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

Idan Bressler, Dafna Ben Bashat, Yuval Buchsweiler, Orna Aizenstein, Dror Limon, Felix Bokestein, T. Deborah Blumenthal, Uri Nevo, Moran Artzi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)33-42
Number of pages10
JournalMagnetic Resonance Materials in Physics, Biology, and Medicine
Volume36
Issue number1
DOIs
StatePublished - Feb 2023

Funding

FundersFunder number
Sackler Faculty of Medicine, Tel-Aviv University

    Keywords

    • Classification
    • DCE
    • Glioblastoma
    • MRI
    • Treatment response assessment

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