Unsupervised Deep Learning for Blood-Brain Barrier Leakage Detection in Diffuse Glioma Using Dynamic Contrast-enhanced MRI

Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI, without requiring pharmacokinetic models and arterial input function estimation. Materials and Methods This retrospective study included data from patients who underwent dynamic contrast-enhanced MRI between April 2010 and December 2020. An autoencoder-based anomaly detection approach identified one-dimensional voxel-wise time-series abnormal signals through reconstruction residuals, separating them into residual leakage signals (RLSs) and residual vascular signals. The RLS maps were evaluated and compared with the volume transfer constant (<i>K</i>^trans) using the structural similarity index and correlation coefficient. Generalizability was tested on subsampled data, and isocitrate dehydrogenase (<i>IDH</i>) status classification performance was assessed using area under the receiver operating characteristic curve (AUC). Results A total of 274 patients (mean age, 54.4 years ± 14.6 [SD]; 164 male) were included in the study. RLS showed high structural similarity (structural similarity index, 0.91 ± 0.02) and correlation (<i>r</i> = 0.56; <i>P</i> < .001) with <i>K</i>^trans. On subsampled data, RLS maps showed better correlation with RLS values from the original data (0.89 vs 0.72; <i>P</i> < .001), higher peak signal-to-noise ratio (33.09 dB vs 28.94 dB; <i>P</i> < .001), and higher structural similarity index (0.92 vs 0.87; <i>P</i> < .001) compared with <i>K</i>^trans maps. RLS maps also outperformed <i>K</i>^trans maps in predicting <i>IDH</i> mutation status (AUC, 0.87 [95% CI: 0.83, 0.91] vs 0.81 [95% CI: 0.76, 0.85]; <i>P</i> = .02). Conclusion The unsupervised framework effectively detected blood-brain barrier leakage without pharmacokinetic models and arterial input function. <b>Keywords:</b> Dynamic Contrast-enhanced MRI, Unsupervised Learning, Feature Detection, Blood-Brain Barrier Leakage Detection <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Júdice de Mattos Farina and Kuriki in this issue.