Moffitt Cancer Center: Why we are building the first machine learning department in oncology

Share on facebook
Share on twitter
Share on linkedin
Share on email
Share on print

Imagine a future where cancer patients are able to receive the treatment they precisely need and not the treatment that is prescribed to all.

To access this subscriber-only content please log in or subscribe.

If your institution has a site license, log in with IP-login or register for a sponsored account.*
*Not all site licenses are enrolled in sponsored accounts.

Login Subscribe
Issam El Naqa, PhD
Chair, Department of Machine Learning, Moffitt Cancer Center
Dana Rollison, PhD
Vice president, chief data officer, associate center director of data science, Division chief, quantitative science, Moffitt Cancer Center
Table of Contents

YOU MAY BE INTERESTED IN

Recently, HHS Secretary Robert F. Kennedy Jr. posted a video montage featuring himself shirtless in jeans, working out with Kid Rock. The duo is in a blue-lit grotto with a cold plunge and sauna. Set to Kid Rock’s “Bawitdaba” and intercut with a selection of patriotic imagery, the video ends with the two men in a hot tub, chugging what appears to be milk.
In January, FDA released a draft guidance entitled “Minimal Residual Disease and Complete Response in Multiple Myeloma: Use as Endpoints to Support Accelerated Approval.” This release came roughly 20 months after the Oncologic Drugs Advisory Committee (ODAC) voted unanimously that minimal residual disease (MRD) negativity, in combination with complete response (CR), is an acceptable primary endpoint to support accelerated approval for multiple myeloma (MM) therapies. 
Issam El Naqa, PhD
Chair, Department of Machine Learning, Moffitt Cancer Center
Dana Rollison, PhD
Vice president, chief data officer, associate center director of data science, Division chief, quantitative science, Moffitt Cancer Center

Never miss an issue!

Get alerts for our award-winning coverage in your inbox.

Login