Detecting pseudo versus true progression of glioblastoma via accurate quantitative DCE-MRI using point-of-care portable perfusion phantoms: a pilot study.

BACKGROUND

Currently, no definitive method reliably differentiates pseudoprogression from true progression. Misclassification can either halt effective therapy or prolong ineffective treatment. We hypothesized that the diagnostic accuracy could be improved using quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) after error correction via point-of-care portable perfusion phantoms (P4s). This study aimed to develop a P4 for quantitative DCE-MRI of the brain and enhance accuracy in distinguishing between pseudo and true glioblastoma progression.

METHODS

Twelve patients with potential glioblastoma progression after adjuvant chemoradiation therapy were recruited. Each subject underwent two DCE-MRI exams within a week using a single 3T MRI scanner. Quantitative DCE-MRI parameters were retrieved based on the extended Tofts model (ETM), Tofts model (TM), and shutter speed model (SSM) before and after P4-based error correction. The consistency of the pharmacokinetic (PK) parameter measurements was evaluated based on the within-subject coefficient of variation (wCV) before and after P4-based error correction. Glioblastoma progression status was determined using the Response Assessment in Neuro-Oncology (RANO) criteria about five months after DCE-MRI exams.

RESULTS

Among the participants, five had true progression, and seven had pseudoprogression. The wCVs of the measurement based on TM, ETM, and SSM were 22%, 22%, and 24%, respectively, before error correction but improved to 7%, 6%, and 8%, respectively, after correction. Similarly, their accuracies in differentiating between pseudo and true progression were 0.88 regardless of the PK models before error correction. However, those after error correction were improved to 100% in TM (or ETM) and 96% in SSM.

CONCLUSIONS

Following P4-based error correction, a quantitative DCE-MRI parameter, , demonstrated 100% accuracy in discriminating between pseudo and true progression when TM or ETM were employed.