Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma

• Published in: Magma (New York, N.Y.) N.Y.), 2023-02, Vol.36 (1), p.33-42
• Main Authors: Bressler, Idan, Ben Bashat, Dafna, Buchsweiler, Yuval, Aizenstein, Orna, Limon, Dror, Bokestein, Felix, Blumenthal, T. Deborah, Nevo, Uri, Artzi, Moran
• Format: Article
• Language: English
• Subjects: Algorithms; Basic Science - Perfusion imaging; Biomedical Engineering and Bioengineering; Computer Appl. in Life Sciences; Contrast Media; Glioblastoma - diagnostic imaging; Glioma; Health Informatics; Humans; Imaging; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Radiology; Research Article; Solid State Physics
• ISSN: 1352-8661; 1352-8661
• DOI: 10.1007/s10334-022-01045-z

Objective Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI. Materials and methods The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome. Results Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist’s assessment, based on RANO criteria, was found in 11/12 cases. Conclusion The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.