scholarly journals ImaGene: A web-based software platform for tumor radiogenomic evaluation and reporting

2021 ◽  
Author(s):  
Shrey S. Sukhadia ◽  
Aayush Tyagi ◽  
Vivek Venkataraman ◽  
Pritam Mukherjee ◽  
AP Prathosh ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Clinical Decision Making ◽  
Tumor Imaging ◽  
Ease Of Use ◽  
Imaging Features ◽  
Omics Data ◽  
Software Platform ◽  
Imaging Data ◽  
Web Based ◽  
Complete Control

ABSTRACTThe field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Potential associations between tumor genotype and phenotype can be identified from imaging and omics data via traditional correlation analysis, as well as through artificial intelligence (AI) models. However, at present the radiogenomics community lacks a unified software platform for which to conduct such analyses in a reproducible manner.To address this gap, we propose ImaGene, a web-based platform that takes tumor omics and imaging data sets as input, performs correlation analysis between them, and constructs AI models (optionally using only those features found to exhibit statistically significant correlation with some element of the opposing dataset). ImaGene has several modifiable configuration parameters, providing users complete control over their analysis. For each run, ImaGene produces a comprehensive report displaying a number of intuitive model diagnostics.To demonstrate the utility of ImaGene, exploratory studies surrounding Invasive Breast Carcinoma (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) on datasets acquired from public databases are conducted. Potential associations are identified between several imaging features and 6 genes: CRABP1, SMTNL2, FABP1, HAS1, FAM163A and DSG1 for IBC, and 4 genes: CEACAM6, NANOG, ACSM2B, and UPK2 for HNSCC.In summary, the software provides researchers with a transparent tool for which to begin radiogenomic analysis and explore possible further directions in their research. We anticipate that ImaGene will become the standard platform for tumor analyses in the field of radiogenomics due to its ease of use, flexibility, and reproducibility, and that it can serve as an enabling centrepoint for an emerging radiogenomic knowledge base.

scholarly journals Radiogenomics and Radiomics in Liver Cancers

Diagnostics ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Aman Saini ◽  
Ilana Breen ◽  
Yash Pershad ◽  
Sailendra Naidu ◽  
M. Knuttinen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Molecular Profile ◽  
Imaging Features ◽  
Imaging Data ◽  
Prognostic Information ◽  
Cross Sectional ◽  
Related Field ◽  
Liver Cancers

Radiogenomics is a computational discipline that identifies correlations between cross-sectional imaging features and tissue-based molecular data. These imaging phenotypic correlations can then potentially be used to longitudinally and non-invasively predict a tumor’s molecular profile. A different, but related field termed radiomics examines the extraction of quantitative data from imaging data and the subsequent combination of these data with clinical information in an attempt to provide prognostic information and guide clinical decision making. Together, these fields represent the evolution of biomedical imaging from a descriptive, qualitative specialty to a predictive, quantitative discipline. It is anticipated that radiomics and radiogenomics will not only identify pathologic processes, but also unveil their underlying pathophysiological mechanisms through clinical imaging alone. Here, we review recent studies on radiogenomics and radiomics in liver cancers, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastases to the liver.

scholarly journals Efficient data management infrastructure for the integration of imaging and omics data in life science research

2019 ◽  
Author(s):  
Luis Kuhn Cuellar ◽  
Andreas Friedrich ◽  
Gisela Gabernet ◽  
Luis de la Garza ◽  
Sven Fillinger ◽  
...  
Keyword(s):  
Data Management ◽  
Biomedical Imaging ◽  
Science Research ◽  
Integrated System ◽  
Joint Analysis ◽  
Omics Data ◽  
Data Generation ◽  
Imaging Data ◽  
Data Repositories ◽  
Web Based

AbstractAs technical developments in omics and biomedical imaging drive the increase in quality, modality, and throughput of data generation in life sciences, the need for information systems capable of integrative, long-term storage and management of these heterogeneous digital assets is also increasing. Here, we propose an approach based on principles of Service Oriented Architecture design, to allow the integrated management and analysis of multi-omics and biomedical imaging data. The proposed architecture introduces an interoperable image management system, the OMERO server, into the backend of qPortal, a FAIR-compliant web-based platform for omics data management. The implementation of an integrated metadata model, the development of software components to enable the communication with the OMERO server, and an extension to the data management operations of established software, allows for FAIR management of heterogeneous omics and biomedical imaging data within an integrated system, which facilitates metadata queries from web-based scientific applications. The applicability of the proposed architecture is demonstrated in two prototypical use cases, a plant biology study using confocal scanning microscopy, and a clinical study on hepatocellular carcinoma, with data from a variety of medical imaging and omics modalities. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, as the joint analysis of omics and imaging data becomes not only desirable but necessary to derive novel insights into biological processes. In particular for powerful machine learning applications where the availability of large datasets with high quality phenotypic annotations is a requirement.

