Methods for Neuroscience Drug Development: Guidance on Standardization of the Process for Defining Clinical Outcome Strategies in Clinical Trials.

Neurosciences clinical trials continue to have notoriously high failure rates. Appropriate outcomes selection in early clinical trials is key to maximizing the likelihood of identifying new treatments in psychiatry and neurology. The field lacks good standards for designing outcome strategies, therefore The Outcomes Research Group was formed to develop and promote good practices in outcome selection. This article describes the first published guidance on the standardization of the process for clinical outcomes in neuroscience. A minimal step process is defined starting as early as possible, covering key activities for evidence generation in support of content validity, patient-centricity, validity requirements and considerations for regulatory acceptance. Feedback from expert members is provided, regarding the risks of shortening the process and examples supporting the recommended process are summarized. This methodology is now available to researchers in industry, academia or clinics aiming to implement consensus-based standard practices for clinical outcome selection, contributing to maximizing the efficiency of clinical research.

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available on the respective repositories.

Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.

With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets. In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.

Liver function indicators in patients with breast cancer before and after detection of hepatic metastases-a retrospective study.

BACKGROUND: Liver metastases are common in patients with breast cancer, and determining the factors associated with such metastases may improve both their early detection and treatment. Given that liver function protein level changes in these patients have not been determined, the aim of our study was to investigate liver function protein level changes over time, spanning 6 months before the detection of liver metastasis to 12 months after. METHODS: We retrospectively studied 104 patients with hepatic metastasis from breast cancer who were treated at the Departments of Internal Medicine I and the Department of Obstetrics and Gynecology at the Medical University of Vienna between 1980 and 2019. Data were extracted from patient records. RESULTS: Aspartate aminotransferase, alanine aminotransferase, gamma-glutamyltransferase, lactate dehydrogenase and alkaline phosphatase were significantly elevated when compared to normal range 6 months before the detection of liver metastases (p<0.001) Albumin was decreased (p<0.001). The values of aspartate aminotransferase, gamma-glutamyltransferase, and lactate dehydrogenase were significantly increased at the time of diagnosis compared to 6 months prior (p<0.001). Patient- and tumor-specific parameters had no influence on these liver function indicators. Elevated aspartate aminotransferase (p = 0.002) and reduced albumin (p = 0.002) levels at the time of diagnosis were associated with shorter overall survival. CONCLUSION: Liver function protein levels should be considered as potential indicators when screening for liver metastasis in patients with breast cancer. With the new treatment options available, it could lead to prolonged life.

Flexible Risk Evidence Combination Rules in Breast Cancer Precision Therapy.

Evidence theory by Dempster-Shafer for determination of hormone receptor status in breast cancer samples was introduced in our previous paper. One major topic pointed out here is the link between pieces of evidence found from different origins. In this paper the challenge of selecting appropriate ways of fusing evidence, depending on the type and quality of data involved is addressed. A parameterized family of evidence combination rules, covering the full range of potential needs, from emphasizing discrepancies in the measurements to aspiring accordance, is covered. The consequences for real patient samples are shown by modeling different decision strategies.

A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images.

Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both nuclei number and shape provide essential diagnostic information. With the advent of digital pathology and emerging computerized methods to analyze the digitized images, nuclei detection, their instance segmentation and classification can be performed automatically. These computerized methods support human experts and allow for faster and more objective image analysis. While methods ranging from conventional image processing techniques to machine learning-based algorithms have been proposed, supervised convolutional neural network (CNN)-based techniques have delivered the best results. In this paper, we propose a CNN-based dual decoder U-Net-based model to perform nuclei instance segmentation in hematoxylin and eosin (H&E)-stained histological images. While the encoder path of the model is developed to perform standard feature extraction, the two decoder heads are designed to predict the foreground and distance maps of all nuclei. The outputs of the two decoder branches are then merged through a watershed algorithm, followed by post-processing refinements to generate the final instance segmentation results. Moreover, to additionally perform nuclei classification, we develop an independent U-Net-based model to classify the nuclei predicted by the dual decoder model. When applied to three publicly available datasets, our method achieves excellent segmentation performance, leading to average panoptic quality values of 50.8%, 51.3%, and 62.1% for the CryoNuSeg, NuInsSeg, and MoNuSAC datasets, respectively. Moreover, our model is the top-ranked method in the MoNuSAC post-challenge leaderboard.

