Prolonged venous transit on perfusion imaging is associated with higher odds of mortality in successfully reperfused patients with large vessel occlusion stroke.

BACKGROUND: Poor venous outflow (VO) profiles are associated with unfavorable outcomes in patients with acute ischemic stroke caused by large vessel occlusion (AIS-LVO), despite achieving successful reperfusion. The objective of this study is to assess the association between mortality and prolonged venous transit (PVT), a novel visual qualitative VO marker on CT perfusion (CTP) time to maximum (Tmax) maps. METHODS: We performed a retrospective analysis of prospectively collected data from consecutive adult patients with AIS-LVO with successful reperfusion (modified Thrombolysis in Cerebral Infarction 2b/2c/3). PVT+ was defined as Tmax ≥10 s timing on CTP Tmax maps in at least one of the following: superior sagittal sinus (proximal venous drainage) and/or torcula (deep venous drainage). PVT- was defined as lacking this in both regions. The primary outcome was mortality at 90 days. In a 1:1 propensity score-matched cohort, regressions were performed to determine the effect of PVT on 90-day mortality. RESULTS: In 127 patients of median (IQR) age 71 (64-81) years, mortality occurred in a significantly greater proportion of PVT+ patients than PVT- patients (32.5% vs 12.6%, P=0.01). This significant difference persisted after matching (P=0.03). PVT+ was associated with a significantly increased likelihood of 90-day mortality (OR 1.22 (95% CI 1.02 to 1.46), P=0.03) in the matched cohort. CONCLUSIONS: PVT+ was significantly associated with 90-day mortality despite successful reperfusion therapy in patients with AIS-LVO. PVT is a simple VO profile marker with potential as an adjunctive metric during acute evaluation of AIS-LVO patients. Future studies will expand our understanding of using PVT in the evaluation of patients with AIS-LVO.

Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in Differentiating between brain tumor progression versus pseudoprogression and radionecrosis.

PURPOSE: In this study we gathered and analyzed the available evidence regarding 17 different imaging modalities and performed network meta-analysis to find the most effective modality for the differentiation between brain tumor recurrence and post-treatment radiation effects. METHODS: We conducted a comprehensive systematic search on PubMed and Embase. The quality of eligible studies was assessed using the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument. For each meta-analysis, we recalculated the effect size, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio from the individual study data provided in the original meta-analysis using a random-effects model. Imaging technique comparisons were then assessed using NMA. Ranking was assessed using the multidimensional scaling approach and by visually assessing surface under the cumulative ranking curves. RESULTS: We identified 32 eligible studies. High confidence in the results was found in only one of them, with a substantial heterogeneity and small study effect in 21% and 9% of included meta-analysis respectively. Comparisons between MRS Cho/NAA, Cho/Cr, DWI, and DSC were most studied. Our analysis showed MRS (Cho/NAA) and 18F-DOPA PET displayed the highest sensitivity and negative likelihood ratios. 18-FET PET was ranked highest among the 17 studied techniques with statistical significance. APT MRI was the only non-nuclear imaging modality to rank higher than DSC, with statistical insignificance, however. CONCLUSION: The evidence regarding which imaging modality is best for the differentiation between radiation necrosis and post-treatment radiation effects is still inconclusive. Using NMA, our analysis ranked FET PET to be the best for such a task based on the available evidence. APT MRI showed promising results as a non-nuclear alternative.

Clinical application of brain perfusion imaging in detecting stroke mimics: A review.

