Pushing the envelope?
MOMA presents an “intriguing new avenue of adding to how we think about and assess someone who has cancer,” Stacey Cohen, MD, an associate professor in the clinical research division of Fred Hutchinson Cancer Center at the University of Washington Medicine, Seattle, said in an interview.
However, the tool as it’s currently described appears primarily to duplicate what clinicians already are doing, which is considering a wide range of factors – including pathologic features, patient features and demographics, and the patient’s other medical illnesses – to develop a treatment plan within the context of current guidelines, noted Dr. Cohen, who was not involved in the project.
“I’m looking for these types of models to not just prognosticate an outcome but to really predict how someone should be treated, and to do that better than [using] standard clinical features,” Dr. Cohen said. “To some degree, they’re taking this AI model and trying to catch up to what we’re currently doing. Clearly, if they could do that, they can then push the envelope.”
Dr. Cohen acknowledged that a strength of using an AI platform is the speed at which it can provide its predictions in areas with few medical resources and few health care professionals – as long as the necessary imaging is available and physicians have a way to use the platform.
“On the one hand, I do see this as an opportunity to share the wealth of knowledge in a more rapid fashion, but I don’t think anybody is going to let a computer program dictate their treatment without a human medical oncologist being able to interpret that information,” Dr. Cohen said. “It still will require a lot of education by the users and not just by the people who are designing the study.”
Although the MOMA platform looked at multiple pathologic features in multiple cohorts, the results remain limited by the fact that the patients in those cohorts were treated decades ago, before many current treatments may have been available, Dr. Cohen said.
She also added that the cohorts did not have much ethnic diversity. In the NHS-HPFS, the largest cohort, 57% of the patients were White, and researchers lacked data on race for 42% of patients, so only about 1% of participants were of a known non-White race. Similarly, 47% of the TCGA patients were White and 41% had no data on race, leaving only 12% of patients from known, non-White racial backgrounds, including 10% Black or African American.
Additional studies that focus on specific patient populations are needed to evaluate the model’s applicability in clinical settings, the investigators note. More research is required to “identify the optimal prognostic prediction methods and enable personalized treatments and advance care planning,” they added.
These are the early days for this type of technology, Dr. Cohen noted.
“I’m very excited to see how this technology develops and how it could be potentially additive or improve upon our current treatment planning for patients,” she said.
Dr. Yu developed the invention “Quantitative Pathology Analysis and Diagnosis using Neural Networks,” whose patent is held by Harvard University, and has consulted for Curatio. One coauthor is a stakeholder and employee of Vertex Pharmaceuticals. The study’s funding sources included the National Institute of General Medical Sciences, the Google Research Scholar Award, the Blavatnik Center for Computational Biomedicine Award, the National Science and Technology Council Taiwan, and the National Center for High-performance Computing Taiwan. Dr. Cohen has advised or consulted for Natera.
A version of this article first appeared on Medscape.com.