Dynamic NMR effects in breast cancer dynamic-contrast-enhanced MRI.
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ABSTRACT:
SUBMITTER: Li X
PROVIDER: S-EPMC2582941 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Angiogenesis is a critical component of breast cancer development, and identification of imaging-based angiogenesis assays has prognostic and treatment implications. We evaluated the association of semi-quantitative kinetic and radiomic breast cancer features on dynamic contrast-enhanced (DCE)-MRI with microvessel density (MVD), a marker for angiogenesis. Invasive breast cancer kinetic features (initial peak percent enhancement [PE], signal enhancement ratio [SER], functional tumor volume [FTV], and washout fraction [WF]), radiomics features (108 total features reflecting tumor morphology, signal intensity, and texture), and MVD (by histologic CD31 immunostaining) were measured in 27 patients (1/2016-7/2017). Lesions with high MVD levels demonstrated higher peak SER than lesions with low MVD (mean: 1.94 vs. 1.61, area under the receiver operating characteristic curve [AUC] = 0.79, p = 0.009) and higher WF (mean: 50.6% vs. 22.5%, AUC = 0.87, p = 0.001). Several radiomics texture features were also promising for predicting increased MVD (maximum AUC = 0.84, p = 0.002). Our study suggests DCE-MRI can non-invasively assess breast cancer angiogenesis, which could stratify biology and optimize treatments.
| S-EPMC8052427 | biostudies-literature
Project description:We explored the prognostic impact of the dynamic contrast enhanced MR imaging (DCE-MRI) parameter ABrix in cervical cancer combined with global gene expression data to reveal the underlying molecular phenotype of the parameter and construct a gene signature that reflected ABrix. Based on 78 cervical cancer patients subjected to curative chemoradiotherapy, we identified a prognostic ABrix parameter by pharmacokinetic analysis of DCE-MR images based on the Brix model, where tumors with low ABrix appeared to be most aggressive. Gene set enrichment analysis of 46 tumors with pairwise DCE-MRI and gene expression data showed a significant correlation between ABrix and the hypoxia gene sets, whereas gene sets related to proliferation, radioresistance, and wound healing were not significant. Hypoxia gene sets specific for cervical cancer created in cell culture experiments, including targets of the hypoxia inducible factor (HIF1M-NM-1) and the unfolded protein response (UPR), were the most significant. In the remaining 32 tumors, low ABrix was associated with upregulation of HIF1M-NM-1 protein expression, as assessed by immunohistochemistry, consistent with increased hypoxia. Based on the hypoxia gene sets, a signature of 31 genes that were upregulated in tumors with low ABrix was constructed. This DCE-MRI hypoxia gene signature showed prognostic impact in an independent validation cohort of 109 patients. Gene expression correlating with the DCE-MRI parameter ABrix were identified in 46 patients (DCE-MRI cohort) and used to find a hypoxia gene signature. The prognsotic impact of the gene signature was validated in an independent cohort of 109 patients (validation chort). Cell culture experiments were performed to generate cervical cancer specific gene lists associated with hypoxia (GSM897799 - GSM897804).
2012-08-16 | E-GEOD-36562 | biostudies-arrayexpress
Project description:BACKGROUND:Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. METHODS:Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4-6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7-4.6?s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values <?0.05 were considered statistically significant. RESULTS:Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P?<?0.001 and P?=?0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1-2 (P?=?0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P?=?0.002), and triple negative or HER2 type vs. luminal type (P?<?0.001). CONCLUSIONS:Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.
| S-EPMC7254650 | biostudies-literature
Project description:BackgroundImaging techniques can provide information about the tumor non-invasively and have been shown to provide information about the underlying genetic makeup. Correlating image-based phenotypes (radiomics) with genomic analyses is an emerging area of research commonly referred to as "radiogenomics" or "imaging-genomics". The purpose of this study was to assess the potential for using an automated, quantitative radiomics platform on magnetic resonance (MR) breast imaging for inferring underlying activity of clinically relevant gene pathways derived from RNA sequencing of invasive breast cancers prior to therapy.MethodsWe performed quantitative radiomic analysis on 47 invasive breast cancers based on dynamic contrast enhanced 3 Tesla MR images acquired before surgery and obtained gene expression data by performing total RNA sequencing on corresponding fresh frozen tissue samples. We used gene set enrichment analysis to identify significant associations between the 186 gene pathways and the 38 image-based features that have previously been validated.ResultsAll radiomic size features were positively associated with multiple replication and proliferation pathways and were negatively associated with the apoptosis pathway. Gene pathways related to immune system regulation and extracellular signaling had the highest number of significant radiomic feature associations, with an average of 18.9 and 16 features per pathway, respectively. Tumors with upregulation of immune signaling pathways such as T-cell receptor signaling and chemokine signaling as well as extracellular signaling pathways such as cell adhesion molecule and cytokine-cytokine interactions were smaller, more spherical, and had a more heterogeneous texture upon contrast enhancement. Tumors with higher expression levels of JAK/STAT and VEGF pathways had more intratumor heterogeneity in image enhancement texture. Other pathways with robust associations to image-based features include metabolic and catabolic pathways.ConclusionsWe provide further evidence that MR imaging of breast tumors can infer underlying gene expression by using RNA sequencing. Size and shape features were appropriately correlated with proliferative and apoptotic pathways. Given the high number of radiomic feature associations with immune pathways, our results raise the possibility of using MR imaging to distinguish tumors that are more immunologically active, although further studies are necessary to confirm this observation.
