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General General

Biomarkers of the Response to Immune Checkpoint Inhibitors in Metastatic Urothelial Carcinoma.

In Frontiers in immunology ; h5-index 100.0

The mechanisms underlying the resistance to immune checkpoint inhibitors (ICIs) therapy in metastatic urothelial carcinoma (mUC) patients are not clear. It is of great significance to discern mUC patients who could benefit from ICI therapy in clinical practice. In this study, we performed machine learning method and selected 10 prognostic genes for constructing the immunotherapy response nomogram for mUC patients. The calibration plot suggested that the nomogram had an optimal agreement with actual observations when predicting the 1- and 1.5-year survival probabilities. The prognostic nomogram had a favorable discrimination of overall survival of mUC patients, with area under the curve values of 0.815, 0.752, and 0.805 for ICI response (ICIR) prediction in the training cohort, testing cohort, and combined cohort, respectively. A further decision curve analysis showed that the prognostic nomogram was superior to either mutation burden or neoantigen burden for overall survival prediction when the threshold probability was >0.35. The immune infiltrate analysis indicated that the low ICIR-Score values in mUC patients were significantly related to CD8+ T cell infiltration and immune checkpoint-associated signatures. We also identified differentially mutated genes, which could act as driver genes and regulate the response to ICI therapy. In conclusion, we developed and validated an immunotherapy-responsive nomogram for mUC patients, which could be conveniently used for the estimate of ICI response and the prediction of overall survival probability for mUC patients.

Chen Siteng, Zhang Ning, Wang Tao, Zhang Encheng, Wang Xiang, Zheng Junhua

2020

PD-L1, machine learning, metastatic urothelial carcinoma, nomogram, response

General General

Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs.

In Frontiers in immunology ; h5-index 100.0

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/.

Springer Ido, Besser Hanan, Tickotsky-Moskovitz Nili, Dvorkin Shirit, Louzoun Yoram

2020

TCR repertoire analysis, autoencoder (AE), deep learning, epitope specificity, evaluation methods, long short-term memory (LSTM), machine learning

Radiology Radiology

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

In Frontiers in endocrinology ; h5-index 55.0

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP.

Zhou Jiamin, Damasceno Pablo F, Chachad Ravi, Cheung Justin R, Ballatori Alexander, Lotz Jeffrey C, Lazar Ann A, Link Thomas M, Fields Aaron J, Krug Roland

2020

biomarkers, bone marrow fat, deep learning, magnetic resonance imaging, segmentation, spine imaging

Radiology Radiology

Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.

In Frontiers in neurology

Background and Purpose: Stroke-related functional risk scores are used to predict patients' functional outcomes following a stroke event. We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment. Methods: Data were from the Precise and Rapid Assessment of Collaterals with Multi-phase CT Angiography (PROVE-IT), an observational study of 614 ischemic stroke patients. Regression and machine learning models, including random forest (RF), classification and regression tree (CART), C5.0 decision tree (DT), support vector machine (SVM), adaptive boost machine (ABM), least absolute shrinkage and selection operator (LASSO) logistic regression, and logistic regression models were used to train and predict the 90-day functional impairment risk, which is measured by the modified Rankin scale (mRS) score > 2. The models were internally validated using split-sample cross-validation and externally validated in the INTERRSeCT cohort study. The accuracy of these models was evaluated using the area under the receiver operating characteristic curve (AUC), Matthews Correlation Coefficient (MCC), and Brier score. Results: Of the 614 patients included in the training data, 249 (40.5%) had 90-day functional impairment (i.e., mRS > 2). The median and interquartile range (IQR) of age and baseline NIHSS scores were 77 years (IQR = 69-83) and 17 (IQR = 11-22), respectively. Both logistic regression and machine learning models had comparable predictive accuracy when validated internally (AUC range = [0.65-0.72]; MCC range = [0.29-0.42]) and externally (AUC range = [0.66-0.71]; MCC range = [0.34-0.42]). Conclusions: Machine learning algorithms and logistic regression had comparable predictive accuracy for predicting stroke-related functional impairment in stroke patients.

Alaka Shakiru A, Menon Bijoy K, Brobbey Anita, Williamson Tyler, Goyal Mayank, Demchuk Andrew M, Hill Michael D, Sajobi Tolulope T

2020

acute ischemic stroke, clinical risk prediction, discrimination calibration, functional outcome, machine learning

General General

Mitigating the Impact of the Novel Coronavirus Pandemic on Neuroscience and Music Research Protocols in Clinical Populations.

In Frontiers in psychology ; h5-index 92.0

The COVID-19 disease and the systemic responses to it has impacted lives, routines and procedures at an unprecedented level. While medical care and emergency response present immediate needs, the implications of this pandemic will likely be far-reaching. Most practices that the clinical research within neuroscience and music field rely on, take place in hospitals or closely connected clinical settings which have been hit hard by the contamination. So too have its preventive and treatment measures. This means that clinical research protocols may have been altered, postponed or put in complete jeopardy. In this context, we would like to present and discuss the problems arising under the current crisis. We do so by critically approaching an online discussion facilitated by an expert panel in the field of music and neuroscience. This effort is hoped to provide an efficient basis to orient ourselves as we begin to map the needs and elements in this field of research as we further propose ideas and solutions on how to overcome, or at least ease the problems and questions we encounter or will encounter, with foresight. Among others, we hope to answer questions on technical or social problems that can be expected, possible solutions and preparatory steps to take in order to improve or ease research implementation, ethical implications and funding considerations. Finally, we further hope to facilitate the process of creating new protocols in order to minimize the impact of this crisis on essential research which may have the potential to relieve health systems.

Papatzikis Efthymios, Zeba Fathima, Särkämö Teppo, Ramirez Rafael, Grau-Sánchez Jennifer, Tervaniemi Mari, Loewy Joanne

2020

COVID-19, music and neuroscience, music and neuroscience research protocols, music therapy, research crisis response

General General

A Brain-Inspired Model of Theory of Mind.

In Frontiers in neurorobotics

Theory of mind (ToM) is the ability to attribute mental states to oneself and others, and to understand that others have beliefs that are different from one's own. Although functional neuroimaging techniques have been widely used to establish the neural correlates implicated in ToM, the specific mechanisms are still not clear. We make our efforts to integrate and adopt existing biological findings of ToM, bridging the gap through computational modeling, to build a brain-inspired computational model for ToM. We propose a Brain-inspired Model of Theory of Mind (Brain-ToM model), and the model is applied to a humanoid robot to challenge the false belief tasks, two classical tasks designed to understand the mechanisms of ToM from Cognitive Psychology. With this model, the robot can learn to understand object permanence and visual access from self-experience, then uses these learned experience to reason about other's belief. We computationally validated that the self-experience, maturation of correlate brain areas (e.g., calculation capability) and their connections (e.g., inhibitory control) are essential for ToM, and they have shown their influences on the performance of the participant robot in false-belief task. The theoretic modeling and experimental validations indicate that the model is biologically plausible, and computationally feasible as a foundation for robot theory of mind.

Zeng Yi, Zhao Yuxuan, Zhang Tielin, Zhao Dongcheng, Zhao Feifei, Lu Enmeng

2020

brain inspired model, connection maturation, false-belief task, inhibitory control, self-experience, theory of mind