Spontaneous regression of multiple flow-related aneurysms following treatment of an associated brain arteriovenous malformation: A case report.

INTRODUCTION There is no consensus in the treatment strategy of intracranial aneurysms (IAs) associated with brain arteriovenous malformation (BAVM). In particular, it is unknown if a more aggressive approach should be considered in patients harboring a BAVM, in whom multiple aneurysms or a history of aneurysmal subarachnoid hemorrhage (aSAH) is present. CASE PRESENTATION We report on an elderly woman harboring multiple aneurysms with a history of SAH due to rupture of an unrelated IA. On evaluation, she was also found to harbor a contralateral, left parietal convexity BAVM. Following resection of the latter, spontaneous regression of two large flow-related aneurysms was encountered. DISCUSSION We discuss the therapeutic decision-making, risk stratification, and functional outcome in this patient with regard to the pertinent literature on the risk of hemorrhage in IAs associated with BAVM

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas.

Background First-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making. Objective To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia. Methods By jointly examining two independent performance metrics - the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) - in combination with a stacked super learner, we present a novel perspective on how to assess and compare the discrimination capacity of a set of binary classifiers. Results We demonstrate that for upfront surgery in prolactinoma patients there are not a one-algorithm-fits-all solution in outcome prediction: different algorithms perform best for different time points and different outcomes parameters. In addition, ML classifiers outperform logistic regression in both performance metrics in our cohort when predicting the primary outcome at long-term follow-up and secondary outcome at early follow-up, thus provide an added benefit in risk prediction modeling. In such a setting, the stacking framework of combining the predictions of individual base learners in a so-called super learner offers great potential: the super learner exhibits very good prediction skill for the primary outcome (AUROC: mean 0.9, 95% CI: 0.92 - 1.00; MCC: 0.85, 95% CI: 0.60 - 1.00). In contrast, predicting control of hyperprolactinemia is challenging, in particular in terms of early follow-up (AUROC: 0.69, 95% CI: 0.50 - 0.83) vs. long-term follow-up (AUROC: 0.80, 95% CI: 0.58 - 0.97). It is of clinical importance that baseline prolactin levels are by far the most important outcome predictor at early follow-up, whereas remissions at 30 days dominate the ML prediction skill for DA-dependency over the long-term. Conclusions This study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers

Prediction of Long-Term Restenosis After Carotid Endarterectomy Using Quantitative Magnetic Resonance Angiography.

Background To detect restenosis after carotid endarterectomy (CEA), long-term monitoring is required. However, non-selective follow-up is controversial and can be limited by costs and logistical considerations. Objective To examine the value of immediate perioperative vessel flow measurements after CEA using quantitative magnetic resonance angiography (QMRA) to detect patients at risk of long-term restenosis. Methods A prospective cohort study with long-term sonographic follow-up after CEA for symptomatic internal carotid artery stenosis (ICAs) > 50%. In all patients, vessel flow has been assessed both pre- and postoperatively using QMRA within ±3 days of surgery. Data on QMRA assessment were analyzed to identify patients at risk of restenosis for up to 10 years. Results Restenosis was recorded in 4 of 24 patients (17%) at a median follow-up of 6.8 ± 2.6 years. None of them experienced an ischemic event. Perioperative flow differences were significantly greater in patients without long-term restenosis, both for the ipsilateral ICA (p < 0.001) and MCA (p = 0.03), compared to those with restenosis (p = 0.22 and p = 0.3, respectively). The ICA mean flow ratio (p = 0.05) tended to be more effective than the MCA ratio in predicting restenosis over the long term (p = 0.35). Conclusion Our preliminary findings suggest that QMRA-based mean flow increases after CEA may be predictive of restenosis over the long term. Perioperative QMRA assessment could become an operator-independent screening tool to identify a subgroup of patients at risk for restenosis, in whom long-term monitoring is advised

Impact of the Pattern of Atrial Fibrillation on Stroke Risk and Mortality

Thromboembolism is the most serious complication of AF, and oral anticoagulation is the mainstay therapy. Current guidelines place all AF types together in terms of anticoagulation with the major determinants being associated comorbidities translated into risk marker. Among patients in large clinical trials, those with non-paroxysmal AF appear to be at higher risk of stroke than those with paroxysmal AF. Higher complexity of the AF pattern is also associated with higher risk of mortality. Moreover, continuous monitoring of AF through cardiac implantable devices provided us with the concept of ‘AF burden’. Usually, the larger the AF burden, the higher the risk of stroke; however, the relationship is not well characterised with respect to the threshold value above which the risk increases. The picture is more complex than it appears: AF and underlying disorders must act synergically respecting the magnitude of its own characteristics, which are the amount of time a patient stays in AF and the severity of associated comorbidities

Liquid biopsy biomarkers in urine: a route towards molecular diagnosis and personalized medicine of bladder cancer

Bladder cancer (BC) is characterized by high incidence and recurrence rates together with genomic instability and elevated mutation degree. Currently, cystoscopy combined with cytology is routinely used for diagnosis, prognosis and disease surveillance. Such an approach is often associated with several side effects, discomfort for the patient and high economic burden. Thus, there is an essential demand of non-invasive, sensitive, fast and inexpensive biomarkers for clinical management of BC patients. In this context, liquid biopsy represents a very promising tool that has been widely investigated over the last decade. Liquid biopsy will likely be at the basis of patient selection for precision medicine, both in terms of treatment choice and real-time monitoring of therapeutic effects. Several different urinary biomarkers have been proposed for liquid biopsy in BC, including DNA methylation and mutations, protein-based assays, non-coding RNAs and mRNA signatures. In this review, we summarized the state of the art on different available tests concerning their potential clinical applications for BC detection, prognosis, surveillance and response to therap

