Speaker
Kenneth L. Kehl, MD, MPH
Assistant Professor of Medicine and Physician
Dana-Farber Cancer Institute
Keywords
Artificial Intelligence; Cancer; Notification; Enrollment; Patient Identification
Key Points
- Historically, less than 10% of adults with cancer enroll in clinical trials. At the same time, many trials struggle to reach their accrual goals. One possible contributor is that many trials of novel therapies for cancer have specific molecular criteria.
- Dana Farber Cancer Institute (DFCI) developed MatchMiner, a computational matching tool, to connect patients to trials. However, identified patients often weren’t at a place in their treatment when information about trials was relevant. The research team was interested in whether they could train an artificial intelligence (AI) model to identify “trial-ready” patients.
- The team conducted an implementation pilot, providing clinicians and research staff with weekly spreadsheets containing predictions of clinical trial “readiness” as identified by AI. The majority of identified patients were found to be ineligible upon RN review. Of those who were eligible, the majority opted not to move forward with the trial referral. At the end of the 9-month pilot, 6 AI-identified patients had been consented and enrolled in a therapeutic trial.
- To assess the impact of AI-driven identification of trial-ready patients, the team launched OPTIONS (Optimizing Precision Trials with an artificial Intelligence driven Oncologist Notification System). The primary outcome of the trial was enrollment in any DFCI therapeutic clinical trial.
- Patients with solid tumors were randomized into either a control group, in which they could be identified by the standard MatchMiner workflow, or 1 of 2 intervention groups. In the intervention arms, treating oncologists for genomically-matched patients with progressive disease and anticipated changes in treatment were contacted via email. In group 3, patients who met the readiness criteria were manually reviewed before the oncologists were contacted.
- They found that, though the AI models successfully predicted which patients with active or progressive cancer may need treatment changes, sharing the trial information with oncologists did not increase trial enrollment.
- This intervention addressed 1 barrier to trial participation. Other barriers may include eligibility criteria that goes beyond genomics and recent progression; and factors related to patient or oncologist preference, such as the motivation for participating, the complexity of the trial, and time toxicity.
- Dr. Kehl concluded with a reminder that while AI can accelerate clinical cancer research by rapidly identifying clinical trial options for patients, impact requires integration. AI must be applied thoughtfully and continuously evaluated, and researchers should be aware of the pitfalls and shortcuts associated with the technology.
Discussion Themes
The DFCI team is currently working on MatchMiner-AI: an open-source tool that they hope will improve the accessibility of clinical trials for all patients by providing a list of relevant clinical trials. They’re running a pilot study focused on incorporating MatchMiner-AI with the historical tool.
It’s easier to train a model than it is to deploy it in a complicated healthcare context. Given that the tool performs as hoped, there are evidently implementation challenges that still need to be worked out.
The study team considered training the model on a more proximal task – i.e., “Predict whether this patient will enroll in a clinical trial.” However, they were concerned that this would introduce biases – a pertinent concern with AI models – based on which patients typically have the opportunity to enroll in clinical trials.
While there may be use cases in which providing the trial information directly to patients would be more efficient, this would need to be done carefully. Information about worsening cancer, for instance, is best contextualized in a conversation with an oncologist.