Predictive value of Tmax perfusion maps on final core in acute ischemic stroke: an observational single-center study

Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool

PURPOSE

The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision^® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM).

METHODS

GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features.

RESULTS

A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT.

CONCLUSIONS

AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.

Comparing the predictive value of quantitative magnetic resonance imaging parametric response mapping and conventional perfusion magnetic resonance imaging for clinical outcomes in patients with chronic ischemic stroke

Predicting clinical outcomes after stroke, using magnetic resonance imaging (MRI) measures, remains a challenge. The purpose of this study was to investigate the prediction of long-term clinical outcomes after ischemic stroke using parametric response mapping (PRM) based on perfusion MRI data. Multiparametric perfusion MRI datasets from 30 patients with chronic ischemic stroke were acquired at four-time points ranging from V2 (6  weeks) to V5 (7  months) after stroke onset. All perfusion MR parameters were analyzed using the classic whole-lesion approach and voxel-based PRM at each time point. The imaging biomarkers from each acquired MRI metric that was predictive of both neurological and functional outcomes were prospectively investigated. For predicting clinical outcomes at V5, it was identified that PRM_Tmax-, PRM_rCBV-, and PRM_rCBV+ at V3 were superior to the mean values of the corresponding maps at V3. We identified correlations between the clinical prognosis after stroke and MRI parameters, emphasizing the superiority of the PRM over the whole-lesion approach for predicting long-term clinical outcomes. This indicates that complementary information for the predictive assessment of clinical outcomes can be obtained using PRM analysis. Moreover, new insights into the heterogeneity of stroke lesions revealed by PRM can help optimize the accurate stratification of patients with stroke and guide rehabilitation.

Predictors of ghost infarct core on baseline computed tomography perfusion in stroke patients with successful recanalization after mechanical thrombectomy

OBJECTIVES

To assess the predictors of ghost infarct core (GIC) in stroke patients achieving successful recanalization after mechanical thrombectomy (MT), based on final infarct volume (FIV) calculated from follow-up diffusion-weighted imaging (DWI).

METHODS

A total of 115 consecutive stroke patients who had undergone baseline computed tomography perfusion (CTP) scan, achieved successful recanalization after MT, and finished follow-up DWI evaluation were retrospectively enrolled. Ischemic core volume was automatically generated from baseline CTP, and FIV was determined manually based on follow-up DWI. Stroke-related risk factors and demographic, clinical, imaging, and procedural data were collected and assessed. Univariate and multivariate analyses were applied to identify the predictors of GIC.

RESULTS

Of the 115 included patients (31 women and 84 men; median age, 66 years), 18 patients (15.7%) showed a GIC. The GIC group showed significantly shorter time interval from stroke onset to CTP scan and that from stroke onset to recanalization (both p < 0.001), but higher ischemic core volume (p < 0.001), hypoperfused area volume (p < 0.001), mismatch area volume (p = 0.006), and hypoperfusion ratio (p = 0.001) than the no-GIC group. In multivariate analysis, time interval from stroke onset to CTP scan (odds ratio [OR], 0.983; p = 0.005) and ischemic core volume (OR, 1.073; p < 0.001) were independently associated with the occurrence of GIC.

CONCLUSIONS

In stroke patients achieving successful recanalization after MT, time interval from stroke onset to CTP and ischemic core volume are associated with the occurrence of GIC. Patients cannot be excluded from MT solely based on baseline CTP-derived ischemic core volume, especially for patients with a shorter onset time.

KEY POINTS

• Ghost infarct core (GIC) was found in 15.7% of patients with acute ischemic stroke (AIS) in our study cohort. • GIC was associated with stroke onset time, volumetric parameters derived from CTP, and collateral status indicated by HIR. • Time interval from stroke onset to CTP scan and ischemic core volume were independent predictors of GIC.