Name: guidelines and experience using imaging biomarker explorer (ibex) for radiomics
Uploaded: 2018-01-08T15:00:00.000+00:00
Duration: 10 min 17 s
Description: Watch this Scientific Journal Video about Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics at JoVE.com

Rachel Ger est financé par le B. de Rosalie Hite Graduate Fellowship et American Légion auxiliaire Fellowship. Carlos Cardenas a été financé par la bourse de George M. Stancel PhD en Sciences biomédicales. Le développement de l’IBEX a été financé par le NCI (R03 CA178495).

<ol>
	<li>Lambin, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Radiomics:+extracting+more+information+from+medical+images+using+advanced+feature+analysis.">Radiomics: extracting more information from medical images using advanced feature analysis.</a> EJC. 48, 441-446 (2012).</li><li>Zhang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IBEX:+an+open+infrastructure+software+platform+to+facilitate+collaborative+work+in+radiomics.">IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.</a> Med Phys. 42, 1341-1353 (2015).</li><li>Haralick, R. M., Shanmugam, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Textural+features+for+image+classification.">Textural features for image classification.</a> IEEE Trans Syst Man Cybern. , 610-621 (1973).</li><li>Galloway, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texture+analysis+using+gray+level+run+lengths.">Texture analysis using gray level run lengths.</a> Comp Graphics and Im Proc. 4, 172-179 (1975).</li><li>Tang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texture+information+in+run-length+matrices.">Texture information in run-length matrices.</a> IEEE Trans on Im Proc. 7, 1602-1609 (1998).</li><li>Amadasun, M., King, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Textural+features+corresponding+to+textural+properties.">Textural features corresponding to textural properties.</a> IEEE Trans Syst Man Cybern. 19, 1264-1274 (1989).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preliminary+investigation+into+sources+of+uncertainty+in+quantitative+imaging+features.">Preliminary investigation into sources of uncertainty in quantitative imaging features.</a> Comp Med Imaging Graph. 44, 54-61 (2015).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+radiomics+features+be+reproducibly+measured+from+CBCT+images+for+patients+with+non-small+cell+lung+cancer.">Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer.</a> Med Phys. 42, 6784-6797 (2015).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+image+preprocessing+on+the+volume+dependence+and+prognostic+potential+of+radiomics+features+in+non-small+cell+lung+cancer.">Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer.</a> Trans Cancer Res. 5, 349-363 (2016).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Delta-radiomics+features+for+the+prediction+of+patient+outcomes+in+non-small+cell+lung+cancer.">Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer.</a> Scientific Reports. 7, 588 (2017).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stage+III+Non-Small+Cell+Lung+Cancer:+Prognostic+Value+of+FDG+PET+Quantitative+Imaging+Features+Combined+with+Clinical+Prognostic+Factors.">Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.</a> Radiology. 278, 214-222 (2016).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+Use+of+(18)F-fluorodeoxyglucose+Positron+Emission+Tomography-Based+Quantitative+Imaging+Features+for+Guiding+Dose+Escalation+in+Stage+III+Non-Small+Cell+Lung+Cancer.">Potential Use of (18)F-fluorodeoxyglucose Positron Emission Tomography-Based Quantitative Imaging Features for Guiding Dose Escalation in Stage III Non-Small Cell Lung Cancer.</a> IJROBP. 94, 368-376 (2016).</li><li>Mackin, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+Computed+Tomography+Scanner+Variability+of+Radiomics+Features.">Measuring Computed Tomography Scanner Variability of Radiomics Features.</a> Invest radiol. 