scholarly journals In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis

2019 ◽  
Vol 20 (23) ◽  
pp. 5825 ◽  
Author(s):  
Francesca Gallivanone ◽  
Claudia Cava ◽  
Fabio Corsi ◽  
Gloria Bertoli ◽  
Isabella Castiglioni
Keyword(s):  
Breast Cancer ◽  
Differential Diagnosis ◽  
Regulatory Networks ◽  
Tumor Imaging ◽  
Tumor Development ◽  
Computational Approach ◽  
Imaging Features ◽  
Imaging Data ◽  
Luminal A ◽  
Mirna Expression Data

Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. Background: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. Methods: We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype. Results: We found six miRNAs correlated with imaging features in Luminal A (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone. Conclusion: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.

scholarly journals Facilitators and barriers to the utilization of the ACT SMART Implementation Toolkit in community-based organizations: a qualitative study

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Aksheya Sridhar ◽  
Amy Drahota ◽  
Kiersten Walsworth
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Autism Spectrum ◽  
Ease Of Use ◽  
Science Literature ◽  
Community Based ◽  
Web Based ◽  
Community Based Organizations ◽  
Facilitators And Barriers ◽  
Acceptance Model

Abstract Background Evidence-based practices (EBPs) have been shown to improve behavioral and mental health outcomes for children diagnosed with autism spectrum disorder (ASD). Research suggests that the use of these practices in community-based organizations is varied; however, the utilization of implementation guides may bridge the gap between research and practice. The Autism Community Toolkit: Systems to Measure and Adopt Research-Based Treatments (ACT SMART) Implementation Toolkit is a web-based implementation toolkit developed to guide organization-based implementation teams through EBP identification, adoption, implementation, and sustainment in ASD community-based organizations. Methods This study examined the facilitators and barriers (collectively termed “determinants”) to the utilization of this toolkit, based on the perspectives of implementation teams at six ASD community-based organizations. Two independent coders utilized the adapted EPIS framework and the Technology Acceptance Model 3 to guide qualitative thematic analyses of semi-structured interviews with implementation teams. Results Salient facilitators (e.g., facilitation teams, facilitation meetings, phase-specific activities) and barriers (e.g., website issues, perceived lack of ease of use of the website, perceived lack of resources, inner context factors) were identified, highlighting key determinants to the utilization of this toolkit. Additionally, frequent determinants and determinants that differed across adapted EPIS phases of the toolkit were noted. Finally, analyses highlighted two themes: (a) Inner Context Determinants to use of the toolkit (e.g., funding) and (b) Innovation Determinants (e.g., all website-related factors), indicating an interaction between the two models utilized to guide study analyses. Conclusions Findings highlighted several factors that facilitated the utilization of this implementation guide. Additionally, findings identified key areas for improvement for future iterations of the ACT SMART Implementation Toolkit. Importantly, these results may inform the development, refinement, and utilization of implementation guides with the aim of increasing the uptake of EBPs in community-based organizations providing services to children with ASD and their families. Finally, these findings contribute to the implementation science literature by illustrating the joint use of the EPIS framework and Technology Acceptance Model 3 to evaluate the implementation of a web-based toolkit within community-based organizations.

scholarly journals Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

2016 ◽  
Vol 18 (12) ◽  
pp. 1680-1687 ◽  
Author(s):  
Ken Chang ◽  
Biqi Zhang ◽  
Xiaotao Guo ◽  
Min Zong ◽  
Rifaquat Rahman ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Recurrent Glioblastoma ◽  
Machine Learning Techniques ◽  
Imaging Biomarkers ◽  
Imaging Features ◽  
Mri Features ◽  
Fluid Attenuated Inversion Recovery ◽  
Multimodal Mri

Abstract Background Bevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab. Methods The aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model. Results Using standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively. Conclusion With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.

scholarly journals Radiomics in radiation oncology—basics, methods, and limitations

2020 ◽  
Vol 196 (10) ◽  
pp. 848-855
Author(s):  
Philipp Lohmann ◽  
Khaled Bousabarah ◽  
Mauritius Hoevels ◽  
Harald Treuer
Keyword(s):  
Mathematical Models ◽  
Quantitative Imaging ◽  
Human Perception ◽  
Image Features ◽  
Radiotherapy Planning ◽  
Large Field ◽  
Imaging Features ◽  
Imaging Data ◽  
Complete Evaluation ◽  
Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

