The ratio between cerebral blood flow and Tmax predicts the quality of collaterals in acute ischemic stroke

Background In acute ischemic stroke the status of collateral circulation is a critical factor in determining outcome. We propose a less invasive alternative to digital subtraction angiography for evaluating collaterals based on dynamic-susceptibility contrast magnetic resonance imaging. Methods Perfusion maps of Tmax and cerebral blood flow (CBF) were created for 35 patients with baseline occlusion of a major cerebral artery. Volumes of hypoperfusion were defined as having a Tmax delay of > 4 seconds (Tmax4s) and > 6 seconds (Tmax6s) and a CBF drop below 80% of healthy, contralateral tissue. For each patient a ratio between the volume of the CBF and the Tmax based perfusion deficit was calculated. Associations with collateral status and radiological outcome were assessed with the Mann-Whitney-U test, uni- and multivariable logistic regression analyses as well as area under the receiver-operator- characteristic (ROC) curve. Results The CBF/Tmax volume ratios were significantly associated with bad collateral status in crude logistic regression analysis as well as with adjustment for NIHSS at admission and baseline infarct volume (OR = 2.5 95% CI[1.2–5.4] p = 0.020 for CBF/Tmax 4s volume ratio and OR = 1.6 95% CI[1.0–2.6] p = 0.031 for CBF/Tmax6s volume ratio). Moreover, the ratios were significantly correlated to final infarct size (Spearman’s rho = 0.711 and 0.619, respectively for the CBF/Tmax4s volume ratio and CBF/Tmax6s volume ration, all p<0.001). The ratios also had a high area under the ROC curve of 0.93 95%CI[0.86–1.00]) and 0.90 95%CI[0.80–1.00]respectively for predicting poor radiological outcome. Conclusions In the setting of acute ischemic stroke the CBF/Tmax volume ratio can be used to differentiate between good and insufficient collateral circulation without the need for invasive procedures like conventional angiography

The Association Between Recanalization, Collateral Flow, and Reperfusion in Acute Stroke Patients: A Dynamic Susceptibility Contrast MRI Study

Background: Collateral circulation in ischemic stroke patients plays an important role in infarct evolution und assessing patients' eligibility for endovascular treatment. By means of dynamic susceptibility contrast MRI, we aimed to investigate the effects of reperfusion, recanalization, and collateral flow on clinical and imaging outcomes after stroke. Methods: Retrospective analysis of 184 patients enrolled into the prospective observational 1000Plus study (clinicaltrials.org NCT00715533). Inclusion criteria were vessel occlusion on baseline MR-angiography, imaging within 24 h after stroke onset and follow-up perfusion imaging. Baseline Higashida score using subtracted dynamic MR perfusion source images was used to quantify collateral flow. The influence of these variables, and their interaction with vessel recanalization, on clinical and imaging outcomes was assessed using robust linear regression. Results: Ninety-eight patients (53.3%) showed vessel recanalization. Higashida score (p = 0.002), and recanalization (p = 0.0004) were independently associated with reperfusion. However, we found no evidence that the association between Higashida score and reperfusion relied on recanalization status (p = 0.2). NIHSS on admission (p < 0.0001) and recanalization (p = 0.001) were independently associated with long-term outcome at 3 months, however, Higashida score (p = 0.228) was not. Conclusion: Higashida score and recanalization were independently associated with reperfusion, but the association between recanalization and reperfusion was similar regardless of collateral flow quality. Recanalization was associated with long-term outcome. DSC-based measures of collateral flow were not associated with long-term outcome, possibly due to the complex dynamic nature of collateral recruitment, timing of imaging and the employed post-processing

The Effect of Scan Length on the Assessment of BOLD Delay in Ischemic Stroke

Objectives: To evaluate the impact of resting-state functional MRI scan length on the diagnostic accuracy, image quality and lesion volume estimation of BOLD delay maps used for brain perfusion assessment in acute ischemic stroke. Methods: Sixty-three acute ischemic stroke patients received a 340 s resting-state functional MRI within 24 h of stroke symptom onset. BOLD delay maps were calculated from the full scan and four shortened versions (68 s, 136 s, 204 s, 272 s). The BOLD delay lesions on these maps were compared in terms of spatial overlap and volumetric agreement with the lesions derived from the full scans and with time-to-maximum (Tmax) lesions derived from DSC-MRI in a subset of patients (n = 10). In addition, the interpretability and quality of these maps were compared across different scan lengths using mixed models. Results: Shortened BOLD delay scans showed a small volumetric bias (ranging from 0.05 to 5.3mL; between a 0.13%volumetric underestimation and a 7.7%overestimation relative to the mean of the volumes, depending on scan length) compared to the full scan. Decreased scan length was associated with decreased spatial overlap with both the BOLD delay lesions derived from the full scans and with Tmax lesions. Only the two shortest scan lengths (68 and 136 s) were associated with substantially decreased interpretability, decreased structure clarity, and increased noisiness of BOLD delay maps. Conclusions: BOLD delay maps derived from resting-state fMRI scans lasting 272 and 204 s provide sufficient diagnostic quality and adequate assessment of perfusion lesion volumes. Such shortened scans may be helpful in situations where quick clinical decisions need to be made

