Why Radiation Oncologists Need AI for Clinical Trials
Recruiting patients for interventional studies has always been a challenge. In oncology, it can be significantly more difficult to find the right patients at the right time for the right clinical trial.
Generally, about 30-40% of oncology clinical trials are unsuccessful because they fail to recruit enough patients to complete the trial. This is devastating because 30% of cancer patients are eligible for a clinical trial – and access to a new, promising therapy – but only about 4% of cancer patients end up in a clinical trial. There are a variety of reasons people do not participate in clinical trials such as toxicity concerns, financial reasons, insurance coverage, travel time, acting as a caregiver and a multitude of other reasons.
These missed opportunities would change lives because drug development in oncology has been especially strong. “Many of our drug trials involve the most promising agents we’ve seen,” says Tufia Haddad, an oncology researcher at the Mayo Clinic.
With radiation oncology studies, it can be especially difficult. There are many contributing factors. Those patients who are typically eligible for radiation oncology clinical trials have often already been through months of treatments and procedures before they are visible to radiation oncologists through the typical hospital or healthcare system. This is problematic because these specialists have treatments available for cancer patients designed for a couple months after they’ve been diagnosed (and before they’ve gone through extensive chemotherapy or other therapies).
Promising New Treatments for Eligible Patients
New breakthrough treatments such as proton beam therapy are a perfect example. This novel therapy precisely targets high doses of radiation to the tumor and ensures nothing travels beyond it. It’s promising for tumors located next to vital organs or for cancer in children who are more sensitive to radiation therapy.
There are hundreds of clinical trials for proton beam therapy recruiting for patients. While this is an incredibly exciting development that will offer patients a new option in the future, right now patients are needed to validate the treatment for various conditions. Unfortunately, not all patients who have already gone through extensive oncology treatments are eligible.
Eligible patients may be rejected for proton beam therapies for various reasons. For instance, it’s common for eligible breast cancer patients to find their insurance carriers reject this type of therapy and are denied coverage. And while Medicare often covers proton beam therapy, the inclusion criteria of Medicare patient over 65 years old “significantly limits the number of patients who can participate in the trials and may reduce the generalizability of the results”.
How AI Comes into Play
We understand it’s always important to find the right patient at the right time for clinical trials, but – when it comes to radiation oncologists where the patient pool is smaller, and the timing is critical – they must catch patients before they go through traditional cancer treatments and are no longer eligible for clinical trials.
Technology – such as Deep 6 AI – is helping. New and promising screening tools exist to enhance community participation and find more patients at NCI-funded radiation oncology centers. In the article cited above, the project found patients who were motivated to participate and resulted in a high accrual rate for the clinical trials. After launching the external beam radiation therapy, the biggest challenge the project faced was a shortage of clinical trials for community members.
This is just one reason why companies and systems like Deep 6 exist. We help doctors, such as radiation oncologists, identify the right patients for clinical trials earlier in the process and gain access to the most promising therapies in clinical trials.
We’re proud to help physicians find patients for their clinical trials, allow patients access to breakthrough therapies and see more clinical trials successfully complete with our software.
Interested in learning more? We’re always excited to explain how our category-creating software is helping patients access new therapies. Reach out to us here.
