New Delhi, Dec 27 (IANS) – Researchers from the United States have created and tested an artificial intelligence (AI) system that can non‑invasively predict whether oropharyngeal cancer, a form of head and neck cancer that appears in the throat, will spread.
Using computed tomography scan data, the AI model forecasts the likelihood that cancer cells have spread beyond lymph nodes into adjacent tissue—an event known as extranodal extension (ENE). ENE is a key factor in determining how aggressively a patient should be treated, yet it can only be confirmed after surgical removal of lymph nodes.
Senior author Benjamin Kann, from the Artificial Intelligence in Medicine (AIM) Programme at Mass General Brigham, noted the tool’s relevance. “Our tool may help identify which patients should receive multiple interventions or would be ideal candidates for clinical trials of intensive strategies such as immunotherapy or additional chemotherapy,” he said. “Our tool can also help identify which patients should undergo de‑intensification of treatment, such as surgery alone,” Kann added.
Published in the Journal of Clinical Oncology, the study evaluated CT images from 1,733 patients with oropharyngeal carcinoma. The AI model was able to predict uncontrolled cancer spread and poorer survival outcomes. When its predictions were combined with existing clinical risk factors, the accuracy of survival and spread predictions improved, offering a more individualized risk assessment.
“The AI tool enables the prediction of the number of lymph nodes with ENE, which could not be done before, and shows that it is a powerful, novel prognostic biomarker for oropharyngeal cancer that could be used to improve the current staging scheme and treatment planning,” Kann explained.
The findings suggest that this AI‑based tool could guide clinicians in deciding which patients are candidates for intensified therapy or, conversely, for lighter treatment regimens, potentially reducing unnecessary treatment burdens and tailoring care to each patient’s unique risk profile. rvt/
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