Digital health understanding and preparedness of medical students: a cross-sectional study.

Digitalisation is changing all areas of our daily life. This changing environment requires new competences from physicians in all specialities. This study systematically surveyed the knowledge, attitude, and interests of medical students. These results will help further develop the medical curriculum, as well as increase our understanding of future physicians by other healthcare market players. A web-based survey consisting of four sections was developed: Section one queried demographic data, section two assessed the current digital health knowledge of medical students, section three queried their attitudes about the future impact of digital health in medicine and section four assessed the recommendations medical students have for the medical curriculum in terms of digital health. This survey was distributed to all (11,978) student at all public Austrian medical schools. A total of 8.4% of the medical student population started the survey. At the knowledge self-assessment section, the medical students reached mean of 11.74 points (SD 4.42) out of a possible maximum of 32 (female mean 10.66/ SD 3.87, male mean 13.34/SD 4.50). The attitude section showed that students see digitalisation as a threat, especially with respect to the patient-physician relationship. The curriculum recommendation section showed a high interest for topics related to AI, a per study year increasing interest in impact of digital health in communication, as well as a decreasing interest in robotic related topics. The attitude towards digital health can be described as sceptical. To ensure that future physicians keep pace with this development and fulfil their responsibility towards the society, medical schools need to be more proactive to foster the understanding of medical students that digital health will persistently alter the medical practice.

Relational local electroencephalography representations for sleep scoring.

Computational sleep scoring from multimodal neurophysiological time-series (polysomnography PSG) has achieved impressive clinical success. Models that use only a single electroencephalographic (EEG) channel from PSG have not yet received the same clinical recognition, since they lack Rapid Eye Movement (REM) scoring quality. The question whether this lack can be remedied at all remains an important one. We conjecture that predominant Long Short-Term Memory (LSTM) models do not adequately represent distant REM EEG segments (termed epochs), since LSTMs compress these to a fixed-size vector from separate past and future sequences. To this end, we introduce the EEG representation model ENGELBERT (electroEncephaloGraphic Epoch Local Bidirectional Encoder Representations from Transformer). It jointly attends to multiple EEG epochs from both past and future. Compared to typical token sequences in language, for which attention models have originally been conceived, overnight EEG sequences easily span more than 1000 30 s epochs. Local attention on overlapping windows reduces the critical quadratic computational complexity to linear, enabling versatile sub-one-hour to all-day scoring. ENGELBERT is at least one order of magnitude smaller than established LSTM models and is easy to train from scratch in a single phase. It surpassed state-of-the-art macro F1-scores in 3 single-EEG sleep scoring experiments. REM F1-scores were pushed to at least 86%. ENGELBERT virtually closed the gap to PSG-based methods from 4-5 percentage points (pp) to less than 1 pp F1-score.

Altered Expression of RB and pRB in Tissue Arrays of Primary Breast Cancers and Matched Axillary Lymph Node Metastases.