MYRTUS SPECIES PREVENTS REPRODUCTIVE TOXICITY INDUCED BY DOXORUBICIN IN MALE MICE

Background: Myrtus sp is one of the natural products being used in Unani System of Medicine. Its leaves are frequently used for various ailments like diarrhoea, dysentery, vomiting and osmetic purposes. Objective: the main goal of the present work was to investigate protective effect of Myrtus sp extract against doxorubicin induced sperm abnormalities, genetic toxicity and gene expression alterations. Method: Plant samples were used to obtain plant extraction. Eighty male albino mice were allocated in several groups and treated with doxorubicin alone, doxorubicin plus Myrtus sp extracts for 30 days starting from 24 or 48h after doxorubicin treatment, or supplemented with Myrtus sp extracts for 30 days then treated with doxorubicin treatment. Results: The results revealed that treatment of male mice with doxorubicin then Myrtus sp for one month was the best treatment strategy for protection against doxorubicin induced toxicity. Whereas, Myrtus sp extract significantly preserved male mice from sperm abnormalities induced by doxorubicin treatment, genetic toxicity and gene expression alterations. Conclusion: The results suggested that phenolic compounds exist in the Myrtus sp extract might be contributed to prevention of the reproductive disorders and genotoxicity.Keywords: Myrtus sp, Doxorubicin, Sperm abnormalities, Genetic toxicity, Reproductive gene

Automated acute ischemic stroke lesion delineation based on apparent diffusion coefficient thresholds

Purpose: Automated lesion segmentation is increasingly used in acute ischemic stroke magnetic resonance imaging (MRI). We explored in detail the performance of apparent diffusion coefficient (ADC) thresholding for delineating baseline diffusion-weighted imaging (DWI) lesions. Methods: Retrospective, exploratory analysis of the prospective observational single-center 1000Plus study from September 2008 to June 2013 (clinicaltrials.org; NCT00715533). We built a fully automated lesion segmentation algorithm using a fixed ADC threshold (≤620 × 10–6 mm2/s) to delineate the baseline DWI lesion and analyzed its performance compared to manual assessments. Diagnostic capabilities of best possible ADC thresholds were investigated using receiver operating characteristic curves. Influential patient factors on ADC thresholding techniques’ performance were studied by conducting multiple linear regression. Results: 108 acute ischemic stroke patients were selected for analysis. The median Dice coefficient for the algorithm was 0.43 (IQR 0.20–0.64). Mean ADC values in the DWI lesion (β = −0.68, p < 0.001) and DWI lesion volumes (β = 0.29, p < 0.001) predicted performance. Optimal individual ADC thresholds differed between subjects with a median of ≤691 × 10−6 mm2/s (IQR ≤660–750 × 10−6 mm2/s). Mean ADC values in the DWI lesion (β = −0.96, p < 0.001) and mean ADC values in the brain parenchyma (β = 0.24, p < 0.001) were associated with the performance of individual thresholds. Conclusion: The performance of ADC thresholds for delineating acute stroke lesions varies substantially between patients. It is influenced by factors such as lesion size as well as lesion and parenchymal ADC values. Considering the inherent noisiness of ADC maps, ADC threshold-based automated delineation of very small lesions is not reliable

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods

Mechanical Thrombectomy for Acute Ischemic Stroke in Metastatic Cancer Patients: A Nationwide Cross-Sectional Analysis

BACKGROUND AND PURPOSE: Mechanical thrombectomy (MT) is the standard treatment for large vessel occlusion (LVO) acute ischemic stroke. Patients with active malignancy have an increased risk of stroke but were excluded from MT trials. METHODS: We searched the National Readmission Database for LVO patients treated with MT between 2016-2018 and compared the characteristics and outcomes of cancer-free patients to those with metastatic cancer (MC). Primary outcomes were all-cause in-hospital mortality and favorable outcome, defined as a routine discharge to home (regardless of whether home services were provided or not). Multivariate regression was used to adjust for confounders. RESULTS: Of 40,537 LVO patients treated with MT, 933 (2.3%) had MC diagnosis. Compared to cancer-free patients, MC patients were similar in age and stroke severity but had greater overall disease severity. Hospital complications that occurred more frequently in MC included pneumonia, sepsis, acute coronary syndrome, deep vein thrombosis, and pulmonary embolism (P\u3c0.001). Patients with MC had similar rates of intracerebral hemorrhage (20% vs. 21%) but were less likely to receive tissue plasminogen activator (13% vs. 23%, P\u3c0.001). In unadjusted analysis, MC patients as compared to cancer-free patients had a higher in-hospital mortality rate and were less likely to be discharged to home (36% vs. 42%, P=0.014). On multivariate regression adjusting for confounders, mortality was the only outcome that was significantly higher in the MC group than in the cancerfree group (P\u3c0.001). CONCLUSION: LVO patients with MC have higher mortality and more infectious and thrombotic complications than cancer-free patients. MT nonetheless can result in survival with good outcome in slightly over one-third of patients

On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking

Average Hausdorff distance is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, it is used to compare ground truth images with segmentations allowing their ranking. We identified, however, ranking errors of average Hausdorff distance making it less suitable for applications in segmentation performance assessment. To mitigate this error, we present a modified calculation of this performance measure that we have coined "balanced average Hausdorff distance". To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using both performance measures. We calculated the Kendall rank correlation coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by balanced average Hausdorff distance had a significantly higher median correlation (1.00) than those by average Hausdorff distance (0.89). In 200 total rankings, the former misranked 52 whilst the latter misranked 179 segmentations. Balanced average Hausdorff distance is more suitable for rankings and quality assessment of segmentations than average Hausdorff distance

An evaluation of performance measures for arterial brain vessel segmentation

Background: Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation. Methods: To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient. Results: The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation. Conclusions: Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches