Neurosurgical education in Europe and the United States of America

Training in neurological surgery is one of the most competitive and demanding specializations in medicine. It therefore demands careful planning in both the scientific and clinical neurosurgery arena to finally turn out physicians that can be clinically sound and scientifically competitive. National and international training and career options are pointed out, based on the available relevant literature, with the objective of comparing the neurosurgical training in Europe and the USA. Despite clear European Association of Neurosurgical Societies guidelines, every country in Europe maintains its own board requirements, which is reflected in an institutional curriculum that is specific to the professional society of that particular country. In contrast, the residency program in the USA is required to comply with the Accreditation Council for Graduate Medical Education guidelines. Rather similar guidelines exist for the education of neurosurgical residents in the USA and Europe; their translation into the practical hospital setting and the resulting clinical lifestyle of a resident diverges enormously. Since neurosurgical education remains heterogeneous worldwide, we argue that a more standardized curriculum across different nations would greatly facilitate the interaction of different centers, allow a direct comparison of available services, and support the exchange of vital information for quality control and future improvements. Furthermore, the exchange of residents between different training centers may improve education by increasing their knowledge base, both technically as well as intellectuall

Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging

We propose to create a medical imaging artificial intelligence (AI) center (name: Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging). AI is the new revolutionary technique for medical research and is reshaping tomorrow’s clinical practice in medical imaging (radiology and pathology). Our long-term vision is to build a center for innovative AI in clinical translational medical imaging by combining computational expertise and clinical resources across Pitt, UPMC, and CMU. The Center concept is a formalization of a group of researchers and clinicians that are united by the common theme: “building advanced and trustworthy imaging AI for clinical applications.” Our short-term plan is to assemble dedicated members from the School of Medicine, the School of Engineering, and the School of Computing and Information. We seek a Scaling grant from the Momentum Funds to foster collaborative activities of the Center between these three Pitt schools to provide the essential components of a competitive P41 (Biomedical Technology Resource Centers) center grant in 2 years. The National Institute of Biomedical Imaging and Bioengineering (NIBIB) P41 mechanism aligns with the overall vision of this initiative to develop specific AI imaging tools and to support the dissemination and commercialization pathways that are essential to bringing AI imaging tools to clinical practice. These projects will include key components: 1) Clinical need-driven medical imaging AI development and evaluation of tools, models, systems, and informatics, 2) Core imaging AI theory, methodology, and algorithm investigation, and 3) Linking imaging phenotypes to the biological (genomics and proteomics) underpinnings. To date, we have already 35 members for the Center. The Pitt Momentum Funds will provide critical scaling support to promote communication between the three Pitt schools to develop a competitive P41 grant application and a sustainable framework to ensure the clinical impact of these AI imaging tools

Successful Treatment of Intracranial Hemorrhage with Recombinant Activated Factor VII in a Patient with Newly Diagnosed Acute Myeloid Leukemia: A Case Report and Review of the Literature

Intracranial hemorrhage (ICH) is a common complication in acute myeloid leukemia (AML) patients with an incidence rate of 6.3% [1]. Bleeding disorders related to disseminated intravascular coagulation (DIC) are common complications in AML cases [2]. Recombinant activated Factor VII (rFVIIa [NovoSeven®]) is approved for the treatment of bleeding complications with FVIII or FIX inhibitors in patients with congenital FVII deficiency. Use of rFVIIa for the treatment of acute hemorrhage in patients without hemophilia has been successful [3,4]. Herein, we describe the successful use of rFVIIa in a patient with acute ICH in the setting of newly diagnosed AML

23 Validation of PD-L1 dynamic expression on extracellular vesicles as a predictor of response to immune-checkpoint inhibitors and survival in non-small cell lung cancer patients

