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June 3, 2020

Ellison Institute Uses AI to Accelerate Cancer Diagnosis and Treatment

By: Guest Author

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By Margaret Lindquist, Senior Director, Content for Oracle Brand Marketing

Nearly 150 years after the introduction of modern tissue staining to detect cancer, doctors are still diagnosing the disease much the way they did then. They still look at each biopsy sample under a microscope and hone in on suspicious areas, although new technologies are now allowing them to identify the molecular markers, like DNA mutations, that indicate whether a patient would respond best to one therapy or another.

But that diagnostic odyssey is beginning to change, thanks in part to groundbreaking research conducted at the Lawrence J. Ellison Institute for Transformative Medicine of USC. There, the introduction of artificial intelligence into cancer research and treatment has the potential to revolutionize both oncology fields, just as AI is doing in transportation (self-driving cars and self-flying planes), education (chatbot advisers), finance (predictive investment models), and a range of other sectors.

“I asked a pathologist, ‘If there’s one thing a computer could do for you, what would it be?’ He said, ‘I want it to tell me where to look,’” says Dr. Dan Ruderman, director of analytics and machine learning at the institute and assistant professor of research medicine at the Keck School of Medicine at the University of Southern California. “Digital pathology is how the medical field is going to catch up with Silicon Valley. Computers are showing us that they can see things we can’t, so we’re taking these techniques that have been perfected for other domains and transferring all that knowledge over to pathology.”

The institute is scanning slides that show slices of biopsies and analyzing that visual data with AI algorithms trained to recognize areas of concern. After enough training, the algorithm will be able to not only recognize cancer, but even recommend a course of treatment. The use of deep learning and neural networks will make sophisticated diagnoses available even in developing countries, where there may be only one doctor in a region who has experience with diagnosing cancer.

“There’s a basic diagnostic slide that is taken for every patient—but not every place has someone who can read the slide and determine the subtype of cancer and be able to recommend a specific course of drugs,” Dr. Ruderman says. “If the AI can handle that, it will not only save these hospitals money, but it should also result in better patient care.”

Unique combination

The Ellison Institute is unique among research facilities, Dr. Ruderman notes, because it combines an established research group with an established clinical group, both focused on discovering new ways to diagnose and treat cancer.

The Ellison Institute is building a new, wireless 5G-enabled building in West Los Angeles that will be home to a community outreach team, a health policy think tank, educational sessions—even an art gallery. Most importantly, the new facility is designed to bring together researchers and patients, making it possible to follow a patient’s progress from diagnosis through outcome.

“The notion of bringing patients and researchers together is really novel,” Dr. Ruderman says. “Patients are able to tour the research labs and talk to researchers about what they're doing. Researchers will be better able to understand what patients are going through.”

Facilitated through the Oracle for Research program, the Ellison Institute’s extended research team also includes Oracle computing experts and technology resources, enabling Dr. Ruderman and other scientists to conduct computational experiments alongside their laboratory research.

“Oracle for Research was developed to enable researchers to use the power of cloud computing to solve some of the world’s hardest problems,” says Oracle for Research vice president Alison Derbenwick Miller. “We provide access to Oracle Cloud and to technical advisors who collaborate with researchers. At the Ellison Institute, this further expands the patient-doctor-researcher team and broadens the approach to improving patient diagnosis and treatment.”

Intense workloads are just the beginning

The 3D imaging at the center of the pathology research is computationally intense and requires massive amounts of storage. For example, the data for just 1,000 patients, with 100 images per patient at 10 gigabytes per image, requires 1 petabyte—or a million gigabytes—of storage. Patient data comes not only from the patients at the clinic, but also from public sources such as The Cancer Genome Atlas and the Australian Breast Cancer Tissue Bank.

To handle its intensive, scalable computing needs, the institute is using Oracle Autonomous Database, Oracle Cloud Infrastructure bare metal compute instances, and Oracle Cloud storage, as well as Oracle Cloud Infrastructure FastConnect for network connectivity.

“We've been building neural networks using Oracle Cloud, and now that we are working in three dimensions, we have much, much more data,” Dr. Ruderman says. “It's going to be taxing the system a lot more and requiring a lot more computation.”

We want to answer a very basic question—whether we can learn a lot more about a patient by looking at this added dimension in pathology.
-Dr. Dan Ruderman, director of analytics and machine learning at the institute
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“Using Oracle’s autonomous capabilities eliminates much of the manual IT labor that’s required for standard databases,” says Muhammad Suhail, Oracle Cloud Infrastructure principal product manager. “Dr. Ruderman’s team was using four CPUs, and they wanted to go to eight,” Suhail says. “All we did was tell them where to find the button. They clicked it, and now the data warehouse is running with eight CPUs without needing any downtime.”

Dr. Ruderman says the institute stopped worrying about scaling issues once it started using Oracle Autonomous Database. “Now I can think more about science and less about technology,” he says.

And there is more to come. The most revolutionary aspect of the institute’s work is still on the horizon: using AI not just for diagnosis, but also for experimentation—whereby, in effect, scientists are running experiments within the computer. For example, Ruderman says that the plan is to use artificial intelligence to interpret complex 3D patterns and determine whether those images provide more information than can be derived from 2D views. “We want to answer a very basic question—whether we can learn a lot more about a patient by looking at this added dimension in pathology,” says Dr. Ruderman.

Dr. Ruderman is particularly excited about applying the institute’s research to better the lives of clinic patients. “We want to understand everything about the patient pipeline. Rather than focusing on just the piece that we think is important, we focus on all the pieces,” he says. “It gives us perspective on the patient's journey and how we can improve it. I hope that everybody on my team would be able to answer this question: ‘How does your work impact a patient's care?’”

This unique vision of the future of patient care and research is a crucial benefit that Derbenwick Miller emphasizes. “We want to bring together patients, clinicians, researchers, and cloud computing into a single setting. The Ellison Institute’s approach seeks to rapidly advance and improve the diagnosis and treatment of cancer, and this should ultimately result in a better quality of life and prognosis for patients,” says Derbenwick Miller.

Previously published on Forbes.
This is a syndicated post, view the original post here

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