50, 757-765 (2015).</li><li>Yang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Uncertainty+analysis+of+quantitative+imaging+features+extracted+from+contrast-enhanced+CT+in+lung+tumors.">Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors.</a> Comp Med Imaging Graph. 48, 1-8 (2016).</li><li>van Rossum, P. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+incremental+value+of+subjective+and+quantitative+assessment+of+18F-FDG+PET+for+the+prediction+of+pathologic+complete+response+to+preoperative+chemoradiotherapy+in+esophageal+cancer.">The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer.</a> JNM. 57, 691-700 (2016).</li><li>Hunter, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NSCLC+tumor+shrinkage+prediction+using+quantitative+image+features.">NSCLC tumor shrinkage prediction using quantitative image features.</a> Comp Med Imaging Graph. 49, 29-36 (2016).</li><li>Hunter, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+quality+machine-robust+image+features:+identification+in+nonsmall+cell+lung+cancer+computed+tomography+images.">High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images.</a> Med Phys. 40, 121916 (2013).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prognostic+value+and+reproducibility+of+pretreatment+CT+texture+features+in+stage+III+non-small+cell+lung+cancer.">Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.</a> IJROBP. 90, 834-842 (2014).</li><li>Gan, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MO-DE-207B-09:+A+Consistent+Test+for+Radiomics+Softwares.">MO-DE-207B-09: A Consistent Test for Radiomics Softwares.</a> Med Phys. 43, 3706-3706 (2016).</li><li>Klawikowski, S., Christian, J., Schott, D., Zhang, M., Li, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SU-D-207B-07:+Development+of+a+CT-Radiomics+Based+Early+Response+Prediction+Model+During+Delivery+of+Chemoradiation+Therapy+for+Pancreatic+Cancer.">SU-D-207B-07: Development of a CT-Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer.</a> Med Phys. 43, 3350-3350 (2016).</li><li>Huang, W., Tu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SU-F-R-22:+Malignancy+Classification+for+Small+Pulmonary+Nodules+with+Radiomics+and+Logistic+Regression.">SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression.</a> Med Phys. 43, 3377-3378 (2016).</li><li>Hanania, A. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+imaging+to+evaluate+malignant+potential+of+IPMNs.">Quantitative imaging to evaluate malignant potential of IPMNs.</a> Oncotarget. 7, 85776-85784 (2016).</li><li>Court, L. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+resources+for+radiomics.">Computational resources for radiomics.</a> Trans Cancer Res. 5, 340-348 (2016).</li><li>. Matlab Add path Available from: <a target="_blank" href="https://www.mathworks.com/help/matlab/ref/addpath.html">https://www.mathworks.com/help/matlab/ref/addpath.html</a> (2017)</li><li>Zhao, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+of+radiomics+for+deciphering+tumor+phenotype+with+imaging.">Reproducibility of radiomics for deciphering tumor phenotype with imaging.</a> Sci Rep. 6, 23428 (2016).</li><li>Owens, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+and+Robustness+of+Radiomic+Features+Extracted+with+Semi-Automatic+Segmentation+Tools.">Reproducibility and Robustness of Radiomic Features Extracted with Semi-Automatic Segmentation Tools.</a> Med Phys. , (2017).</li><li>Kassner, A., Liu, F., Thornhill, R. E., Tomlinson, G., Mikulis, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+hemorrhagic+transformation+in+acute+ischemic+stroke+using+texture+analysis+of+postcontrast+T1-weighted+MR+images.">Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1-weighted MR images.</a> JMRI. 30, 933-941 (2009).</li><li>. IBEX Google Forum Available from: <a target="_blank" href="https://groups.google.com/forum/#!forum/ibex_users">https://groups.google.com/forum/#!forum/ibex_users</a> (2017)</li></ol>

