AI spots unidentified brain tumor on PET imaging

An AI algorithm designed for brain PET imaging found a glioblastoma in a patient that had gone undetected by physicians, according to a case reported February 15 in the Journal of Nuclear Medicine.“This incidental finding highlights the potential of AI-based decision support for patient management in terms of diagnostic and treatment planning based on amino acid PET,” noted lead author Philipp Lohmann, PhD, of Aachen University in Aachen, and colleagues.In brief, the deep learning-based AI model (called “JuST_BrainPET”) is designed to automatically segment metabolic tumor volume (MTV) from surrounding healthy tissue on brain PET imaging, a key step in medical imaging analysis, the researchers explained. The group previously described developing the AI model in an article published last year.In this case, a 43-year-old man underwent an MRI scan that showed no contrast enhancement, yet hyperintensities were apparent in the patient’s left thalamus and frontoparietal region. Thus, the clinicians performed an additional PET scan with an amino acid radiotracer (F-18 FET) for further diagnosis of a suspected glioma.Baseline scan (A), segmentation results (B), and follow-up scan (C) from patient with molecular glioblastoma. Baseline MRI showed FLAIR hyperintensities in left thalamus and frontoparietal region (white arrowheads). In contrast to expert segmentation, in which only left thalamic region showed slightly increased uptake (red contour), AI algorithm identified addit...

Source: AuntMinnie.com Headlines - February 19, 2024 Category: Radiology Authors: Will Morton Tags: Subspecialties Neuroradiology Source Type: news