检索结果-内蒙古大学图书馆

Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

引用

Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Deep Learning Approach for Automated Detection and Characterization of Distinctly Salient Features of Alzheimers Disease

A Multimodal Deep Learning Approach for Automated Detection ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Ishaan Batta Anees Abrol Vince Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta USA

Neurological disorders generally involve multiple kinds of changes in the functional and structural properties of the brain. In this study, we develop a CNN-based multimodal deep learning pipeline by exploiting both functional and structural neuroimaging features to generate full-brain maps that encode significant differences between patient groups and between modalities in terms of their distinctive contribution towards diagnostic classification of Alzheimer’s disease. Through a repeated cross-validation procedure and robust statistical analysis, we show that our approach can be used to encode highly discriminative and abstract information from full-brain data, while also retaining the ability to identify and categorize significantly contributing voxel-level features based on their salient strength in various diagnostic and modality-related contexts. Our results on an Alzheimer’s disease classification task show that such approaches can be used for creating more elaborately defined biomarkers for brain disorders.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sex Differences in Coupled Dynamic Functional Connectivity and Structural Brain Morphology: Insights from the ABCD Study

Sex Differences in Coupled Dynamic Functional Connectivity a...

引用

IEEE International Symposium on Biomedical Imaging

作者： Aline Kotoski Sir-Lord Wiafe Vince Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta USA Neuroscience Institute Georgia State University Atlanta USA Department of Computer Science Georgia State University Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

This study investigates sex-based differences in brain structure-function coupling using a novel dynamic intermodality source coupling (dIMSC) method, which we use to integrate dynamic functional network connectivity (dFNC) and structural brain morphometry (SBM) data from the ABCD study. This study aims to examine the coupling between dFNC and sMRI gray matter volume, comparing the coupling in relation to sex differences across brain regions. By examining the coupling between dFNC and SBM matrices, we identified distinct functional-structural coupling patterns across sexes in various brain regions. Females showed stronger correlations in areas previously associated with social and language processing, such as the inferior frontal gyrus and superior parietal lobule, while males exhibited stronger correlations in regions that have previously been linked to spatial processing, including the superior temporal gyrus, lingual gyrus, and cerebellum. These findings underscore nuanced sex-specific variations in brain structure-function relationships, offering insights into distinct patterns of the brain.

关键词： Couplings Cerebellum Correlation Limiting Head Magnetic resonance imaging Morphology Grey matter Magnetic heads Hippocampus

来源：评论

学校读者我要写书评

暂无评论

SELF-Clustering Graph Transformer Approach to Model Resting State Functional Brain Activity

SELF-Clustering Graph Transformer Approach to Model Resting ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Bishal Thapaliya Esra Akbas Ram Sapkota Bhaskar Ray Vince Calhoun Jingyu Liu Department of Computer Science Georgia State University Atlanta USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the sub clusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures.

关键词： Biometrics Correlation Cognitive processes Biological system modeling Organizations Predictive models Functional magnetic resonance imaging Transformers Brain modeling Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

CGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal 46

CGDM-GAN: An Adversarial Network Approach with Self-supervis...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Cui, Xiangxiang Zhi, Dongmei Yan, Weizheng Calhoun, Vince D. Zhuo, Chuanjun Sui, Jing Beijing Normal University The State Key Lab of Cognitive Neuroscience and Learning Beijing China National Institutes of Health National Institute on Alcohol Abuse and Alcoholism Lab of Neuroimaging Bethesda United States Georgia State University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States Tianjin China Beijing Normal University IDG/McGovern Institute for Brain Research State Key Laboratory of Cognitive Neuroscience and Learning Beijing China

ISBN: (纸本)9798350371499

Imaging data collected from different sites is difficult to pool together due to unwarranted variations introduced by different acquisition protocols or scanners. data harmonization is an effective way to mitigate site-specific bias while preserving the intrinsic image properties, thereby increasing the sample size and enhancing the generalization of models. Although various harmonization methods exist, their performance on specific tasks is often unsatisfactory. Here, we proposed a novel approach, CGDM-GAN, by combining the advantages of generative models, maximum discrepancy theory, and gradient discrepancy minimization with self-supervised learning to harmonize site effects and improve cross-site classification performance. The proposed CGDM-GAN was successfully conducted on synthetic dataset, and further validated on in-house and ABCD datasets, outperforming three data harmonization methods, including ComBat, CycleGAN, and MCD-GAN, suggesting its potential for removing site effects and improving cross-site neuroimaging classification. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Multimodal Fusion of Functional and Structural data to Recognize Longitudinal Change Patterns in the Adolescent Brain

Multimodal Fusion of Functional and Structural Data to Recog...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Rekha Saha Debbrata K. Saha Zening Fu Rogers F. Silva Vince D. Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

