Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA338764 | ENA |
REPOSITORIES: ENA
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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: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:To extract urinary proteome spectral features based on advanced mass spectrometer and machine learning algorithms, it could get more accurate reporting results for disease classification. We tried to establish a novel diagnosis model of kidney diseases by combining machine learning XGBoost algorithm with complete urinary proteomic information.
2022-09-13 | PXD018996 | Pride
Project description:Large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system.
2022-11-12 | GSE211692 | 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: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:Association of Multiparametric MRI Quantitative Imaging Features with Prostate Cancer Gene Expression in MRI-targeted Prostate Biopsies
| PRJNA324676 | ENA
Project description:Using unsupervised machine learning applied to computed tomography-based imaging characteristics we have found three distinct phenotypes of lung disease in two large cohorts of ever-smokers and have identified their molecular correlates.
2020-12-07 | GSE132829 | GEO
Project description:Background: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Deep learning captures complex non-linear associations within multimodal data but, to date, has not been used for multi-omic-based endotyping of KOA patients. We developed a novel multimodal deep learning framework for clustering of multi-omic data from three subject-matched biofluids to identify distinct KOA endotypes and classify one-year post-total knee arthroplasty (TKA) pain/function responses. Materials and Methods: In 414 KOA patients, subject-matched plasma, synovial fluid and urine were analyzed by microRNA sequencing or metabolomics. Integrating 4 high-dimensional datasets comprising metabolites from plasma (n=151 features), along with microRNAs from plasma (n=421), synovial fluid (n=930), or urine (n=1225), a multimodal deep learning variational autoencoder architecture with K-means clustering was employed. Features influencing cluster assignment were identified and pathway analyses conducted. An integrative machine learning framework combining 4 molecular domains and a clinical domain was then used to classify WOMAC pain/function responses post-TKA within each cluster. Findings: Multimodal deep learning-based clustering of subjects across 4 domains yielded 3 distinct patient clusters. Feature signatures comprising microRNAs and metabolites across biofluids included 30, 16, and 24 features associated with Clusters 1-3, respectively. Pathway analyses revealed distinct pathways associated with each cluster. Integration of 4 multi-omic domains along with clinical data improved response classification performance, with Cluster 3 achieving AUC=0·879 for subject pain response classification and Cluster 2 reaching AUC=0·808 for subject function response, surpassing individual domain classifications by 12% and 15% respectively. Interpretation: We have developed a deep learning-based multimodal clustering model capable of integrating complex multi-fluid, multi-omic data to assist in KOA patient endotyping and test outcome response to TKA surgery.
2024-12-29 | GSE222979 | GEO
Project description:The following consists of an RNA-Seq cohort comprised of samples from patients diagnosed with various tumor types across the pan-cancer spectrum. Collected at diagnosis, these samples serve as a resource for tumor classification using machine learning classifiers. Process count tables are provided. This cohort can assist researchers in exploring molecular signatures and facilitate accurate classification across diverse malignancies.
2024-06-30 | E-MTAB-14038 | biostudies-arrayexpress