Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years enabling high precision segmentation with minimal compute. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. Here, we used a unique dataset comprising 568 T1-weighted (T1w) MR images from 191 different studies in combination with cutting edge deep learning methods to build a fast, high-precision brain extraction tool called deepbet. deepbet uses LinkNet, a modern UNet architecture, in a two stage prediction process. This increases its segmentation performance, setting a novel state-of-the-art performance during cross-validation with a median Dice score (DSC) of 99.0% on unseen datasets, outperforming current state of the art models (DSC = 97.8% and DSC = 97.9%). While current methods are more sensitive to outliers, resulting in Dice scores as low as 76.5%, deepbet manages to achieve a Dice score of > 96.9% for all samples. Finally, our model accelerates brain extraction by a factor of ~10 compared to current methods, enabling the processing of one image in ~2 seconds on low level hardware

Activation of NF-κB and p300/CBP potentiates cancer chemoimmunotherapy through induction of MHC-I antigen presentation

Many cancers evade immune rejection by suppressing major histocompatibility class I (MHC-I) antigen processing and presentation (AgPP). Such cancers do not respond to immune checkpoint inhibitor therapies (ICIT) such as PD-1/PD-L1 [PD-(L)1] blockade. Certain chemotherapeutic drugs augment tumor control by PD-(L)1 inhibitors through potentiation of T-cell priming but whether and how chemotherapy enhances MHC-I-dependent cancer cell recognition by cytotoxic T cells (CTLs) is not entirely clear. We now show that the lysine acetyl transferases p300/CREB binding protein (CBP) control MHC-I AgPPM expression and neoantigen amounts in human cancers. Moreover, we found that two distinct DNA damaging drugs, the platinoid oxaliplatin and the topoisomerase inhibitor mitoxantrone, strongly up-regulate MHC-I AgPP in a manner dependent on activation of nuclear factor kappa B (NF-κB), p300/CBP, and other transcription factors, but independently of autocrine IFNγ signaling. Accordingly, NF-κB and p300 ablations prevent chemotherapy-induced MHC-I AgPP and abrogate rejection of low MHC-I-expressing tumors by reinvigorated CD8+ CTLs. Drugs like oxaliplatin and mitoxantrone may be used to overcome resistance to PD-(L)1 inhibitors in tumors that had "epigenetically down-regulated," but had not permanently lost MHC-I AgPP activity

Brain-age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.</p

Prediction of Locally Advanced Urothelial Carcinoma of the Bladder Using Clinical Parameters before Radical Cystectomy - A Prospective Multicenter Study

Introduction: We aimed at developing and validating a pre-cystectomy nomogram for the prediction of locally advanced urothelial carcinoma of the bladder (UCB) using clinicopathological parameters. Materials and Methods: Multicenter data from 337 patients who underwent radical cystectomy (RC) for UCB were prospectively collected and eligible for final analysis. Univariate and multivariate logistic regression models were applied to identify significant predictors of locally advanced tumor stage (pT3/4 and/or pN+) at RC. Internal validation was performed by bootstrapping. The decision curve analysis (DCA) was done to evaluate the clinical value. Results: The distribution of tumor stages pT3/4, pN+ and pT3/4 and/or pN+ at RC was 44.2, 27.6 and 50.4%, respectively. Age (odds ratio (OR) 0.980; p < 0.001), advanced clinical tumor stage (cT3 vs. cTa, cTis, cT1; OR 3.367; p < 0.001), presence of hydronephrosis (OR 1.844; p = 0.043) and advanced tumor stage T3 and/or N+ at CT imaging (OR 4.378; p < 0.001) were independent predictors for pT3/4 and/or pN+ tumor stage. The predictive accuracy of our nomogram for pT3/4 and/or pN+ at RC was 77.5%. DCA for predicting pT3/4 and/or pN+ at RC showed a clinical net benefit across all probability thresholds. Conclusion: We developed a nomogram for the prediction of locally advanced tumor stage pT3/4 and/or pN+ before RC using established clinicopathological parameters

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Brain‐age prediction: systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics

The value of Chinese patents: An empirical investigation of citation lags

China has been experiencing a substantial growth in patent applications. But is this increase accompanied by a similar increase in patent value? To assess this question, we examine the citation lag of Chinese patents as a proxy of patent value in comparison with patents from the US, Europe, Japan, and Korea. Our empirical analysis comprises a unique data set of 60,000 patents with priority years between 2000 and 2010. Utilizing Cox regressions, our results show that Chinese patents suffer from a large citation lag in comparison to international patents, indicating a lower value. This is especially true for patents filed domestically. However, we find empirical support for an increasing patent value in more recent patents. China shows a strong dynamic in the field of patenting and our results suggest that the gap between Chinese patents and international patents might narrow down in the near future

Early specialized care after a first unprovoked epileptic seizure.

A first seizure is a life-changing event with physical and psychological consequences. We aimed to assess the role of early comprehensive patient care after a first unprovoked seizure to improve diagnostic accuracy and follow-up adherence. From April 2011 to March 2012, patients presenting a first unprovoked epileptic seizure received standard patient care (SPC), i.e., a consultation in the ED, an EEG and a CT scan. The patients were notified of the follow-ups. We compared this protocol to subsequently acquired "early comprehensive patient care" (ECPC), which included a consultation by an epileptologist in the emergency department (ED), a routine or long-term monitoring electroencephalogram (LTM-EEG), magnetic resonance imaging and three follow-up consultations (3 weeks, 3 months, 12 months). 183 patients were included (113 ECPC, 70 SPC). LTM-EEG and MRI were performed in 51 and 85 %, respectively, of the patients in the ECPC group vs in 7 and 52 % of the patients in the SPC group (p < 0.001). A final diagnosis was obtained in 64 vs 43 % of the patients in the ECPC vs SPC group (p < 0.01). Patient attendance at 3-month was 84 % in the ECPC group vs 44 % in the SPC group (p < 0.001). At 12-month follow-up, the delay until the first recurrence was longer in the ECPC group (p = 0.008). An early epileptologist-driven protocol is associated with clinical benefit in terms of diagnostic accuracy, follow-up adherence and recurrence. This study highlights the need for epilepsy experts in the early assessment of a first epileptic seizure, starting already in the ED