerful network of 24,000 cancer care professionals from 2,100 hospitals and practices nationwide. ACCC is recognized as the premier provider of resources for the entire oncology care team. For more information, visit accc-cancer.org or call …

The promises of Big Data are intuitively appealing: (virtually) unlimited data that will enable us to answer (virtually) any questions that we may have. Unfortunately, by and of themselves, Big Data are rather useless. They require Deep Analytics: inquiring people equipped with engines of analysis to explore, discover, and invent.

Determining the best personalized treatment for a patient will require input from a team of physicians, ideally with access to a patient’s information over time and across multiple modalities. Collecting data in a consistent, secure, and scalable manner with the ability to share across disciplines will be vital to furthering personalized medicine.

As a genitourinary medical oncologist specializing in immunotherapy for kidney and bladder cancers, I am continually striving for more ways to connect with and learn from my patients. The emerging availability of immuno-oncology (IO) drugs for the conditions I treat, as well as many other cancer types, has generated tremendous excitement amongst patients and oncologists, but there still is so much …