| S-EPMC6628478 | biostudies-literature
Project description:We explored the prognostic impact of the dynamic contrast enhanced MR imaging (DCE-MRI) parameter ABrix in cervical cancer combined with global gene expression data to reveal the underlying molecular phenotype of the parameter and construct a gene signature that reflected ABrix. Based on 78 cervical cancer patients subjected to curative chemoradiotherapy, we identified a prognostic ABrix parameter by pharmacokinetic analysis of DCE-MR images based on the Brix model, where tumors with low ABrix appeared to be most aggressive. Gene set enrichment analysis of 46 tumors with pairwise DCE-MRI and gene expression data showed a significant correlation between ABrix and the hypoxia gene sets, whereas gene sets related to proliferation, radioresistance, and wound healing were not significant. Hypoxia gene sets specific for cervical cancer created in cell culture experiments, including targets of the hypoxia inducible factor (HIF1α) and the unfolded protein response (UPR), were the most significant. In the remaining 32 tumors, low ABrix was associated with upregulation of HIF1α protein expression, as assessed by immunohistochemistry, consistent with increased hypoxia. Based on the hypoxia gene sets, a signature of 31 genes that were upregulated in tumors with low ABrix was constructed. This DCE-MRI hypoxia gene signature showed prognostic impact in an independent validation cohort of 109 patients.
2012-08-16 | GSE36562 | GEO
Project description:The volume transfer constant Ktrans , which describes the leakage of contrast agent (CA) from vasculature into tissue, is the most commonly reported quantitative parameter for dynamic contrast-enhanced (DCE-) MRI. However, the variation in reported Ktrans values between studies from different institutes is large. One of the primary sources of uncertainty is quantification of the arterial input function (AIF). The aim of this study is to determine the influence of the CA injection duration on the AIF and tracer kinetic analysis (TKA) parameters (i.e. Ktrans , kep and ve ). Thirty-one patients with prostate cancer received two DCE-MRI examinations with an injection duration of 5 s in the first examination and a prolonged injection duration in the second examination, varying between 7.5 s and 30 s. The DCE examination was carried out on a 3.0 T MRI scanner using a transversal T1 -weighted 3D spoiled gradient echo sequence (300 s duration, dynamic scan time of 2.5 s). Data of 29 of the 31 were further analysed. AIFs were determined from the phase signal in the left and right femoral arteries. Ktrans , kep and ve were estimated with the standard Tofts model for regions of healthy peripheral zone and tumour tissue. We observed a significantly smaller peak height and increased width in the AIF for injection durations of 15 s and longer. However, we did not find significant differences in Ktrans , kep or ve for the studied injection durations. The study demonstrates that the TKA parameters Ktrans , kep and ve , measured in the prostate, do not show a significant change as a function of injection duration.
| S-EPMC6175355 | biostudies-literature
Project description:Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as wavelet-based features, both extracted from pixel-based time-signal intensity curves to segment prostate lesions on prostate DCE-MRI.Quantitative dynamic contrast-enhanced MRI data were acquired on 22 patients. Optimal features selected by forward selection are used for the segmentation of prostate lesions by applying fuzzy c-means (FCM) clustering. The images were reviewed by an expert radiologist and manual segmentation performed as the ground truth.Empirical results indicate that fuzzy c-mean classifier can achieve better results in terms of sensitivity, speci?city when semi-quantitative features were considered versus wavelet kinetic features for lesion segmentation (Sensitivity of 87.58% and 75.62%, respectively) and (Specificity of 89.85% and 68.89 %, respectively).The proposed segmentation algorithm in this work can potentially be implemented for automatic prostate lesion detection in a computer aided diagnosis scheme and combined with morphologic features to increase diagnostic credibility.
