Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.
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ABSTRACT:
SUBMITTER: Kim D
PROVIDER: S-EPMC8482265 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundProbes on a microarray represent a frozen view of a genome and are quickly outdated when new sequencing studies extend our knowledge, resulting in significant measurement error when analyzing any microarray experiment. There are several bioinformatics approaches to improve probe assignments, but without in-house programming expertise, standardizing these custom array specifications as a usable file (e.g. as Affymetrix CDFs) is difficult, owing mostly to the complexity of the specification file format. However, without correctly standardized files there is a significant barrier for testing competing analysis approaches since this file is one of the required inputs for many commonly used algorithms. The need to test combinations of probe assignments and analysis algorithms led us to develop ArrayInitiative, a tool for creating and managing custom array specifications.ResultsArrayInitiative is a standalone, cross-platform, rich client desktop application for creating correctly formatted, custom versions of manufacturer-provided (default) array specifications, requiring only minimal knowledge of the array specification rules and file formats. Users can import default array specifications, import probe sequences for a default array specification, design and import a custom array specification, export any array specification to multiple output formats, export the probe sequences for any array specification and browse high-level information about the microarray, such as version and number of probes. The initial release of ArrayInitiative supports the Affymetrix 3' IVT expression arrays we currently analyze, but as an open source application, we hope that others will contribute modules for other platforms.ConclusionsArrayInitiative allows researchers to create new array specifications, in a standard format, based upon their own requirements. This makes it easier to test competing design and analysis strategies that depend on probe definitions. Since the custom array specifications are easily exported to the manufacturer's standard format, researchers can analyze these customized microarray experiments using established software tools, such as those available in Bioconductor.
| S-EPMC3113937 | biostudies-literature
Project description:IntroductionIn the field of personalized medicine, radiomics has shown its potential to support treatment decisions. However, the limited feature interpretability hampers its introduction into the clinics. Here, we propose a new methodology to create radiomics feature activation maps, which allows to identify the spatial-anatomical locations responsible for signature activation based on local radiomics. The feasibility of this technique will be studied for histological subtype differentiation (adenocarcinoma versus squamous cell carcinoma) in non-small cell lung cancer (NSCLC) using computed tomography (CT) radiomics.Materials and methodsPre-treatment CT scans were collected from a multi-centric Swiss trial (training, n=73, IIIA/N2 NSCLC, SAKK 16/00) and an independent cohort (validation, n=32, IIIA/N2/IIIB NSCLC). Based on the gross tumor volume (GTV), four peritumoral region of interests (ROI) were defined: lung_exterior (expansion into the lung), iso_exterior (expansion into lung and soft tissue), gradient (GTV border region), GTV+Rim (GTV and iso_exterior). For each ROI, 154 radiomic features were extracted using an in-house developed software implementation (Z-Rad, Python v2.7.14). Features robust against delineation variability served as an input for a multivariate logistic regression analysis. Model performance was quantified using the area under the receiver operating characteristic curve (AUC) and verified using five-fold cross validation and internal validation. Local radiomic features were extracted from the GTV+Rim ROI using non-overlapping 3x3x3 voxel patches previously marked as GTV or rim. A binary activation map was created for each patient using the median global feature value from the training. The ratios of activated/non-activated patches of GTV and rim regions were compared between histological subtypes (Wilcoxon test).ResultsIso_exterior, gradient, GTV+Rim showed good performances for histological subtype prediction (AUCtraining=0.68-0.72 and AUCvalidation=0.73-0.74) whereas GTV and lung_exterior models failed validation. GTV+Rim model feature activation maps showed that local texture feature distribution differed significantly between histological subtypes in the rim (p=0.0481) but not in the GTV (p=0.461).ConclusionIn this exploratory study, radiomics-based prediction of NSCLC histological subtypes was predominantly based on the peritumoral region indicating that radiomics activation maps can be useful for tracing back the spatial location of regions responsible for signature activation.