Les auteurs n’ont aucun intérêt financier concurrentes de divulguer.

Bouquetin est un outil puissant pour la recherche en imagerie médicale radiomics. Il a jusqu’ici surtout été utilisé à des fins d’oncologie de rayonnement dans les études menées par le groupe de radiomics MD Anderson. IBEX permet pour la manipulation des ROIs et le calcul des caractéristiques au sein de 5 catégories de caractéristique principale. La version de code source de BOUQUETINS permet à l’utilisateur pour les applications de conception qui ne font pas déjà partie du bouquetin, par exemple des ?...

En médecine, diagnostic des patients généralement intègre un grand nombre d’examens diagnostiques comme les rayons x, échographie, la tomodensitométrie (TDM), imagerie (IRM), par résonance magnétique et de tomographie par émission de positrons (PET) scanne pour aider à déterminer la cours de soins aux patients. Alors que les médecins utilisent ces images pour évaluer qualitativement diagnostic du patient, il peut y avoir des caractéristiques quantitatives supplémentaires qui peuvent être extraites afin de guider les soins aux patients. Le raisonnement est que ces fonctionnalités peuvent représenter de protéomique et génomiques motifs exprimés à l' échelle macroscopique1. Combinant ces informations quantitatives avec les informations cliniques actuelles, par exemple, données sur le patients, peut permettre plus individualisée des soins aux patients. Il s’agit de la théorie derrière radiomics : analyse d’images sur un niveau de voxel en vedette. Les caractéristiques généralement regroupés en 5 catégories : matrice de co-occurrence des niveaux, la matrice de longueur de course niveau gris, quartier intensité différence matrice, histogramme et forme de gris.
Imagerie des biomarqueurs Explorer (IBEX) est un outil open source pour radiomics travail2. L’interface utilisateur graphique (GUI) a été développé au MD Anderson Cancer Center, dans le but de faciliter l’extraction et le calcul des caractéristiques quantitatives afin d’aider à la prise de décision dans le traitement du cancer. Une source code3 et une version stand-alone4 sont disponibles en ligne. IBEX calcule 5 catégories principales fonctionnalités utilisées en médecine radiomics avec des paramètres qui peuvent être définies pour chaque catégorie de fonction. Les catégories sont : gris de matrice de co-occurrence des niveaux5, longueur de course niveau gris matrice6,7, intensité, quartier intensité différence matrice8et forme. IBEX est open source, il permet pour résultats d’extraction harmonisées dans les institutions de comparer facilement les différents radiomics études. Toutes les fonctions au sein de l’IBEX sont décrits dans le document initial par Zhang et al. 2
Le but de ce manuscrit est de fournir des conseils sur la façon d’utiliser IBEX et de démontrer ses applications par le biais de pairs des études publiées par le groupe de radiomics MD Anderson. Depuis sa sortie au public en 2015, IBEX a été utilisé pour calculer les caractéristiques de CT, MRI et PET scan images par le groupe de radiomics MD Anderson, étudie généralement des fonctionnalités pour améliorer la survie clinique modèles9,10, 11,12,13,14,15,16,17,18,19,20 et par les institutions extérieures21,22,23,24. On trouvera des indications supplémentaires sur les outils logiciels qui peuvent être utilisés pour les étapes de recherche de radiomics qui ne sont pas inclus dans l’IBEX en Cour et al. 25
Une introduction générale au workflow de BOUQUETINS vous aidera à organiser les données correctement avant de commencer le radiomics projets utilisant IBEX. Si l’importation d’images DICOM, IBEX exige que chaque patient aient leur propre dossier avec leurs images DICOM. DICOM rayonnement structure ensemble est facultatif d’inclure dans le dossier patient, mais il est recommandé plutôt que d’utiliser la plate-forme de contournage IBEX. Pour faciliter l’importation de tous les patients pour une étude spécifique, tous les dossiers de patients peuvent être assemblés dans un seul dossier afin que toutes les données peuvent être importées dans l’IBEX en utilisant qu’une seule étape. Si importer les patients de Pinnacle, il est préférable d’avoir la structure définie avec le plan de la patient. Car les patients peuvent avoir plusieurs ensembles d’images et de plans au sein de Pinnacle, c’est mieux de savoir quel ensemble images et plan sont corrects avant de l’importer. Si le temps de calcul est un sujet de préoccupation, la réduction du nombre de tranches de l’image pour un patient peut réduire considérablement temps. Par exemple, si seul le foie est d’un intérêt dans une étude, mais les patients ont complet du corps CT scans, réduisant les tranches DICOM à seulement l’étendue de la zone d’intérêt peuvent raccourcir le temps de calcul (p. ex., réduction de la DICOM de tranches de 300 à 50 tranches peut prendre 1/6 t h la fois). Il existe différents outils disponibles pour réaliser la réduction de cette tranche, manuelle ou semi-automatique.

