Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA324676 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective is to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.
2016-06-07 | GSE80683 | GEO
Project description:Not all prostate cancers are visible on multiparametric MRI. The biologic basis and clinical implication of MRI visibility are unknown. We sought to identify genes associated with prognosis and MRI visibility.
2018-02-22 | GSE95369 | GEO
Project description:The aim of this study was to perform the multiscale correlation between quantitative texture features phenotype of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. Images from pre-biopsy 3T bpMRI scans in clinically localised prostate cancer (PCa) patients of various risk categories (n=15) were used to extract textural features. The genomic landscape of hypoxia-related genes expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the three important hypoxia-related genes with 28 radiomic texture features. Following this, cBioportal was accessed and a gene-oriented query was conducted to extract Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Correlation analysis using Pearson's coefficients calculated against each selected gene profile; survival analysis using Kaplan-Meier estimators were carried out. We found the quantitative bpMR imaging textural features, including histogram and grey level co-occurrence matrix (GLCM), correlated with hypoxia related genes (ANGPTL4, VEGFA, and P4HA1) seen on RNA sequencing using TempO-Seq method. Further radiogenomic analysis, including data accessed on cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcome, with a median survival of 81.11: 133.00 months in those with and without alterations of genes respectively. In summary, radiomic texture features of bpMRI in localised PCa correlate with the expression of hypoxia-related genes expression in prostate cancer. The expression data analysis showed that hypoxia-related genes are associated with poor survival.
2023-01-29 | E-MTAB-12593 | biostudies-arrayexpress
Project description:Background:To evaluate the association of multiparametric and multiregional MRI-features with key molecular characteristics in patients with newly-diagnosed glioblastoma. Methods:Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number: S-320/2012) and informed consent was waived. Preoperative MRI-features were correlated with key molecular characteristics within a single-institutional cohort of 152 patients with newly-diagnosed glioblastoma. Preoperative MRI-features (n=31) included multiparametric (anatomical, diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast enhancing and non-enhancing FLAIR-hyperintense) information with (histogram) quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow / volume (CBF / CBV) and intratumoral susceptibility signals. Molecular characteristics determined with the Illumina Infinium HumanMethylation450 array included global DNA-methylation subgroups (e.g. mesenchymal (MES), RTK I “PGFRA”, RTK II “classic”), MGMT-promoter methylation status and hallmark copy-number-variations (EGFR-, PDGFRA-, MDM4- and CDK4-amplification; PTEN-, CDKN2A-, NF1- and RB1-loss). Univariate analyses (voxel-lesion-symptom-mapping for tumor location, Wilcoxon-test for all other MRI-features) as well as machine-learning models were applied to study the strength of association and discriminative value of MRI-features for predicting underlying molecular characteristics. Results: There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted p>0.05 each). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, both demonstrating increased nrCBV and nrCBF values (performance of these parameters, as assessed by the area under the ROC curve ranged from 63 to 69%, FDR-adjusted p<0.05, respectively). Subjecting all MRI-features to machine-learning-based classification allowed to predict EGFR amplification status and the RTK II “classic” GB subgroup with a moderate, yet significantly greater accuracy (63% for EGFR [p<0.01] and 61% for RTK II [p=0.01]) than the prediction by chance, whereas prediction accuracy for all other molecular parameters was non-significant (p>0.05, all models). Conclusions: In summary, we found univariate associations between established MRI-features and molecular characteristics, however not of sufficient strength to allow the generation of machine-learning classification models for reliable and clinically meaningful prediction of the assessed molecular characteristics in patients with newly-diagnosed glioblastoma.
2016-11-23 | GSE85539 | GEO
Project description:Background: To analyze the potential of radiomics for disease stratification beyond key molecular, clinical and standard imaging features in patients with glioblastoma. Methods: Quantitative imaging features (n=1043) were extracted from the multiparametric MRI of 181 patients with newly-diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI-test-retest cohort) and selected for analysis. A penalized Cox-model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS, OS). The incremental value of a radiomic signature beyond molecular (MGMT-promoter methylation, DNA-methylation subgroups), clinical (patients age, KPS, extent-of-resection, adjuvant treatment) and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox-models (performance quantified with prediction error curves). Results: The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation set) beyond the assessed molecular, clinical and standard imaging parameters (p≤0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (as compared to 29% and 27% with molecular + clinical features alone). The radiomic signature was - along with MGMT-status - the only parameter with independent significance on multivariate analysis (p≤0.01). Conclusions: Our study stresses the role of integrating radiomics into a multi-layer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.
2018-01-05 | GSE103659 | GEO
Project description:Proteomics analysis of matched tumor and normal adjacent tumor regions of 40 patients with multiparametric magnetic resonance imaging (mpMRI) visible or invisible tumors. All patients have clinically significant intermediate-risk (pathological ISUP Grade Group 2), localized prostate cancer.
2021-08-18 | MSV000088000 | MassIVE
Project description:Genomic Evaluation of Multiparametric Magnetic Resonance Imaging -visible and -nonvisible Lesions in Clinically Localized Prostate Cancer
| PRJNA395907 | ENA
Project description:Interventions: Single arm, multicentre, prospective, observational biomarker study.
Patients will have standard chemotherapy and radiotherapy. Patients participating in this study will have a multiparametric MRI at the following four time points:
1. Prior to chemoradiotherapy
2. During the second week of treatment
3. During the fourth week of treatment
4. At 6-8 weeks post treatment
A Multiparametric MRI incorporates standard morphological as well as diffusion weighted and dynamic contrast enhanced sequences. Each MRI takes approximately 45 minutes. All sequences will be performed on all patients at each of the 4 time points.
Observation post treatment is for 6 months.
Primary outcome(s): Correlation of change in Standard Morphological MRI with tumour response as determined by DRE +/- imaging [6 months];Correlation of change in Diffusion Weighted MRI with tumour response as determined by DRE +/- imaging[6 months];Correlation of change in Dynamic Contrast Enhanced MRI with tumour response as determined by DRE +/- imaging
[6 months]
| 2461578 | ecrin-mdr-crc
Project description:Radiogenomics of glioblastoma: Machine-learning based classification of molecular characteristics using multiparametric and multiregional MRI features
| PRJNA338764 | ENA
Project description:The DREAM study will assess the diagnostic accuracy of diffusion-weighted MRI in combination with other imaging modalities (multiparametric MRI and CT Scan) in determining the true status of disappearing liver metastasis (DLM) detected after conversion systemic therapy for unresectable or borderline resectable colorectal liver metastasis (CRLM).
| 2215891 | ecrin-mdr-crc