Presentation of Self in E-veryday Life: How People Labelled with Intellectual Disability Manage Identity as They Engage the Blogosphere

2008 ◽  
Vol 13 (4) ◽  
pp. 40-52 ◽  
Author(s):  
Alex McClimens ◽  
Frances Gordon
Keyword(s):  
Intellectual Disability ◽  
Mass Communication ◽  
Sense Of Self ◽  
Ease Of Use ◽  
Presentation Of Self ◽  
Web Based ◽  
Sociological Inquiry ◽  
Cultural Construct ◽  
Field Of Inquiry ◽  
The Web

Very little is known about the online habits of people labelled with intellectual disability. What little information there is focuses more on demographic descriptors rather than any analyses of issues specific to that group. Hence the vast majority of the literature is firmly focused on more generic issues as they affect the general population. Some very few disability dedicated studies, however, have examined homepages maintained by individuals who live with Down syndrome. Here at least is evidence of a field of inquiry that recognises there may be particular aspects of web based communications that deserve special interest. The dynamics of web based communications are fast moving and the relatively static homepage has subsequently given way to Web 2.0 technologies. Here the recent and exponential increase in the popularity of blogging as a means of mass communication has attracted much comment in both popular and specialist quarters. Its ease of use and near universal availability has prompted massive sociological inquiry. But again the profile of people living with intellectual disability is absent from the debate. Our study reports on a project in which adults with intellectual disability were assisted to access the web in general, and the ‘blogosphere’ in particular. Our focus is on the means and methods by which the participants were able to manage their off and online identities. We look at the language employed, the layouts used and the way the online messages and postings reflected or distorted the actual lived experiences of these proto-bloggers. Notions of authorship and audience also contribute to the debate as these issues raise questions about sense of self, disability as a cultural construct and our ability to negotiate the increasingly important virtual world of the web.

scholarly journals A Web-Based Software Platform for Restoration-Oriented Species Selection Based on Plant Functional Traits

2021 ◽  
Vol 9 ◽  
Author(s):  
Chen Wang ◽  
Shuguang Jian ◽  
Hai Ren ◽  
Junhua Yan ◽  
Nan Liu
Keyword(s):  
Plant Species ◽  
Functional Traits ◽  
Selection Process ◽  
Plant Functional Traits ◽  
Species Selection ◽  
Software Platform ◽  
Web Based ◽  
Successful Case ◽  
The Future ◽  
Degraded Ecosystems

Plant functional traits are fundamental to the understanding of plant adaptations and distributions. Recently, scientists proposed a trait-based species selection theory to support the selection of suitable plant species to restore the degraded ecosystems, to prevent the invasive exotic species and to manage the sustainable ecosystems. Based on this theory, in a previous study, we developed a species screening model and successfully applied it to a project where plant species were selected for restoring a tropical coral island. However, during this process we learned that a software platform is necessary to automate the selection process because it can flexible to assist users. Here, we developed a generalized software platform called the “Restoration Plant Species Selection (RPSS) Platform.” This flexible software is designed to assist users in selecting plant species for particular purposes (e.g., restore the degraded ecosystems and others). It is written in R language and integrated with external R packages, including the packages that computing similarity indexes, providing graphic outputs, and offering web functions. The software has a web-based graphical user interface that allows users to execute required functions via checkboxes and buttons. The platform has cross-platform functionality, which means that it can run on all common operating systems (e.g., Windows, Linux, macOS, and others). We also illustrate a successful case study in which the software platform was used to select suitable plant species for restoration purpose. The objective of this paper is to introduce the newly developed software platform RPSS and to provide useful guidances on using it for various applications. At this step, we also realized that the software platform should be constantly updated (e.g., add new features) in the future. Based on the existing successful application and the possible updates, we believe that our RPSS software platform will have broader applications in the future.

scholarly journals E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

2016 ◽  
Author(s):  
Maia A. Smith ◽  
Cydney Nielsen ◽  
Fong Chun Chan ◽  
Andrew McPherson ◽  
Andrew Roth ◽  
...  
Keyword(s):  
Single Cell ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Treatment Resistance ◽  
Cancer Evolution ◽  
Imaging Data ◽  
Web Based ◽  
Clinical Endpoints ◽  
Cell Experiment ◽  
Spatio Temporal

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

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