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The deep learning model may offer enhanced sensitivity and specificity on MRI for patients with glioblastoma, according to preliminary research presented at the Society for Imaging Informatics in Medicine (SIIM) conference.
For patients with glioblastomas, timely decision-making and treatment are critical as the median prognosis can range between 16 to 20 months. A key diagnostic challenge in this patient population is differentiating between true tumor progression and temporary pseudoprogression caused by adjunctive use of temozolomide after surgical resection.
While conventional magnetic resonance imaging (MRI) is limited in this regard, the emergence of a deep learning model may offer promise for radiologists and physicians treating these patients, according to new research presented at the Society for Imaging Informatics in Medicine (SIIM) conference.
In a poster abstract presentation, researchers noted the assessment of patients with glioblastoma who had a second resection due to suspected recurrence based on imaging changes and reviewed T2 and contrast-enhanced T1 MRI scans taken after the first resection. Using these scans to help develop a deep learning model, they performed subsequent five-fold cross validation with 56 patients (29 patients with true tumor progression and 27 patients with pseudoprogression).
The cross-validation testing revealed a mean area under the curve (AUC) of .86, an average sensitivity of 92.7 and an average specificity of 79 percent.
While acknowledging the need for larger studies and external validation of the deep learning model, the researchers said the initial findings are promising for the care and treatment of patients with glioblastomas.
“Conventional MRI reading techniques cannot distinguish between (true tumor progression and psuedoprogression). Therefore, providing a reliable and consistent technique to distinguish chemoradiation-induced (psuedoprogression) from tumor recurrence would be highly beneficial,” wrote Mana Moassefi, MD, a research fellow affiliated with the Radiology Informatic Lab at the Mayo Clinic in Rochester, Mn., and colleagues.