| S-EPMC5928300 | biostudies-literature
Project description:BackgroundThe MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon recommends that a breast MRI protocol contain T2 -weighted and dynamic contrast-enhanced (DCE) MRI sequences. The addition of diffusion-weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE-MRI, DWI, and T2 -weighted imaging are most strongly associated with a breast cancer diagnosis.Purpose/hypothesisTo develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BI-RADS recommended descriptors for breast MRI with DCE, T2 -weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping.Study typeRetrospective.SubjectsIn all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014.Field strength/sequenceIR inversion recovert DCE-MRI dynamic contrast-enhanced magnetic resonance imaging VIBE Volume-Interpolated-Breathhold-Examination FLASH turbo fast-low-angle-shot TWIST Time-resolved angiography with stochastic Trajectories.AssessmentTwo radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T2 -weighted imaging according to BI-RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE-MRI BI-RADS descriptors, T2 -weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10-3 mm2 /sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference.Statistical testsχ2 test, Fisher's exact test, Kruskal-Wallis test, Pearson correlation coefficient, multivariate logistic regression analysis, Hosmer-Lemeshow test of goodness-of-fit, receiver operating characteristics analysis.ResultsIn Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T2 -weighted imaging variables were not included in the final models. DATA CONCLUSION: mpMRI with DCE-MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE-MRI and DWI identifies breast cancer with a high diagnostic accuracy. T2 -weighted imaging does not significantly contribute to breast cancer diagnosis.Level of evidence3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:864-874.
| S-EPMC6375760 | biostudies-literature
Project description:BackgroundTo construct and validate a radiomic-based model for estimating axillary lymph node (ALN) metastasis in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).MethodsIn this retrospective study, a radiomic-based model was established in a training cohort of 236 patients with breast cancer. Radiomic features were extracted from breast DCE-MRI scans. A method named the least absolute shrinkage and selection operator (LASSO) was applied to select radiomic features based on highly reproducible features. A radiomic signature was built by a support vector machine (SVM). Multivariate logistic regression analysis was adopted to establish a clinical characteristic-based model. The performance of models was analysed through discrimination ability and clinical benefits.ResultsThe radiomic signature comprised 6 features related to ALN metastasis and showed significant differences between the patients with ALN metastasis and without ALN metastasis (P < 0.001). The area under the curve (AUC) of the radiomic model was 0.990 and 0.858, respectively, in the training and validation sets. The clinical feature-based model, including MRI-reported status and palpability, performed slightly worse, with an AUC of 0.784 in the training cohort and 0.789 in the validation cohort. The radiomic signature was confirmed to provide more clinical benefits by decision curve analysis.ConclusionsThe radiomic-based model developed in this study can successfully diagnose the status of lymph nodes in patients with breast cancer, which may reduce unnecessary invasive clinical operations.
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Project description:Diabetes is associated with hepatic metabolic dysfunction predisposing patients to drug-induced liver injury. Mouse models of type 2 diabetes (T2D) have dramatically reduced expression of organic anion transporting polypeptide (OATP)1A1, a transporter expressed in hepatocytes and in the kidneys. The effects of diabetes on OATP1B2 expression are less studied and less consistent. OATP1A1 and OATP1B2 both transport endogenous substrates such as bile acids and hormone conjugates as well as numerous drugs including gadoxetate disodium (Gd-EOB-DTPA). As master pharmacokinetic regulators, the altered expression of OATPs in diabetes could have a profound and clinically significant influence on drug therapies. Here, we report a method to noninvasively measure OATP activity in T2D mice by quantifying the transport of hepatobiliary-specific gadolinium-based contrast agents (GBCAs) within the liver and kidneys using dynamic contrast-enhanced MRI (DCE-MRI). By comparing GBCA uptake in control and OATP knockout mice, we confirmed liver clearance of the hepatobiliary-specific GBCAs, Gd-EOB-DTPA, and gadobenate dimeglumine, primarily though OATP transporters. Then, we measured a reduction in the hepatic uptake of these hepatobiliary GBCAs in T2D ob/ob mice, which mirrored significant reductions in the mRNA and protein expression of OATP1A1 and OATP1B2. As these GBCAs are U.S. Food and Drug Administration-approved agents and DCE-MRI is a standard clinical protocol, studies to determine OATP1B1/1B3 deficiencies in human individuals with diabetes can be easily envisioned.
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