Stroke mimics constitute a significant proportion of patients with suspected acute ischemic stroke. These conditions may resemble acute ischemic stroke and demonstrate abnormalities on perfusion imaging sequences. The most common stroke mimics include seizure/epilepsy, migraine with aura, brain tumors, functional disorders, infectious encephalopathies, Wernicke's encephalopathy, and metabolic abnormalities. Brain perfusion imaging techniques, particularly computed tomography perfusion and magnetic resonance perfusion, are being widely used in routine clinical practice for treatment selection in patients presenting with large vessel occlusion. At the same time, the utilization of these imaging modalities enables the opportunity to better diagnose patients with stroke mimics in a time-sensitive setting, leading to appropriate management, decision-making, and resource allocation. In this review, we describe patterns of perfusion abnormalities that could discriminate patients with stroke mimics from those with acute ischemic stroke and provide specific case examples to illustrate these perfusion abnormalities. In addition, we discuss the challenges associated with interpretation of perfusion images in stroke-related pathologies. In general, perfusion imaging can provide additional information in some cases-when used in combination with conventional magnetic resonance imaging and computed tomography-and might help in detecting stroke mimics among patients who present with acute onset focal neurological symptoms.

Arterial spin labeling clinical applications for brain tumors and tumor treatment complications: A comprehensive case-based review.

Arterial spin labeling (ASL) is a noninvasive neuroimaging technique that allows for quantifying cerebral blood flow without intravenous contrast. Various neurovascular disorders and tumors have cerebral blood flow alterations. Identifying these perfusion changes through ASL can aid in the diagnosis, especially in entities with normal structural imaging. In addition, complications of tumor treatment and tumor progression can also be monitored using ASL. In this case-based review, we demonstrate the clinical applications of ASL in diagnosing and monitoring brain tumors and treatment complications.

Parotid Warthin's tumor: novel MR imaging score as diagnostic indicator.

OBJECTIVES: Despite known characteristic radiologic and clinical features, differentiation between Warthin's tumor (WT) and other parotid tumors remains challenging. The purpose of this study was to more precisely assess the MR imaging features of WT and to develop a scoring system combining the most specific characteristics. METHODS: A total of 208 patients with parotid gland tumors and presurgical MRI were included. Tumors were divided into 5 histological subtypes, and different MRI features were compared between groups. An MRI scoring test was developed including MR parameters that contributed significantly in distinguishing WT from other tumors. RESULTS: The best MRI features for differentiating between WTs from other tumors included bilaterality (P = 0.002), multifocality (P < 0.001), ADC values <905.1 (P < 0.001), and high signal intensity on T1-W images (P < 0.001). Six or more points on the 14-point scoring MRI scale was associated with an area under the curve of 0.99 (Accuracy of 98%), while a cut-off value of 7 indicated 100% specificity and 100% positive predictive value. CONCLUSIONS: Ill-defined margins, low T1-W signal, and location in the upper 2/3 of the parotid gland excluded WTs in 100% of cases. The proposed scoring method allows WTs to be distinguished from other tumors with high accuracy.

Can Preoperative Mapping with Functional MRI Reduce Morbidity in Brain Tumor Resection? A Systematic Review and Meta-Analysis of 68 Observational Studies.

Background Preoperative functional MRI (fMRI) is one of several techniques developed to localize critical brain structures and brain tumors. However, the usefulness of fMRI for preoperative surgical planning and its potential effect on neurologic outcomes remain unclear. Purpose To assess the overall postoperative morbidity among patients with brain tumors by using preoperative fMRI versus surgery without this tool or with use of standard (nonfunctional) neuronavigation. Materials and Methods A systematic review and meta-analysis of studies across major databases from 1946 to June 20, 2020, were conducted. Inclusion criteria were original studies that (a) included patients with brain tumors, (b) performed preoperative neuroimaging workup with fMRI, (c) investigated the usefulness of a preoperative or intraoperative functional neuroimaging technique and used that technique to resect cerebral tumors, and (d) reported postoperative clinical measures. Pooled estimates for adverse event rate (ER) effect size (log ER, log odds ratio, or Hedges g) with 95% CIs were computed by using a random-effects model. Results Sixty-eight studies met eligibility criteria (3280 participants; 58.9% men [1555 of 2641]; mean age, 46 years ± 8 [standard deviation]). Functional deterioration after surgical procedure was less likely to occur when fMRI mapping was performed before the operation (odds ratio, 0.25; 95% CI: 0.12, 0.53; P < .001]), and postsurgical Karnofsky performance status scores were higher in patients who underwent fMRI mapping (Hedges g, 0.66; 95% CI: 0.21, 1.11; P = .004]). Craniotomies for tumor resection performed with preoperative fMRI were associated with a pooled adverse ER of 11% (95% CI: 8.4, 13.1), compared with a 21.0% ER (95% CI: 12.2, 33.5) in patients who did not undergo fMRI mapping. Conclusion From the currently available data, the benefit of preoperative functional MRI planning for the resection of brain tumors appears to reduce postsurgical morbidity, especially when used with other advanced imaging techniques, such as diffusion-tensor imaging, intraoperative MRI, or cortical stimulation. © RSNA, 2021 Online supplemental material is available for this article.