<table><tbody><tr><td>Excel</td><td>Microsoft Office</td><td></td><td>Any version of excel should work.</td></tr><tr><td>Matlab</td><td>MathWorks</td><td></td><td>Only use IBEX on 32 bit Matlab 2011a or 64 bit Matlab 2014b.</td></tr></tbody></table>

guidelines and experience using imaging biomarker explorer (ibex) for radiomics

Imagerie des biomarqueurs Explorer (IBEX) est un outil open source pour le travail de radiomics d’imagerie médicale. Le but de cet article est de décrire la façon d’utiliser l’interface graphique (GUI) de BOUQUETINS et de démontrer comment IBEX calculé caractéristiques ont été utilisés dans les études cliniques. IBEX permet pour l’importation d’images DICOM DICOM radiation therapy structure fichiers ou Pinnacle. Une fois que les images sont importées, IBEX a outils dans le GUI de sélection de données pour manipuler l’affichage des images, les valeurs de mesure voxel et distances et de créer et de modifier le périmètre de rayonnement. IBEX est livré avec 27 choix de fonction prétraitement et 132 à concevoir des ensembles de fonctionnalités. Chaque catégorie de prétraitement et de fonctionnalité possède les paramètres pouvant être modifiés. La sortie de l’IBEX est une feuille de calcul qui contient : 1) chaque fonctionnalité de la fonction de la valeur calculée pour chaque contour dans un ensemble de données, les informations d’image 2) sur chaque contour dans un ensemble de données et 3) un résumé du prétraitement et fonctionnalités utilisées avec leurs sélectionnés paramètres. Caractéristiques calculées à partir de BOUQUETINS ont été utilisés dans des études pour tester la variabilité des caractéristiques dans des conditions différentes d’imagerie et des modèles de survie afin d’améliorer les modèles cliniques actuels.

La sortie de l’IBEX est une feuille de calcul (voir Figure 4) qui contient 3 onglets. L’onglet « Résultats » contient les valeurs de fonction pour chaque ROI dans l’ensemble de données (Figure 4A). L’onglet « Données d’informations. » contient des informations sur les images prises de chaque ROI dans l’ensemble de données (Figure 4B). L’ongl...

Watch this Scientific Journal Video about Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics at JoVE.com

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Nous décrivons des BOUQUETINS, un outil open source conçu pour des études d’imagerie médicales de radiomics et comment utiliser cet outil. En outre, certaines œuvres publiées qui ont utilisé IBEX pour l’analyse de l’incertitude et le modèle de bâtiment sont à l’honneur.

Lignes directrices et l’expérience à l’aide d’imagerie biomarqueur Explorer (IBEX) pour Radiomics

Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's ...

guidelines-experience-using-imaging-biomarker-explorer-ibex-for

Research

JoVE Journal

Engineering

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics

13.1K vues.  The University of Texas MD Anderson Cancer Center. Nous décrivons des BOUQUETINS, un outil open source conçu pour des études d’imagerie médicales de radiomics et comment utiliser cet outil. En outre, certaines œuvres publiées qui ont utilisé IBEX pour l’analyse de l’incertitude et le modèle de bâtiment sont à l’honneur.

Vidéo: Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring 

Nanomedications can be carried by blood borne monocyte-macrophages into the reticuloendothelial system (RES; spleen, liver, lymph nodes) and to end organs. The latter include the lung, RES, and brain and are operative during human immunodeficiency virus type one (HIV-1) infection. Macrophage entry into tissues is notable in areas of active HIV-1 replication and sites of inflammation. In order to assess the potential of macrophages as nanocarriers, superparamagnetic iron-oxide and/or drug laden particles coated with surfactants were parenterally injected into HIV-1 encephalitic mice. This was done to quantitatively assess particle and drug biodistribution. Magnetic resonance imaging (MRI) test results were validated by histological coregistration and enhanced image processing. End organ disease as typified by altered brain histology were assessed by MRI. The demonstration of robust migration of nanoformulations into areas of focal encephalitis provides '"proof of concept" for the use of advanced bioimaging techniques to monitor macrophage migration. Importantly, histopathological aberrations in brain correlate with bioimaging parameters making the general utility of MRI in studies of cell distribution in disease feasible. We posit that using such methods can provide a real time index of disease burden and therapeutic efficacy with translational potential to humans.

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Bioimaging methods used to assess cell biodistribution of nanoparticles are applicable for therapeutic and diagnostic monitoring of nanoformulated compounds. The methods described herein are sensitive and specific when assessed by histological coregistration. The methodologies provide a translational pathway from rodent to human applications.