Objectives: The retinoblastoma (RB) pathway is crucial in the development and progression of many cancers. To better understand the biology of progressive breast cancer (BC), we examined protein expression of the RB pathway in primary BCs and matched axillary lymph node metastases (LM). Methods: Immunohistochemistry was used to evaluate cyclin D1, CDK4/6, RB, phosphorylated RB (pRB), and E2F1 expression in tissue arrays containing cores of 50 primary BCs and matched LM. The number of positive tumor cells and staining intensity were scored. Results: The proteins were localized in the nucleus, while CDK6 was detected in the cytoplasm and CDK4 was found in both. pRB and E2F1 showed higher expression in matched LM than in primary tumors. Expression of these proteins differed significantly by the percentage of positive tumor cells, while proteins in the proximal portion of the RB pathway showed no significant differences. The main path of alteration consisted of high pRB in primary BC, remaining pRB high in the majority of LM, variations occurring in fewer cases. All matched LM of the few primary tumors that had unaltered RB and pRB expression showed changes in RB or pRB expression. Conclusion: Expression of pRB and E2F1 was significantly higher in LM than in primary BC. A majority of cancers with LM showed altered RB or pRB expression, suggesting that proteins downstream in the RB pathway play a critical role in metastatic BC and disease progression. So looking at the RB pathway could be an option for chemotherapy decisions in patients with only few LM.

Influence of Obesity and Unemployment on Fertility Rates: A Multinational Analysis of 30 Countries from 1976 to 2014.

Background: The rationale of a postulated decrease in fertility rate development is still being debated. Among the multiple influencing factors, socioeconomic variables and their complex influence are of particular interest. Methods: Data on socioeconomic and health variables from 1976−2014 of 30 countries within the OECD region were analysed for their respective influence on fertility rates by using mixed-effect regression models. Results: A significant negative influence of the increase in unemployment rate on the following year's changes in fertility rate in Western (−0.00256; p < 0.001) as well as Eastern European (−0.0034; p < 0.001) countries was revealed. The effect of being overweight was significant for Western European (−0.00256; p < 0.001) countries only. When analysing the whole OECD region, an increase in unemployment retained its significant negative influence on the fertility rate (−0.0028; p < 0.001), while being overweight did not. Interestingly, divergent influences of time were revealed and fertility rates increased with time in Eastern Europe while they decreased in Western Europe. Conclusion: Importantly, a significant negative influence of increase in unemployment on the fertility rate was revealed­irrespective of the region and time analysed. Furthermore, an adverse effect of being overweight on the fertility rate in Western European countries was revealed. Interestingly, time was associated with a decreasing fertility rate in Western but not in Eastern Europe.

Machine learning-derived electrocardiographic algorithm for the detection of cardiac amyloidosis.

BACKGROUND: Diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited. OBJECTIVE: The aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis. METHODS: We applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in patients with CA and controls. A machine learning approach was then used to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool. FINDINGS: Areas of low voltage were localised in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualised on the right ventricle. Potential maps revealed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1-V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III and aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify patients with CA by analysing 12-lead ECGs before and after training on the defined ECG patterns. Training led to significant improvements in the detection rate of CA, with an area under the curve of 0.69 before and 0.97 after training. INTERPRETATION: Using a machine learning approach, an ECG-based tool was developed from detailed electroanatomical mapping of patients with CA. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities.

The roles of predictors in cardiovascular risk models - a question of modeling culture?

BACKGROUND: While machine learning (ML) algorithms may predict cardiovascular outcomes more accurately than statistical models, their result is usually not representable by a transparent formula. Hence, it is often unclear how specific values of predictors lead to the predictions. We aimed to demonstrate with graphical tools how predictor-risk relations in cardiovascular risk prediction models fitted by ML algorithms and by statistical approaches may differ, and how sample size affects the stability of the estimated relations. METHODS: We reanalyzed data from a large registry of 1.5 million participants in a national health screening program. Three data analysts developed analytical strategies to predict cardiovascular events within 1 year from health screening. This was done for the full data set and with gradually reduced sample sizes, and each data analyst followed their favorite modeling approach. Predictor-risk relations were visualized by partial dependence and individual conditional expectation plots. RESULTS: When comparing the modeling algorithms, we found some similarities between these visualizations but also occasional divergence. The smaller the sample size, the more the predictor-risk relation depended on the modeling algorithm used, and also sampling variability played an increased role. Predictive performance was similar if the models were derived on the full data set, whereas smaller sample sizes favored simpler models. CONCLUSION: Predictor-risk relations from ML models may differ from those obtained by statistical models, even with large sample sizes. Hence, predictors may assume different roles in risk prediction models. As long as sample size is sufficient, predictive accuracy is not largely affected by the choice of algorithm.