Role of Functional Magnetic Resonance Imaging in the Presurgical Mapping of Brain Tumors.

When planning for brain tumor resection, a balance between maximizing resection and minimizing injury to eloquent brain parenchyma is paramount. The advent of blood oxygenation level-dependent functional magnetic resonance (fMR) imaging has allowed researchers and clinicians to reliably measure physiologic fluctuations in brain oxygenation related to neuronal activity with good spatial resolution. fMR imaging can offer a unique insight into preoperative planning for brain tumors by identifying eloquent areas of the brain affected or spared by the neoplasm. This article discusses the fMR imaging techniques and their applications in neurosurgical planning.

Learning fuzzy clustering for SPECT/CT segmentation via convolutional neural networks.

PURPOSE: Quantitative bone single-photon emission computed tomography (QBSPECT) has the potential to provide a better quantitative assessment of bone metastasis than planar bone scintigraphy due to its ability to better quantify activity in overlapping structures. An important element of assessing the response of bone metastasis is accurate image segmentation. However, limited by the properties of QBSPECT images, the segmentation of anatomical regions-of-interests (ROIs) still relies heavily on the manual delineation by experts. This work proposes a fast and robust automated segmentation method for partitioning a QBSPECT image into lesion, bone, and background. METHODS: We present a new unsupervised segmentation loss function and its semi- and supervised variants for training a convolutional neural network (ConvNet). The loss functions were developed based on the objective function of the classical Fuzzy C-means (FCM) algorithm. The first proposed loss function can be computed within the input image itself without any ground truth labels, and is thus unsupervised; the proposed supervised loss function follows the traditional paradigm of the deep learning-based segmentation methods and leverages ground truth labels during training. The last loss function is a combination of the first and the second and includes a weighting parameter, which enables semi-supervised segmentation using deep learning neural network. EXPERIMENTS AND RESULTS: We conducted a comprehensive study to compare our proposed methods with ConvNets trained using supervised, cross-entropy and Dice loss functions, and conventional clustering methods. The Dice similarity coefficient (DSC) and several other metrics were used as figures of merit as applied to the task of delineating lesion and bone in both simulated and clinical SPECT/CT images. We experimentally demonstrated that the proposed methods yielded good segmentation results on a clinical dataset even though the training was done using realistic simulated images. On simulated SPECT/CT, the proposed unsupervised model's accuracy was greater than the conventional clustering methods while reducing computation time by 200-fold. For the clinical QBSPECT/CT, the proposed semi-supervised ConvNet model, trained using simulated images, produced DSCs of 0.75 and 0.74 for lesion and bone segmentation in SPECT, and a DSC of 0.79 bone segmentation of CT images. These DSCs were larger than that for standard segmentation loss functions by > 0.4 for SPECT segmentation, and > 0.07 for CT segmentation with P-values < 0.001 from a paired t-test. CONCLUSIONS: A ConvNet-based image segmentation method that uses novel loss functions was developed and evaluated. The method can operate in unsupervised, semi-supervised, or fully-supervised modes depending on the availability of annotated training data. The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT. The method can potentially be applied to other medical image segmentation applications.