Bioimaging enregistré des nanomatériaux pour la surveillance des services diagnostiques et thérapeutiques

Nanomedications can be carried by blood borne monocyte-macrophages into the reticuloendothelial system (RES; spleen, liver, lymph nodes) and to end ...

Recherche

Bio-ingénierie

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Existing algorithms generate one solution for a biomarker detection dataset. This protocol demonstrates the existence of multiple similarly effective solutions and presents a user-friendly software to help biomedical researchers investigate their datasets for the proposed challenge. Computer scientists may also provide this feature in their biomarker detection algorithms.

Sélection de plusieurs sous-ensembles de biomarqueur de même Classification binaire efficace performances

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker ...

Recherche en cancérologie

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

In recent years, emerging databases were designed to lower the barriers for approaching the intricate cancer genomic datasets, thereby, facilitating investigators to analyze and interpret genes, samples and clinical data across different types of cancer. Herein, we describe a practical operation procedure, taking ID1 (Inhibitor of DNA binding proteins 1) as an example, to characterize the expression patterns of biomarker and survival predictors of breast cancer based on pooled clinical datasets derived from online accessible databases, including ONCOMINE, bcGenExMiner v4.0 (Breast cancer gene-expression miner v4.0), GOBO (Gene expression-based Outcome for Breast cancer Online), HPA (The human protein atlas), and Kaplan-Meier plotter. The analysis began with querying the expression pattern of the gene of interest (e.g., ID1) in cancerous samples vs. normal samples. Then, the correlation analysis between ID1 and clinicopathological characteristics in breast cancer was performed. Next, the expression profiles of ID1 was stratified according to different subgroups. Finally, the association between ID1 expression and survival outcome was analyzed. The operation procedure simplifies the concept to integrate multidimensional data types at the gene level from different databases and test hypotheses regarding recurrence and genomic context of gene alteration events in breast cancer. This method can improve the credibility and representativeness of the conclusions, thereby, present informative perspective on a gene of interest.

Here, we present a protocol to explore the biomarker and survival predictor of breast cancer based on the comprehensive analysis of pooled clinical datasets derived from a variety of publicly accessible databases, using the strategy of expression, correlation and survival analysis step by step.

L’exploration de données et l’analyse intégrative du biomarqueur dans le cancer du sein à l’aide de plusieurs bases de données accessibles au public

In recent years, emerging databases were designed to lower the barriers for approaching the intricate cancer genomic datasets, thereby, facilitating ...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations.
The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model&#8217;s tumor-take rate is 60%&#8211;80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth.
Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups: one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

Algorithmes d’apprentissage automatique pour la détection précoce des métastases osseuses dans un modèle de rat expérimental

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy ...

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.
LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.
While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.

Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.

Lésion Explorer: Un Protocole de vidéo-guidée standardisé pour Volumetrics IRM dérivées exactes et fiables dans la maladie d&#39;Alzheimer et normale personnes âgées

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are ...

Médecine

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was established using robust, reproducible in vivo models and standardized methods to identify novel players in melanoma metastasis. This approach allows for data inference at specific experimental stages to precisely characterize a gene's role in metastasis. Models are established by introducing genetically modified melanoma cells via intracardiac, intradermal, or subcutaneous injections into mice, followed by monitoring with serial in vivo imaging. Once preestablished endpoints are reached, primary tumors and/or metastases-bearing organs are harvested and processed for various analyses. Tumor cells can be sorted and subjected to any of several 'omics' platforms, including single-cell RNA sequencing. Organs undergo imaging and immunohistopathological analyses to quantify the overall burden of metastases and map their specific anatomic location. This optimized pipeline, including standardized protocols for engraftment, monitoring, tissue harvesting, processing, and analysis, can be adopted for patient-derived, short-term cultures and established human and murine cell lines of various solid cancer types.

This article describes a workflow of techniques employed for testing novel candidate mediators of melanoma metastasis and their mechanism(s) of action.

Une plateforme de découverte robuste pour l’identification de nouveaux médiateurs de métastases de mélanome

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was ...