Functional and structural magnetic resonance imaging (fMRI/sMRI) are extensively used modalities for studying brain development. While individual modalities may overlook crucial aspects of brain analysis, combining multiple modalities allows us to leverage the benefits of revealing hidden brain connections. To analyze multivariate change patterns in brain function and structure with increasing age across the entire brain, we employ a symmetric multimodal fusion approach that combines multiset canonical correlation analysis and joint independent component analysis. In this study, we present a novel approach to analyze linked longitudinal change patterns in functional network connectivity (FNC) and gray matter (GM) data derived from the large-scale Adolescent Brain and Cognitive Development dataset. Our approach uncovers significant pattern changes in both modalities. Specifically, we identify highly structured functional change patterns and structural change patterns that include increased brain functional connectivity between the visual and sensorimotor domains in the fMRI data, as well as changes in the bilateral sensorimotor cortex in the sMRI data. Overall, our study demonstrates the strength of our approach in uncovering longitudinal changes in FNC and GM, provides valuable insights into the dynamic nature of brain connectivity and structure during adolescence, and sheds light on potential gender-related differences in these processes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage data*

Improving Multichannel Raw Electroencephalography-based Diag...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Charles A. Ellis Abhinav Sattiraju Robyn L. Miller Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta USA

As the field of deep learning has grown in recent years, its application to the domain of raw resting-state electroencephalography (EEG) has also increased. Relative to traditional machine learning methods or deep learning methods applied to manually engineered features, there are fewer methods for developing deep learning models on small raw EEG datasets. One potential approach for enhancing deep learning performance, in this case, is the use of transfer learning. While a number of studies have presented transfer learning approaches for manually engineered EEG features, relatively few approaches have been developed for raw resting-state EEG. In this study, we propose a novel EEG transfer learning approach wherein we first train a model on a large publicly available single-channel sleep stage classification dataset. We then use the learned representations to develop a classifier for automated major depressive disorder diagnosis with raw multichannel EEG. Statistical testing reveals that our approach significantly improves the performance of our model (p < 0.05), and we also find that the performance of our approach exceeds that of many previous studies using both engineered features and raw EEG. We further examine how transfer learning affected the representations learned by the model through a pair of explainability analyses, identifying key frequency bands and channels utilized across models. Our proposed approach represents a significant step forward for the domain of raw resting-state EEG classification and has broader implications for use with other electrophysiology and time-series modalities. Importantly, it has the potential to expand the use of deep learning methods across a greater variety of raw EEG datasets and lead to the development of more reliable EEG classifiers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis*

Improving Explainability for Single-Channel EEG Deep Learnin...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Charles A. Ellis Robyn L. Miller Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta USA

Deep learning methods are increasingly being applied to raw electroencephalography (EEG) data. Relative to traditional machine learning methods, deep learning methods can increase model performance through automated feature extraction. Nevertheless, they are also less explainable. Existing raw EEG explainability methods identify relative feature importance but do not identify how features relate to model predictions. In this study, we combine well-characterized first layer filters with a novel post hoc statistical analysis of the filter activations that linearly relates properties of model activations with predictions. We implement our approach within the context of automated sleep stage classification, finding that the model uncovers positive relationships between waveforms resembling sleep spindles and NREM2, between high frequency filters and Awake samples, and between δ activity and NREM3. Our approach represents a significant step forward for raw EEG explainability and has the potential to provide many insights relating learned features and model predictions in future studies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis

An Explainable and Robust Deep Learning Approach for Automat...

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Abhinav Sattiraju Charles A. Ellis Robyn L. Miller Vince D. Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

Schizophrenia (SZ) is a neuropsychiatric disorder that affects millions globally. Current diagnosis of SZ is symptom-based, which poses difficulty due to the variability of symptoms across patients. To this end, many recent studies have developed deep learning methods for automated diagnosis of SZ, especially using raw EEG, which provides high temporal precision. For such methods to be productionized, they must be both explainable and robust. Explainable models are essential to identify biomarkers of SZ, and robust models are critical to learn generalizable patterns, especially amidst changes in the implementation environment. One common example is channel loss during EEG recording, which could be detrimental to classifier performance. In this study, we developed a novel channel dropout (CD) approach to increase the robustness of explainable deep learning models trained on EEG data for SZ diagnosis to channel loss. We developed a baseline convolutional neural network (CNN) architecture and implement our approach as a CD layer added to the baseline (CNN-CD). We then applied two explainability approaches to both models for insight into learned spatial and spectral features and show that the application of CD decreases model sensitivity to channel loss. The CNN and CNN-CD achieved accuracies of 81.9% and 80.9% on testing data, respectively. Furthermore, our models heavily prioritized the parietal electrodes and the a-band, which is supported by existing literature. It is our hope that this study motivates the further development of explainable and robust models and bridges the transition from research to application in a clinical decision support role.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of Schizophrenia

Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Bas...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Robyn L. Miller Victor M. Vergara Vince D. Calhoun The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS Center) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

While analysis of temporal signal fluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) research, the role of spatially localized directional diffusion in both signal propagation and emergent large-scale functional integration remains almost entirely neglected. We are proposing an extensible framework to capture and analyze spatially localized fMRI directional signal flow dynamics. The approach is validated in a large resting-state fMRI schizophrenia study where it uncovers significant and novel relationships between hyperlocal spatial dynamics and subject diagnostic status.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：