Assessment of three MR perfusion software packages in predicting final infarct volume after mechanical thrombectomy

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Bani-Sadr, Alexandre | Cho, Tae-Hee | Cappucci, Matteo | Hermier, Marc | Ameli, Roxana | Filip, Andrea | Riva, Roberto | Derex, Laurent | de Bourguignon, Charles | Mechtouff, Laura | Eker, Omer | Nighoghossian, Norbert | Berthezène, Yves

Edité par CCSD ; BMJ Journals -

International audience. Aims To evaluate the performance of three MR perfusion software packages (A: RAPID; B: OleaSphere; and C: Philips) in predicting final infarct volume (FIV). Methods This cohort study included patients treated with mechanical thrombectomy following an admission MRI and undergoing a follow-up MRI. Admission MRIs were post-processed by three packages to quantify ischemic core and perfusion deficit volume (PDV). Automatic package outputs (uncorrected volumes) were collected and corrected by an expert. Successful revascularization was defined as a modified Thrombolysis in Cerebral Infarction (mTICI) score ≥2B. Uncorrected and corrected volumes were compared between each package and with FIV according to mTICI score. Results Ninety-four patients were included, of whom 67 (71.28%) had a mTICI score ≥2B. In patients with successful revascularization, ischemic core volumes did not differ significantly from FIV regardless of the package used for uncorrected and corrected volumes (p>0.15). Conversely, assessment of PDV showed significant differences for uncorrected volumes. In patients with unsuccessful revascularization, the uncorrected PDV of packages A (median absolute difference −40.9 mL) and B (median absolute difference −67.0 mL) overestimated FIV to a lesser degree than package C (median absolute difference −118.7 mL; p=0.03 and p=0.12, respectively). After correction, PDV did not differ significantly from FIV for all three packages (p≥0.99). Conclusions Automated MRI perfusion software packages estimate FIV with high variability in measurement despite using the same dataset. This highlights the need for routine expert evaluation and correction of automated package output data for appropriate patient management.

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