Prognostic impact of alternative splicing-derived hMENA isoforms in resected, node-negative, non-small-cell lung cancer

Risk assessment and treatment choice remain a challenge in early non-small-cell lung cancer (NSCLC). Alternative splicing is an emerging source for diagnostic, prognostic and therapeutic tools. Here, we investigated the prognostic value of the actin cytoskeleton regulator hMENA and its isoforms, hMENA(11a) and hMENA Delta v6, in early NSCLC. The epithelial hMENA(11a) isoform was expressed in NSCLC lines expressing E-CADHERIN and was alternatively expressed with hMENA Delta v6. Enforced expression of hMENA Delta v6 or hMENA(11a) increased or decreased the invasive ability of A549 cells, respectively. hMENA isoform expression was evaluated in 248 node-negative NSCLC. High pan-hMENA and low hMENA(11a) were the only independent predictors of shorter disease-free and cancer-specific survival, and low hMENA(11a) was an independent predictor of shorter overall survival, at multivariate analysis. Patients with low pan-hMENA/high hMENA(11a) expression fared significantly better (P &lt;= 0.0015) than any other subgroup. Such hybrid variable was incorporated with T-size and number of resected lymph nodes into a 3-class-risk stratification model, which strikingly discriminated between different risks of relapse, cancer-related death, and death. The model was externally validated in an independent dataset of 133 patients. Relative expression of hMENA splice isoforms is a powerful prognostic factor in early NSCLC, complementing clinical parameters to accurately predict individual patient risk

Spontaneous regression of multiple flow-related aneurysms following treatment of an associated brain arteriovenous malformation: A case report

IntroductionThere is no consensus in the treatment strategy of intracranial aneurysms (IAs) associated with brain arteriovenous malformation (BAVM). In particular, it is unknown if a more aggressive approach should be considered in patients harboring a BAVM, in whom multiple aneurysms or a history of aneurysmal subarachnoid hemorrhage (aSAH) is present.Case presentationWe report on an elderly woman harboring multiple aneurysms with a history of SAH due to rupture of an unrelated IA. On evaluation, she was also found to harbor a contralateral, left parietal convexity BAVM. Following resection of the latter, spontaneous regression of two large flow-related aneurysms was encountered.DiscussionWe discuss the therapeutic decision-making, risk stratification, and functional outcome in this patient with regard to the pertinent literature on the risk of hemorrhage in IAs associated with BAVM

The SSDC Role in the LICIACube Mission: Data Management and the MATISSE Tool

Light Italian Cubesat for Imaging of Asteroids (LICIACube) is an Italian mission managed by the Italian Space Agency (ASI) and part of the NASA Double Asteroid Redirection Test (DART) planetary defense mission. Its main goals are to document the effects of the DART impact on Dimorphos, the secondary member of the (65803) Didymos binary asteroid system, characterizing the shape of the target body and performing dedicated scientific investigations on it. Within this framework, the mission Science Operations Center will be managed by the Space Science Data Center (ASI-SSDC), which will have the responsibility of processing, archiving, and disseminating the data acquired by the two LICIACube onboard cameras. In order to better accomplish this task, SSDC also plans to use and modify its scientific webtool Multi-purpose Advanced Tool for Instruments for the solar system Exploration (MATISSE), making it the primary tool for the LICIACube data analysis, thanks to its advanced capabilities for searching and visualizing data, particularly useful for the irregular shapes common to several small bodies

VADER: Probing the Dark Side of Dimorphos with LICIACube LUKE

The ASI cubesat LICIACube has been part of the first planetary defense mission DART, having among its scopes to complement the DRACO images to better constrain the Dimorphos shape. LICIACube had two different cameras, LEIA and LUKE, and to accomplish its goal, it exploited the unique possibility of acquiring images of the Dimorphos hemisphere not seen by DART from a vantage point of view, in both time and space. This work is indeed aimed at constraining the tridimensional shape of Dimorphos, starting from both LUKE images of the nonimpacted hemisphere of Dimorphos and the results obtained by DART looking at the impacted hemisphere. To this aim, we developed a semiautomatic Computer Vision algorithm, named VADER, able to identify objects of interest on the basis of physical characteristics, subsequently used as input to retrieve the shape of the ellipse projected in the LUKE images analyzed. Thanks to this shape, we then extracted information about the Dimorphos ellipsoid by applying a series of quantitative geometric considerations. Although the solution space coming from this analysis includes the triaxial ellipsoid found by using DART images, we cannot discard the possibility that Dimorphos has a more elongated shape, more similar to what is expected from previous theories and observations. The result of our work seems therefore to emphasize the unique value of the LICIACube mission and its images, making even clearer the need of having different points of view to accurately define the shape of an asteroid.This work was supported by the Italian Space Agency (ASI) within the LICIACube project (ASI-INAF agreement AC No. 2019-31-HH.0) and by the DART mission, NASA contract 80MSFC20D0004