Convolutional Neural Networks for Fully Automated Diagnosis of Cardiac Amyloidosis by Cardiac Magnetic Resonance Imaging.

AIMS: We tested the hypothesis that artificial intelligence (AI)-powered algorithms applied to cardiac magnetic resonance (CMR) images could be able to detect the potential patterns of cardiac amyloidosis (CA). Readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of CMRs, in general, may overlook CA. In light of the growing prevalence of the disease and emerging therapeutic options, there is an urgent need to avoid misdiagnoses. METHODS AND RESULTS: Using CMR data from 502 patients (CA: n = 82), we trained convolutional neural networks (CNNs) to automatically diagnose patients with CA. We compared the diagnostic accuracy of different state-of-the-art deep learning techniques on common CMR imaging protocols in detecting imaging patterns associated with CA. As a result of a 10-fold cross-validated evaluation, the best-performing fine-tuned CNN achieved an average ROC AUC score of 0.96, resulting in a diagnostic accuracy of 94% sensitivity and 90% specificity. CONCLUSIONS: Applying AI to CMR to diagnose CA may set a remarkable milestone in an attempt to establish a fully computational diagnostic path for the diagnosis of CA, in order to support the complex diagnostic work-up requiring a profound knowledge of experts from different disciplines.

Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation.

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository.

k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data.

PURPOSE: State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination significantly reduces the amount of data, currently it is performed in image space at the end of the reconstruction. This prolongs reconstruction times and increases RAM requirements. We propose an alternative k-space-based coil combination that uses geometric deep learning to combine MRSI data already in native non-Cartesian k-space. METHODS: Twelve volunteers were scanned at a 3T MR scanner with a 20-channel head coil at 10 different positions with water-unsuppressed MRSI. At the eleventh position, water-suppressed MRSI data were acquired. Data of 7 volunteers were used to estimate sensitivity maps and form a base for simulating training data. A neural network was designed and trained to remove the effect of sensitivity profiles of the coil elements from the MRSI data. The water-suppressed MRSI data of the remaining volunteers were used to evaluate the performance of the new k-space-based coil combination relative to that of a conventional image-based alternative. RESULTS: For both approaches, the resulting metabolic ratio maps were similar. The SNR of the k-space-based approach was comparable to the conventional approach in low SNR regions, but underperformed for high SNR. The Cramér-Rao lower bounds show the same trend. The analysis of the FWHM showed no difference between the two methods. CONCLUSION: k-Space-based coil combination of MRSI data is feasible and reduces the amount of raw data immediately after their sampling.

CryoNuSeg: A dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images.

Nuclei instance segmentation plays an important role in the analysis of hematoxylin and eosin (H&E)-stained images. While supervised deep learning (DL)-based approaches represent the state-of-the-art in automatic nuclei instance segmentation, annotated datasets are required to train these models. There are two main types of tissue processing protocols resulting in formalin-fixed paraffin-embedded samples (FFPE) and frozen tissue samples (FS), respectively. Although FFPE-derived H&E stained tissue sections are the most widely used samples, H&E staining of frozen sections derived from FS samples is a relevant method in intra-operative surgical sessions as it can be performed more rapidly. Due to differences in the preparation of these two types of samples, the derived images and in particular the nuclei appearance may be different in the acquired whole slide images. Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality. In this paper, we introduce CryoNuSeg, the first fully annotated FS-derived cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset contains images from 10 human organs that were not exploited in other publicly available datasets, and is provided with three manual mark-ups to allow measuring intra-observer and inter-observer variabilities. Moreover, we investigate the effects of tissue fixation/embedding protocol (i.e., FS or FFPE) on the automatic nuclei instance segmentation performance and provide a baseline segmentation benchmark for the dataset that can be used in future research. A step-by-step guide to generate the dataset as well as the full dataset and other detailed information are made available to fellow researchers at https://github.com/masih4/CryoNuSeg.

Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study.

(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. While novel therapies improve outcomes, many affected individuals remain undiagnosed due to a lack of awareness among clinicians. This study was undertaken to develop an expert-independent machine learning (ML) prediction model for CA relying on routinely determined laboratory parameters. (2) Methods: In a first step, we developed baseline linear models based on logistic regression. In a second step, we used an ML algorithm based on gradient tree boosting to improve our linear prediction model, and to perform non-linear prediction. Then, we compared the performance of all diagnostic algorithms. All prediction models were developed on a training cohort, consisting of patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF) patients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate prognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best model, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver operating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2% and 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and specificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it possible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared with CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the diagnostic workup of CA and may assist physicians in clinical reasoning.

Ca2+ imaging of neurons in freely moving rats with automatic post hoc histological identification.

BACKGROUND: Cognitive neuroscientists aim to understand behavior often based on the underlying activity of individual neurons. Recently developed miniaturized epifluorescence microscopes allow recording of cellular calcium transients, resembling neuronal activity, of individual neurons even in deep brain areas in freely behaving animals. At the same time, molecular markers allow the characterization of diverse neuronal subtypes by post hoc immunohistochemical labeling. Combining both methods would allow researchers to increase insights into how individual neuronal activity and entities contribute to behavior. NEW METHOD: Here, we present a novel method for identifying the same neurons, recorded with calcium imaging using a miniaturized epifluorescence microscope, post hoc in fixed histological sections. This allows immunohistochemical investigations to detect the molecular signature of in vivo recorded neurons. Our method utilizes the structure of blood vessels for aligning in vivo acquired 2D images with a reconstructed 3D histological model. RESULTS: We automatically matched, 60 % of all in vivo recorded cells post hoc in histology. Across all animals, we successfully matched 43 % to 89 % of the recorded neurons. We provide a measure for the confidence of matched cells and validated our method by multiple simulation studies. COMPARISON WITH EXISTING METHODS: To our knowledge, we present the first method for matching cells, recorded with a miniaturized epifluorescence microscope in freely moving animals, post hoc in histological sections. CONCLUSIONS: Our method allows a comprehensive analysis of how cortical circuits relate to freely moving animal behavior by combining functional activity of individual neurons with their underlying histological profiles.

Complication rates among women undergoing preventive mastectomy: An Austrian registry.

Germline variations in the BRCA-1 and BRCA-2 genes are associated with an increased risk of breast cancer. These variants are found in 5% of all breast cancer cases. Prophylactic mastectomy is the most effective risk-reducing method and shows high rates of patient satisfaction and acceptance. We established a registry of Austrian BRCA-1 and BRCA-2 mutation carriers who had undergone mastectomy for oncologic or prophylactic reasons. Data were collected on the type of operation, complications, and type of reconstructive surgery for patients between 2014 and 2017. The complication rate in patients with nipple-sparing mastectomy was significantly lower (23.1%) than in those with other types of mastectomies (60.7%; P = .005). In patients with implant-based breast reconstruction, subpectoral placement was associated with a significantly higher rate of complications than prepectoral placement (P = .025). Median implant volume was 350 cc (range: 155-650 cc), and a 100-cc increase was associated with doubling of the odds of a complication (regression coefficient = 0.007); based on this finding, some surgeons may decide on using smaller implants. In summary, we identified significant associations between the risk of complications and surgical characteristics, and found host factors like diabetes, BMI, and smoking among Austrian patients with BRCA-1 and BRCA-2 variants.