Researchers at UC San Francisco and the University of Michigan have developed an AI approach that can predict, with a high degree of accuracy, where the cancer is likely to recur first.
In glioblastoma, most recurrences occur at or close to the tumor cavity.
The findings, published in Science Advances and supported by the National Institutes of Health, could eventually enable doctors to use targeted treatments in these areas before a new tumor becomes visible on MRI.
That AI score alone performed about as well as conventional pathology at predicting which areas would later develop recurrent tumors.
But when researchers combined the AI score with clinical, imaging and molecular data in six machine-learning models, they found that the best-performing model was significantly better at distinguishing between sites that would and would not develop recurrences.
The researchers also tested how precisely the model could pinpoint where recurrence would develop.
The model performed well at predicting whether cancer would return within 5 or 10 millimeters (0.2 or 0.4 inches) of the tissue that had been sampled.
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