Embracing AI's Promise in Ovarian Cancer Research: A Journey of Collaboration and Hope

By Audra Moran , President and CEO, Ovarian Cancer Research Alliance

As I reflect on the landscape of ovarian cancer research, I'm reminded of the poignant moments that have shaped our journey at Ovarian Cancer Research Alliance (OCRA). There's an undeniable sense of optimism amid the challenges, a hope fueled by collaboration and a belief in the potential of technology to transform lives.

The road to progress has been marked by both milestones and obstacles. We've witnessed the advent of drugs like PARP inhibitors, for example, acknowledging their impact on a subset of individuals battling this disease. This past year, we funded $9.2 million in research grants – more than ever in our history. Moreover, a new beacon of promise has emerged in ovarian cancer research—a vision rooted in harnessing the potential of artificial intelligence (AI) to drive transformative change.

One pivotal stride in our pursuit of progress is the conception of a Federated Data Network—a project close to our hearts. The essence of this initiative lies in our recognition of the dearth of comprehensive data sets in ovarian cancer research. The Data Network is an ambitious project poised to revolutionize ovarian cancer research, serving as a comprehensive repository, uniting diverse institutions and researchers under one virtual roof. The essence of the Data Network lies in transcending siloed data, fostering collaboration, and democratizing access to extensive ovarian cancer datasets—a crucial cornerstone in unleashing AI's potential.

We envision the Data Network as a collaborative platform, a 'storefront for researchers,' fostering data sharing and accessibility across diverse research entities, and providing the base from which AI can extrapolate patterns that can help us with early detection, prevention, and, eventually, a cure.

We have seen promising AI work in breast cancer research to improve screening accuracy, with other areas being explored as well. Large data sets analyzed by AI can often detect patterns that can lead to progress. Our hope is to enable these capabilities for ovarian cancer.

Large datasets are needed, but there isn't a comprehensive large data set for ovarian cancer. Institutions and researchers have data, but it’s typically siloed. Connecting those silos is how we're going to get somewhere. The Data Network will link different major institutions together so there will be a “storefront” of sorts for researchers to harness AI's potential in unraveling patterns within ovarian cancer data.

This is a huge step forward.

Of course, this can’t happen overnight. We have a Steering Committee and have created a roadmap with four phases and are now launching the first phase. We are starting with rare ovarian cancers, because it gives us a smaller dataset to work with at the outset. Our overall goal is to move to high-grade serous ovarian cancers, the most common type of ovarian cancer.

So far, the project has been extremely heartening. A huge consortium of rare cancer researchers have joined, so we are really excited about the project and the potential.

Another way we are harnessing the power of AI is our collaboration with Microsoft's AI For Health lab. This partnership allows us to fund work being done by researchers currently using AI in the field of ovarian cancer. For example, Memorial Sloan Kettering’s Sohrab Shah, PhD, and Daniel Heller, MD are using AI to potentially create a screening test for the disease, which doesn’t currently exist, as part of this exciting $60 million, five-year program.

AI, with its burgeoning capabilities, holds the promise of unraveling mysteries and charting new paths towards conquering ovarian cancer— paving the way for a future where ovarian cancer is no longer a formidable adversary, but a conquerable disease.