A team led by researchers at Dana-Farber Cancer Institute and Brigham General Hospital in Massachusetts has developed and validated a non-invasive artificial intelligence (AI)-based tool that can predict the likelihood that a patient's oropharyngeal cancer (a type of head and neck cancer that starts in the throat) will spread, thereby informing which patients should receive aggressive treatment. The study will be published in the Journal of Clinical Oncology.
“Our tool may help identify which patients should receive multiple interventions or be ideal candidates for clinical trials of intensive strategies such as immunotherapy or additional chemotherapy,” said lead author Benjamin Kang, MD, a radiation oncologist at Dana-Farber Cancer Institute and Brigham and Women's Hospital and a faculty member in the Artificial Intelligence (AIM) Program at the Massachusetts General Brigham. “Our tool can also help identify which patients should de-intensify treatment, such as surgery only.”
Treatments for oropharyngeal cancer, including a combination of surgery, radiation therapy, and chemotherapy, can be difficult to tolerate and can have lasting adverse effects. Therefore, it is important to identify subgroups of patients who may benefit from a more intensive or more intensive treatment approach. One way to accomplish this involves assessing whether a patient has pathological extranodal extension (ENE), which occurs when cancer cells invade the surrounding tissue beyond the lymph nodes. Currently, ENE can only be definitively diagnosed by surgically removing and examining the lymph nodes.
To provide a way to assess ENE before making treatment decisions, Kann et al. developed an AI-based tool. The tool captures image data from computed tomography scans and can predict the number of lymph nodes with ENE, which is an indicator of a patient's prognosis and likelihood of benefiting from intensive therapy. When applied to image scans of 1,733 oropharyngeal cancer patients, the tool was able to predict uncontrolled cancer spread and decreased patient survival. Integrating AI assessments with established clinical risk predictors has improved risk stratification, allowing more accurate predictions of individual patient survival and cancer spread.
“This AI tool allows prediction of lymph node count in ENE, which was previously not possible, and shows that this is a powerful novel prognostic biomarker for oropharyngeal cancer, which could be used to improve current staging schemes and treatment plans,” said Kann.
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