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically complex tumors, with a high degree of proteomic variability across both the tumor itself and its heterogenous microenvironment. The malignant potential of these tumors is enhanced by the dysregulation of proteins involved in several key pathways, including processes that maintain cellular stability and preserve the structural integrity of the microenvironment. Although there have been numerous bulk and single-cell glioma analyses, there is a relative paucity of spatial stratification of these proteomic data. Understanding differences in spatial distribution of tumorigenic factors and immune cell populations between the intrinsic tumor, invasive edge, and microenvironment offers valuable insight into the mechanisms underlying tumor proliferation and propagation. Digital spatial profiling (DSP) represents a powerful technology that can form the foundation for these important multilayer analyses.
DSP is a method that efficiently quantifies protein expression within user-specified spatial regions in a tissue specimen. DSP is ideal for studying the differential expression of multiple proteins within and across regions of distinction, enabling multiple levels of quantitative and qualitative analysis. The DSP protocol is systematic and user-friendly, allowing for customized spatial analysis of proteomic data. In this experiment, tissue microarrays are constructed from archived glioblastoma core biopsies. Next, a panel of antibodies is selected, targeting proteins of interest within the sample. The antibodies, which are preconjugated to UV-photocleavable DNA oligonucleotides, are then incubated with the tissue sample overnight. Under fluorescence microscopy visualization of the antibodies, regions of interest (ROIs) within which to quantify protein expression are defined with the samples. UV light is then directed at each ROI, cleaving the DNA oligonucleotides. The oligonucleotides are microaspirated and counted within each ROI, quantifying the corresponding protein on a spatial basis.

Proteomic dysregulation plays an important role in the spread of diffusely infiltrating gliomas, but several relevant proteins remain unidentified. Digital spatial processing (DSP) offers an efficient, high-throughput approach for characterizing the differential expression of candidate proteins that may contribute to the invasion and migration of infiltrative gliomas.

Profilage spatial numérique pour la caractérisation du microenvironnement dans le gliome à infiltration diffuse de type adulte

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically ...

<meta charset="utf8"></head><body>Réponse aux rayonnements dans des organoïdes tumoraux dérivés de patients à l’aide d’un test clonogénique

Live Imaging to Quantify Cellular Radiosensitivity in Patient-Derived Tumor Organoids

Radiation therapy (RT) is one of the mainstays of modern clinical cancer management. However, not all cancer types are equally sensitive to irradiation, often (but not always) because of differences in the ability of malignant cells to repair oxidative DNA damage as elicited by ionizing rays. Clonogenic assays have been employed for decades to assess the sensitivity of cultured cancer cells to ionizing irradiation, largely because irradiated cancer cells often die in a delayed manner that is difficult to quantify with short-term flow cytometry- or microscopy-assisted techniques. Unfortunately, clonogenic assays cannot be employed as such for more complex tumor models, such as patient-derived tumor organoids (PDTOs). Indeed, irradiating established PDTOs may not necessarily abrogate their growth as multicellular units, unless their stem-like compartment is completely eradicated. Moreover, irradiating PDTO-derived single-cell suspensions may not properly recapitulate the sensitivity of malignant cells to RT in the context of established PDTOs. Here, we detail an adaptation of conventional clonogenic assays that involves exposure of established PDTOs to ionizing radiation, followed by single-cell dissociation, replating in suitable culture conditions and live imaging. Non-irradiated (control) PDTO-derived stem-like cells reform growing PDTOs with a PDTO-specific efficiency, which is negatively influenced by irradiation in a dose-dependent manner. In these conditions, PDTO-forming efficiency and growth rate can be quantified as a measure of radiosensitivity on time-lapse images collected until control PDTOs achieve a predefined space occupancy.

<meta charset="utf8"></head><body>Pleins feux sur l’auteur : Radiothérapie et tests clonogènes pour faire progresser la recherche sur le cancer et la médecine personnalisée

Here, we detail a straightforward live imaging approach for quantifying the sensitivity of patient-derived tumor organoids to ionizing radiation.

Imagerie en direct pour quantifier la radiosensibilité cellulaire dans les organoïdes tumoraux dérivés de patients

Radiation therapy (RT) is one of the mainstays of modern clinical cancer management. However, not all cancer types are equally sensitive to irradiation, ...

Lignes directrices et l’expérience à l’aide d’imagerie biomarqueur Explorer (IBEX) pour Radiomics

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