| S-EPMC7753181 | biostudies-literature
Project description:The need to perform gene expression profiling using next generation sequencing and quantitative real-time PCR (qPCR) on small sample sizes and single cells is rapidly expanding. However, to analyse few molecules, preamplification is required. Here, we studied global and target-specific preamplification using 96 optimised qPCR assays. To evaluate the preamplification strategies, we monitored the reactions in real-time using SYBR Green I detection chemistry followed by melting curve analysis. Next, we compared yield and reproducibility of global preamplification to that of target-specific preamplification by qPCR using the same amount of total RNA. Global preamplification generated 9.3-fold lower yield and 1.6-fold lower reproducibility than target-specific preamplification. However, the performance of global preamplification is sufficient for most downstream applications and offers several advantages over target-specific preamplification. To demonstrate the potential of global preamplification we analysed the expression of 15 genes in 60 single cells. In conclusion, we show that global preamplification simplifies targeted gene expression profiling of small sample sizes by a flexible workflow. We outline the pros and cons for global preamplification compared to target-specific preamplification.
| S-EPMC5362892 | biostudies-literature
Project description:PurposeThis study was aimed at evaluating whether a radiomics model based on the entire tumor region from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps and apparent diffusion coefficient (ADC) maps could indicate the Ki-67 status of patients with breast cancer.Materials and methodsThis retrospective study enrolled 205 women with breast cancer who underwent clinicopathological examination. Among them, 93 (45%) had a low Ki-67 amplification index (Ki-67 positivity< 14%), and 112 (55%) had a high Ki-67 amplification index (Ki-67 positivity ≥ 14%). Radiomics features were extracted from three DCE-MRI parametric maps and ADC maps calculated from two different b values of diffusion-weighted imaging sequences. The patients were randomly divided into a training set (70% of patients) and a validation set (30% of patients). After feature selection, we trained six support vector machine classifiers by combining different parameter maps and used 10-fold cross-validation to predict the expression level of Ki-67. The performance of six classifiers was evaluated with receiver operating characteristic (ROC) analysis, sensitivity, and specificity in both cohorts.ResultsAmong the six classifiers constructed, a radiomics feature set combining three DCE-MRI parametric maps and ADC maps yielded an area under the ROC curve (AUC) of 0.839 (95% confidence interval [CI], 0.768-0.895) within the training set and 0.795 (95% CI, 0.674-0.887) within the independent validation set. Additionally, the AUC value, compared with that for a single parameter map, was moderately increased by combining features from the three parametric maps.ConclusionsRadiomics features derived from the DCE-MRI parametric maps and ADC maps have the potential to serve as imaging biomarkers to determine Ki-67 status in patients with breast cancer.
| S-EPMC9989944 | biostudies-literature
Project description:Tripal is an open-source, resource-efficient toolkit for construction of genomic, genetic and breeding databases. It facilitates development of biological websites by providing tools to integrate and display biological data using the generic database schema, Chado, together with Drupal, a popular website creation and content management system. Tripal MapViewer is a new interactive tool for visualizing genetic map data. Developed as a Tripal replacement for Comparative Map Viewer (CMap), it enables visualization of entire maps or linkage groups and features such as molecular markers, quantitative trait loci (QTLs) and heritable phenotypic markers. It also provides graphical comparison of maps sharing the same markers as well as dot plot and correspondence matrices. MapViewer integrates directly with the Tripal application programming interface framework, improving data searching capability and providing a more seamless experience for site visitors. The Tripal MapViewer interface can be integrated in any Tripal map page and linked from any Tripal page for markers, QTLs, heritable morphological markers or genes. Configuration of the display is available through a control panel and the administration interface. The administration interface also allows configuration of the custom database query for building materialized views, providing better performance and flexibility in the way data is stored in the Chado database schema. MapViewer is implemented with the D3.js technology and is currently being used at the Genome Database for Rosaceae (https://www.rosaceae.org), CottonGen (https://www.cottongen.org), Citrus Genome Database (https://citrusgenomedb.org), Vaccinium Genome Database (https://www.vaccinium.org) and Cool Season Food Legume Database (https://www.coolseasonfoodlegume.org). It is also currently in development on the Hardwood Genomics Web (https://hardwoodgenomics.org) and TreeGenes (https://treegenesdb.org). Database URL: https://gitlab.com/mainlabwsu/tripal_map.
| S-EPMC6829499 | biostudies-literature
Project description:Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology. Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map. Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps. Preparation of the data and construction of a heat map is rarely a simple linear process. Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches. Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.