BackgroundImmune-checkpoint inhibitors (ICIs) revolutionized the treatment of advanced non-small cell lung cancer (NSCLC).1–3 To date, tissue PD-L1 immunohistochemistry is one of the leading biomarkers for prediction of ICIs response but has several limitations.4 5Extracellular vesicles (EVs) are cell-derived structures involved in cell communication and represent a potential minimally invasive alternative to predicting ICI response.6–9 Based on this and our preliminary results presented at SITC 2020,10 we hypothesize that EV PD-L1 predicts response to ICIs in NSCLC.MethodsThis study evaluates an exploratory cohort of advanced/metastatic NSCLC patients receiving ICIs (cohort A) and a validation cohort receiving Pembrolizumab+docetaxel or docetaxel alone (PROLUNG Phase 2 randomized trial) (cohort B).11 Plasma samples were collected pre-treatment (T1) and at 3 treatment cycles (T2) (figure 1A). Response was assessed by computed-tomography scan at 3 (cohort A) and 6–8 treatment cycles (cohort B) according to mono- or chemotherapy combination therapy. Patients were classified as responders (partial, stable, or complete response) or non-responders (progressive disease) by RECISTv1.1.12 EVs were isolated by serial ultracentrifugation and characterized following ISEV recommendations.13,14 Tissue PD-L1 expression was measured by standardized immunohistochemistry (SP263, 22C3, or 28–8 clones)5 and EV PD-L1 expression by immunoblot and its ratio was calculated as EV PD-L1 T2/T1. Cut-offs from the exploratory cohort were applied to the validation cohort, being EV PD-L1 ratio <0.85 = Low.ResultsPaired samples from 30 ICIs, 23 pembrolizumab+docetaxel, and 15 docetaxel treated patients were analyzed. In cohort A, non-responders showed higher EV PD-L1 ratio than responders (p=0.012) (figure 1B) with an area-under-the-curve (AUC) of 77.3%, 83.3% sensitivity, and 61.1% specificity, while the tissue PD-L1 was not predictive (AUC=50%). As a validation, pembrolizumab+docetaxel treated non-responders showed higher EV PD-L1 ratio (p=0.036) than responders with an AUC=69.3%, sensitivity=75%, and specificity=63.6%, outperforming the tissue PD-L1 (figure 1C). No statistically significant differences were observed in the docetaxel group (p=0.885). Moreover, ICIs patients with higher EV PD-L1 ratio showed shorter progression-free survival (PFS) (HR=0.30, p=0.066) and overall survival (OS) (HR=0.17, p=0.016) (figure 1D) which was also observed in the pembrolizumab+docetaxel cohort with shorter PFS (HR=0.12, p=0.004) and OS (HR=0.23, p=0.010) (figure 1E). EV PD-L1 ratio did not predict survival in docetaxel-treated patients.Abstract 23 Figure 1(A) Study design and methodology. (B) EV PD-L1 ratio predicts response to ICIs in 30 NSCLC patients from the discovery cohort A and outperforms tissue PD-L1. (C) EV PD-L1 ratio is predictive for response to pembrolizumab+docetaxel in 23 NSCLC patients but not in 15 patients receiving docetaxel alone from cohort B. (D) Higher EV PD-L1 ratio predicts shorter PFS and OS in 30 patients from the discovery cohort A treated with ICIs. (E) Higher EV PD-L1 ratio is associated with shorter PFS and OS in 23 patients treated with pembrolizumab+docetaxel but not in patients treated with docetaxel alone. Abbreviations: CT: Computed tomography, EV: Extracellular vesicle; HR: Hazard Ratio; ICIs: Immune-checkpoint Inhibitors; IHC: Immunohistochemistry; NR: Non-Responders; OS: Overall Survival; p: p-value; PFS: Progression-free survival; R: Responders [Created with BioRender].ConclusionsWe demonstrated that treatment-associated changes in EV PD-L1 levels are predictive of response and survival in advanced NSCLC patients treated with ICIs. This model, if confirmed in a large prospective cohort, could have important clinical implications, guiding treatment decisions and improving the outcome of patients receiving ICIs.AcknowledgementsWe would like to extend our gratitude to the all the patients that participated in the study.ReferencesBorghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus Docetaxel in Advanced Nonsquamous Non–Small-Cell Lung Cancer. N Engl J Med 2015;373:1627–39.Herbst RS, Baas P, Kim DW, Felip E, Pérez-Gracia JL, Han JY, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): A randomised controlled trial. Lancet 2016;387:1540–50.Ruiz-Patiño A, Arrieta O, Cardona AF, Martín C, Raez LE, Zatarain-Barrón ZL, et al. Immunotherapy at any line of treatment improves survival in patients with advanced metastatic non-small cell lung cancer (NSCLC) compared with chemotherapy (Quijote-CLICaP). Thorac Cancer 2020;11:353–61.Doroshow DB, Bhalla S, Beasley MB, Sholl LM, Kerr KM, Gnjatic S, et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol 2021;18:345–362.Hirsch FR, McElhinny A, Stanforth D, Ranger-Moore J, Jansson M, Kulangara K, et al. PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the blueprint PD-L1 IHC assay comparison project. J Thorac Oncol 2017;12:208–222.Poggio M, Hu T, Pai CC, Chu B, Belair CD, Chang A, et al. Suppression of exosomal PD-L1 induces systemic anti-tumor immunity and memory. Cell 2019;177:414–427.e13.Cordonnier M, Nardin C, Chanteloup G, Derangere V, Algros MP, Arnould L, et al. Tracking the evolution of circulating exosomal-PD-L1 to monitor melanoma patients. J Extracell Vesicles 2020;9:1710899.Del Re M, Cucchiara F, Rofi E, Fontanelli L, Petrini I, Gri N, et al. A multiparametric approach to improve the prediction of response to immunotherapy in patients with metastatic NSCLC. Cancer Immunol Immunother 2020;70:1667–1678.Chen G, Huang AC, Zhang W, Zhang G, Wu M, Xu W, et al. Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature. 2018;560:382–6.10 de Miguel Perez D, Russo A, Gunasekaran M, Cardona A, Lapidus R, Cooper B, et al. 31 Dynamic change of PD-L1 expression on extracellular vesicles predicts response to immune-checkpoint inhibitors in non-small cell lung cancer patients. 2020J Immunother Cancer;8(Suppl 3):A30–A30.Arrieta O, Barrón F, Ramírez-Tirado LA, Zatarain-Barrón ZL, Cardona AF, Díaz-García D, et al. Efficacy and safety of pembrolizumab plus docetaxel vs docetaxel alone in patients with previously treated advanced non–small cell lung cancer: the PROLUNG phase 2 randomized clinical trial. 2020JAMA Oncol;6:856–864.Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). 2009Eur J Cancer;45:228–47.Reclusa P, Verstraelen P, Taverna S, Gunasekaran M, Pucci M, Pintelon I, et al. Improving extracellular vesicles visualization: From static to motion. 2020Sci Rep;10:6494.Théry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. 2018J Extracell Vesicles;7:1535750Ethics ApprovalPatients consented to Institutional Review Board–approved protocol, A.O. Pappardo, Messina, Italy for cohort A and Thoracic Oncology Unit, Instituto Nacional de Cancerología (INCan), México City, México in case of the cohort B. Biological material was transferred to the University of Maryland School of Medicine, Baltimore for EV analysis under signed MTA between institutions MTA/2020–13111 & MTA/2020–13113

Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P \u3c 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer

Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity.

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors