The researchers retrospectively analyzed patient scans from a 2002-2006 clinical trial conducted at Penn Medicine. For each woman, the group generated a "signal enhancement ratio" (SER) map and from it, extracted various imaging features in order to understand the relationship between those features and conventional biomarkers (such as gene mutations or hormone receptor status) and patient outcomes.
They found that their algorithm was able to successfully predict recurrence-free survival after 10 years. To validate their findings, the group compared their results to an independent sample of 163 patients with breast cancer from the publicly available Cancer Imaging Archive.
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While imaging may not completely replace the need for tumor biopsies, radiologic methods could augment what is currently the "gold standard" of care, Kontos said, by giving a more detailed profile of a patient's disease and guiding personalized treatment. Next steps for the research team will include expanding the analysis to a larger patient cohort and also further exploring which specific markers are more predictive of particular outcomes.
"We've just touched the tip of the iceberg," Kontos said. "Our results and the validation study give us confidence that there are many opportunities for these markers to be used in a prognostic and potentially a predictive setting."
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This study was supported by the National Cancer Institute at the National Institutes of Health. Additional Penn authors include Jennifer Rowland, Elizabeth S. McDonald, Lauren Pantalone, Eric A. Cohen, Aimilia Gastounioti, Michael Feldman, Mitchell D. Schnall, and Emily F. Conant.
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