| S-EPMC7111501 | biostudies-literature
Project description:Quantitative radiomics features, extracted from medical images, characterize tumour-phenotypes and have been shown to provide prognostic value in predicting clinical outcomes. Stability of radiomics features extracted from apparent diffusion coefficient (ADC)-maps is essential for reliable correlation with the underlying pathology and its clinical applications. Within a multicentre, multi-vendor trial we established a method to analyse radiomics features from ADC-maps of ovarian (n = 12), lung (n = 19), and colorectal liver metastasis (n = 30) cancer patients who underwent repeated (<7 days) diffusion-weighted imaging at 1.5 T and 3 T. From these ADC-maps, 1322 features describing tumour shape, texture and intensity were retrospectively extracted and stable features were selected using the concordance correlation coefficient (CCC > 0.85). Although some features were tissue- and/or respiratory motion-specific, 122 features were stable for all tumour-entities. A large proportion of features were stable across different vendors and field strengths. By extracting stable phenotypic features, fitting-dimensionality is reduced and reliable prognostic models can be created, paving the way for clinical implementation of ADC-based radiomics.
| S-EPMC6423042 | biostudies-literature
Project description:Among the diverse location of the breakpoints (BPs) of structural variants (SVs), the breakpoints of fusion genes (FGs) are located in the gene bodies. This broken gene context provided the aberrant functional clues to study disease genesis. Many tumorigenic fusion genes have retained or lost functional or regulatory domains and these features impacted tumorigenesis. Full annotation of fusion genes aided by the visualization tool based on two gene bodies will be helpful to study the functional aspect of fusion genes. To date, a specialized tool with effective visualization of the functional features of fusion genes is not available. In this study, we built FGviewer, a tool for visualizing functional features of human fusion genes, which is available at https://ccsmweb.uth.edu/FGviewer. FGviewer gets the input of fusion gene symbols, breakpoint information, or structural variants from whole-genome sequence (WGS) data. For any combination of gene pairs/breakpoints to be involved in fusion genes, the users can search the functional/regulatory aspect of the fusion gene in the three bio-molecular levels (DNA-, RNA-, and protein-levels) and one clinical level (pathogenic-level). FGviewer will be a unique online tool in disease research communities.
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Project description:GPS navigation is commonplace in everyday life. While it has the capacity to make our lives easier, it is often used to automate functions that were once exclusively performed by our brain. Staying mentally active is key to healthy brain aging. Therefore, is GPS navigation causing more harm than good? Here we demonstrate that traditional turn-by-turn navigation promotes passive spatial navigation and ultimately, poor spatial learning of the surrounding environment. We propose an alternative form of GPS navigation based on sensory augmentation, that has the potential to fundamentally alter the way we navigate with GPS. By implementing a 3D spatial audio system similar to an auditory compass, users are directed towards their destination without explicit directions. Rather than being led passively through verbal directions, users are encouraged to take an active role in their own spatial navigation, leading to more accurate cognitive maps of space. Technology will always play a significant role in everyday life; however, it is important that we actively engage with the world around us. By simply rethinking the way we interact with GPS navigation, we can engage users in their own spatial navigation, leading to a better spatial understanding of the explored environment.
| S-EPMC8032695 | biostudies-literature
Project description:Abstract In preclinical research, histological analysis of tissue samples is often limited to qualitative or semiquantitative scoring assessments. The reliability of this analysis can be impaired by the subjectivity of these approaches, even when read by experienced pathologists. Furthermore, the laborious nature of manual image assessments often leads to the analysis being restricted to a relatively small number of images that may not accurately represent the whole sample. Thus, there is a clear need for automated image analysis tools that can provide robust and rapid quantification of histologic samples from paraffin‐embedded or cryopreserved tissues. To address this need, we have developed a color image analysis algorithm (DigiPath) to quantify distinct color features in histologic sections. We demonstrate the utility of this tool across multiple types of tissue samples and pathologic features, and compare results from our program to other quantitative approaches such as color thresholding and hand tracing. We believe this tool will enable more thorough and reliable characterization of histological samples to facilitate better rigor and reproducibility in tissue‐based analyses.
| S-EPMC8780932 | biostudies-literature