New AI-Based Tool Better Matches Patients to Cancer Trials


New AI-Based Tool Better Matches Patients to Cancer Trials
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Real-world survival associated with anti-cancer therapies is often significantly lower than that reported in randomized controlled trials (RCTs). Now, a new study demonstrates that a first-of-its-kind, AI-based platform could help clinicians and patients assess whether and how much an individual patient may benefit from a particular therapy being tested in a clinical trial. Its creators believe this AI platform can help make better informed treatment decisions, understand the expected benefits of novel therapies, and plan future care.

Published in Nature Medicine, the study was led by Ravi B. Parikh, MD, medical director of the Data and Technology Applications Shared Resource at Winship Cancer Institute of Emory University. It was co-led by Qi Long, PhD, a professor of biostatistics, and computer and information science, and founding director of the Center for Cancer Data Science at the University of Pennsylvania.

Parikh and his colleagues developed TrialTranslator, a machine learning framework to “translate” clinical trial results to real-world populations. By emulating 11 landmark cancer clinical trials using real-world data, they were able to recapitulate actual clinical trial findings, thus enabling them to identify which distinct groups of patients may respond well to treatments in a clinical trial, and those that may not.

“We hope that this AI platform will provide a framework to help doctors and patients decide if the results of a clinical trial can apply to individual patients,” Parikh said. “Furthermore, this study may help researchers identify subgroups in whom novel treatments do not work, spurring newer clinical trials for those high-risk groups.”

Because clinical trials of potential new cancer treatments are limited because less than 10% of all cancer patients participate in a clinical trial. This means clinical trials often do not represent all patients with that cancer. Even if a clinical trial shows a novel treatment strategy has better outcomes than the standard of care, “there are many patients in whom the novel treatment does not work,” Parikh said.

“This framework and our open-source calculators will allow patients and doctors to decide whether results from phase III clinical trials are applicable to individual patients with cancer,” he said, adding that “this study offers a platform to analyze the real-world generalizability of other randomized trials, including trials that have had negative results.”

The team used a nationwide database of electronic health records (EHR) from Flatiron Health to emulate 11 landmark randomized controlled trials (studies that compare the effects of different treatments by randomly assigning participants to groups) that investigated anticancer regimens considered standard of care for the four most prevalent advanced solid malignancies in the United States: advanced non-small cell lung cancer, metastatic breast cancer, metastatic prostate cancer and metastatic colorectal cancer.

Patients with low- and medium-risk phenotypes, had survival times and treatment-associated survival benefits similar to patients observed in the randomized controlled trials. In contrast, those with high-risk phenotypes showed significantly lower survival times and treatment-associated survival benefits compared to the randomized controlled trials.

The team’s findings suggest that machine learning can identify groups of real-world patients in whom randomized controlled trial results are less generalizable. This means, they added, that “real-world patients likely have more heterogeneous prognoses than randomized controlled trial participants.”



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