Dataset Information

Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.

ABSTRACT: Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examples. Potential clinical applications were tested on CT and MR images for the differentiation of pulmonary lesions. In these sample applications, maps of Skewness enhanced the differentiation of non-malignant lesions and non-small lung carcinoma manifestations on CT images and maps of Variance enhanced the differentiation of pulmonary lymphoma manifestations and fungal infiltrates on MR images. This new and simple method for creating parametric maps makes radiomics features visually perceivable, allows direct feature quantification by placing a region of interest, can improve the assessment of radiological images and, furthermore, can increase the use of radiomics in clinical routine.

SUBMITTER: Kim D

PROVIDER: S-EPMC8482265 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.

Kim Damon D Jensen Laura J LJ Elgeti Thomas T Steffen Ingo G IG Hamm Bernd B Nagel Sebastian N SN

Tomography (Ann Arbor, Mich.) 20210917 3

Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examp ...[more]

PMID: 34564303

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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.

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.

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.

Project description:BackgroundTo investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [18F]FDG PET metabolic uptake rate Ki parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients.MethodsWe analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP Ki parametric maps. Spearman's rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets.ResultsVia Spearman's rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann-Whitney test).ConclusionsOur results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients.

Dataset Information

Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.

Publications

Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.

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