“HPV-associated oropharynx cancer is now the most common type of this cancer. And while these patients tend to respond very well to surgery or
and radiation, there’s been a lot of interest in trying to figure out ways to de-escalate treatment, so patients can have fewer side effects and long-term issues that reduce quality of life,” said first author Benjamin Kann, MD. “An appealing strategy is to use a type of minimally invasive surgery for these patients, called
However, the presence of ENE is a risk factor for the cancer to return after surgery and for lower rates of survival overall, making patients with ENE poor candidates for TORS. “If ENE is found after surgery, those patients still need to receive a long course of chemotherapy and radiation, or trimodality therapy, which is associated with the worst complications and quality of life outcomes,” said Kann.
Improving Throat Cancer Outcomes with Artificial Intelligence
Historically, ENE has been very difficult to detect using traditional diagnostic imaging, so there have been a good number of patients who still require trimodality therapy, despite screening. “The unmet need and the impetus for using AI in this study was to see if we could do a better job at predicting whether ENE was present or not on a CT scan prior to treatment, so we can help select the appropriate patients for surgery or for chemotherapy and radiation,” said Kann.
Advertisement
For this study, the team conducted a retrospective evaluation of the AI algorithm’s performance, using pretreatment CTs and corresponding surgical pathology reports from ECOG-ACRIN Cancer Research Group E3311, a multicenter, phase 2 de-escalation trial.
“What was important about this study is it tested the algorithm in the context of a very large randomized clinical trial, where patients who were enrolled, by definition, were supposed to be screened out for having ENE, and yet a significant portion still ended up having ENE,” said Kann. “When we applied the algorithm to this population to see how it would have done in in predicting ENE, we found that it performed well with a high degree of accuracy better than all four expert head and neck radiologists.
“The main benefit seems to be an increase in sensitivity, or a lower percentage of missed ENE,” said Kann. “Ideally, better recognition of ENE in pre-treatment would result in a lower rate of trimodality therapy and improved quality of life for patients.”
Source: Eurekalert
