***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: The purpose of this study was to investigate the molecular background of cerebral blood volume ( CBV ) and vessel size ( VS ) of capillaries in glioblastoma multiforme ( GBM ) . Both parameters are derived from extended perfusion MR imaging . A prospect i ve case study including 21 patients ( median age 66 years , 10 females ) was performed . Before operation , CBV and VS of contrast enhancing tumor were assessed . Tissue was sample d from the assessed areas under neuronavigation control . After RNA extraction , transcriptional data was analyzed by Weighted Gene Co-Expression Network Analysis ( WGCNA ) and split into modules based on its network affiliations . Gene Set Enrichment Analysis ( GSEA ) identified biological functions or pathways of the genetic modules . These were applied on 484 GBM sample s of the TCGA data base . Ten modules were highly correlated to CBV and VS . One module was exclusively associated to VS and highly correlated to hypoxia , another one exclusively to CBV showing strong enrichments in the Epithelial Growth Factor ( EGF ) pathway and Epithelial-to-Mesenchymal-Transition ( EMT ) . Moreover , patients with increased CBV and VS predominantly showed a mesenchymal gene-expression , a finding that could be corroborated by TCGA data . In conclusion , CBV and VS mirror different genetic pathways and reflect certain molecular subclasses of GBM Molecular subsets of oligodendroglioma behave in biologically distinct ways . Their locations in the brain , rates of growth , and responses to therapy differ with their genotypes . Retrospectively , we inquired whether allelic loss of chromosomal arms 1p and 19q , an early molecular event and favorable prognostic marker in oligodendrogliomas , were reflected in their appearance on magnetic resonance imaging . Loss of 1p and 19q was associated with an indistinct border on T1 images and mixed intensity signal on T1 and T2 . Loss of 1p and 19q was also associated with paramagnetic susceptibility effect and with calcification , a common histopathological finding in oligodendrogliomas . These data encourage prospect i ve evaluation of molecular alterations and magnetic resonance imaging characteristics of glial neoplasms To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on  dynamic contrast en-hanced (DCE)-MRI  that are correlated with the low and high OncotypeDX risk categories . We collected 96 ER-positivebreast lesions with low ( < 18 , N = 55 ) and high ( > 30 , N = 41 ) OncotypeDX recurrence scores . Each lesion was quantitatively charac-terize via 6 shape features , 3 pharmacokinetics , 4 enhancement kinetics , 4 intensity kinetics , 148 textural kinetics , 5 dynamic histogram of oriented gradient ( DHoG ) , and 6 dynamic local binary pattern ( DLBP ) features . The extracted features were evaluated by a linear discriminant analysis ( LDA ) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories . Classification performance was evaluated by area under the receiver operator characteristic curve ( Az ) . The DHoG and DLBP achieved Az values of 0.84 and 0.80 , respectively . The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87 . The correlation analysis showed that DHoG ( ρ = 0.85 , P < 0.001 ) and DLBP ( ρ = 0.83 , P < 0.01 ) were significantly associated with the low and high risk classifications from the OncotypeDX assay . Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers BACKGROUND MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics . However , previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures . The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma ( GB ) . METHODS One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs . These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype . Cross-validation ensured generalizability of these predictors to new patients . Subsequently , the predictors were evaluated in a prospect i ve cohort of 29 new patients . RESULTS Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors . The overall , 3-way ( long/medium/short survival ) accuracy in the prospect i ve cohort approached 80 % . Classification of patients into the 4 molecular subtypes of GB achieved 76 % accuracy . CONCLUSIONS By employing machine learning techniques , we were able to demonstrate that imaging patterns are highly predictive of patient survival . Additionally , we found that GB subtypes have distinctive imaging phenotypes . These results reveal that when imaging markers related to infiltration , cell density , microvascularity , and blood-brain barrier compromise are integrated via advanced pattern analysis methods , they form very accurate predictive biomarkers . These predictive markers used solely preoperative images , hence they can significantly augment diagnosis and treatment of GB patients AIM To investigate whether quantitative dynamic susceptibility-weighted contrast-enhanced ( DSC ) perfusion magnetic resonance imaging ( MRI ) metrics are influenced by cellular and genomic expression patterns of glioblastoma angiogenesis . MATERIAL S AND METHODS Twenty-five stereotactic neurosurgical tissue sample s were prospect ively obtained from enhancing and non-enhancing tumour regions from 10 patients with treatment-naïve glioblastoma . Using monoclonal antibodies , histopathological features of angiogenesis were examined : total microvascular density , vascular morphology , and hypoxia . Angiogenic expression patterns of tissue sample s were investigated using RNA microarrays . DSC perfusion MRI metrics were measured from the tissue sampling sites . MRI and histopathological variables were compared using Pearson 's correlations . Microarray analysis was performed using false discovery rate ( FDR ) statistics . RESULTS Thirteen enhancing and 12 non-enhancing MR image-guided tissue specimens were prospect ively obtained . Enhancing tumour regions demonstrated a significant difference in DSC perfusion and histopathological metrics of angiogenesis when compared to non-enhancing regions . Four angiogenic pathways ( vascular endothelial growth factor [ VEGF ] , hypoxia inducible factor [ HIF ] , platelet-derived growth factor [ PDGF ] , fibroblast growth factor [ FGF ] ; 25 individual genes ) were significantly up-regulated within enhancing regions when compared to non-enhancing regions ( adjusted p<0.05 , FDR < 0.05 ) . A statistically significant correlation was observed between VEGF-A expression , microvascular density , microvascular morphology , and DSC perfusion MRI metrics ( p<0.05 ) . CONCLUSION Pro-angiogenic genomic and cellular expression patterns of treatment-naïve primary glioblastoma significantly influences morphological and physiological DSC perfusion metrics suggesting that expression levels of therapeutically relevant genetic signatures can be quantified using MRI Objectives To characterize a radiogenomic risk score ( RRS ) , a previously defined biomarker , and to evaluate its potential for stratifying radiological progression-free survival ( rPFS ) in patients with metastatic renal cell carcinoma ( mRCC ) undergoing pre-surgical treatment with bevacizumab .  Methodology In this IRB-approved study , prospect i ve imaging analysis of the RRS was performed on phase II clinical trial data of mRCC patients ( n = 41 ) evaluating whether patient stratification according to the RRS result ed in groups more or less likely to have a rPFS to pre-surgical bevacizumab prior to cytoreductive nephrectomy .  Survival times of RRS subgroups were analyzed using Kaplan-Meier survival analysis . Results The RRS is enriched in diverse molecular processes including drug response , stress response , protein kinase regulation , and signal transduction pathways ( P < 0.05 ) . The RRS successfully stratified rPFS to bevacizumab based on pre-treatment computed tomography imaging with a median progression-free survival of 6 versus > 25 months ( P = 0.005 ) and overall survival of 25 versus > 37 months in the high and low RRS groups ( P = 0.03 ) , respectively . Conventional prognostic predictors including the Motzer and Heng criteria were not predictive in this cohort ( P > 0.05 ) . Conclusions The RRS stratifies rPFS to bevacizumab in patients from a phase II clinical trial with mRCC undergoing cytoreductive nephrectomy and pre-surgical bevacizumab . Key Points• The RRS SOMA stratifies patient outcomes in a phase II clinical trial.• RRS stratifies subjects into prognostic groups in a discrete or continuous fashion.• RRS is biologically enriched in diverse processes including drug response programs These authors combined 3 advanced MRI techniques in an attempt to determine which oligodendrogliomas had 1p/19q deletions and thus a more favorable prognosis . They retrospectively analyzed data obtained in 50 tumors and found that though these 3 techniques were helpful in the grading of oligodendrogliomas , the multimodal approach used showed no significant differences between tumors with and without 1p/19q deletions . BACKGROUND AND PURPOSE : Pretherapeutic determination of tumor grade and genotype in grade II and III oligodendroglial tumors is clinical ly important but is still challenging . Tumor grade and 1p/19q status are currently the 2 most important factors in therapeutic decision making for patients with these tumors . Histopathology and cMRI studies are still limited in some cases . In the present study , we were interested in determining whether the combination of PWI , DWI , and MR spectroscopy could help distinguish oligodendroglial tumors according to their histopathologic grade and genotype . MATERIAL S AND METHODS : We retrospectively review ed 50 adult patients with grade II and III oligodendrogliomas and oligoastrocytomas who had DWI , PWI , and MR spectroscopy at short and long TE data and known 1p/19q status . Univariate analyses and multivariate r and om forest models were performed to determine which criteria could differentiate between grade s and genotypes . RESULTS : ADC , rCBV , rCBF , and rK2 were significantly different between grade II and III oligodendroglial tumors . DWI , PWI , and MR spectroscopy showed no significant difference between tumors with and without 1p/19q loss . Separation between tumor grade s and genotypes with cMRI alone showed 31 % and 48 % misclassification rates , respectively . Multimodal MR imaging helps to determine tumor grade and 1p/19q genotype more accurately ( misclassification rates of 17 % and 40 % , respectively ) . CONCLUSIONS : Although multimodal investigation of oligodendroglial tumors has a lower contribution to 1p/19q genotyping compared with cMRI alone , it greatly improves the accuracy of grading of these neoplasms . Use of multimodal MR imaging could thus provide valuable information that may assist clinicians in patient preoperative management and treatment decision making BACKGROUND Treatment with cetuximab , a monoclonal antibody directed against the epidermal growth factor receptor , improves overall and progression-free survival and preserves the quality of life in patients with colorectal cancer that has not responded to chemotherapy . The mutation status of the K-ras gene in the tumor may affect the response to cetuximab and have treatment-independent prognostic value . METHODS We analyzed tumor sample s , obtained from 394 of 572 patients ( 68.9 % ) with colorectal cancer who were r and omly assigned to receive cetuximab plus best supportive care or best supportive care alone  , to look for activating mutations in exon 2 of the K-ras gene . We assessed whether the mutation status of the K-ras gene was associated with survival in the cetuximab and supportive-care groups . RESULTS Of the tumors evaluated for K-ras mutations , 42.3 % had at least one mutation in exon 2 of the gene . The effectiveness of cetuximab was significantly associated with K-ras mutation status ( P=0.01 and P<0.001 for the interaction of K-ras mutation status with overall survival and progression-free survival , respectively ) . In patients with wild-type K-ras tumors , treatment with cetuximab as compared with supportive care alone significantly improved overall survival ( median , 9.5 vs. 4.8 months ; hazard ratio for death , 0.55 ; 95 % confidence interval [ CI ] , 0.41 to 0.74 ; P<0.001 ) and progression-free survival ( median , 3.7 months vs. 1.9 months ; hazard ratio for progression or death , 0.40 ; 95 % CI , 0.30 to 0.54 ; P<0.001 ) . Among patients with mutated K-ras tumors , there was no significant difference between those who were treated with cetuximab and those who received supportive care alone with respect to overall survival ( hazard ratio , 0.98 ; P=0.89 ) or progression-free survival ( hazard ratio , 0.99 
    Output:
    
Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma , relevant for prognosis and disease profiling .
In non-small cell lung cancer , FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements .
Oncology-wide gene pathway analysis revealed an association between contrast enhancement ( imaging ) and the targetable VEGF-signalling pathway .
Although the need of independent validation remains a concern , radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection .